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Orakwue, Stella I., and Nkolika O. Nwazor. "Plant Disease Detection and Monitoring Using Artificial Neural Network." International Journal of Scientific Research and Management 10, no. 01 (2022): 715–22. http://dx.doi.org/10.18535/ijsrm/v10i1.ec01.
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Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.
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KAIMI, I., and P. J. DIGGLE. "A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections." Epidemiology and Infection 139, no. 12 (2011): 1854–62. http://dx.doi.org/10.1017/s0950268811000057.
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SUMMARYThe AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic model for the time series of daily incident cases to enable probabilistic prediction of temporal variation in risk, and demonstrated the resulting gains in predictive accuracy by comparison with a conventional analysis based on an over-dispersed Poisson log-linear regression model. We used Bayesian methods of inference in order to incorporate parameter uncertainty in our predictive inference of risk. Incorporation of our model in the overall spatio-temporal model, will contribute to the accurate and timely prediction of unusually high food-poisoning incidence, and thus to the identification and prevention of future outbreaks.
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Wang, Y. P., N. H. Idris, F. M. Muharam, N. Asib, and Alvin M. S. Lau. "Comparison of different variable selection methods for predicting the occurrence of Metisa Plana in oil palm plantation using machine learning." IOP Conference Series: Earth and Environmental Science 1274, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1755-1315/1274/1/012008.
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Abstract Monitoring and predicting the spatio-temporal distribution of crop pests and assessing related risks are crucial for effective pest management strategies. Machine learning techniques have shown potential in analysing agricultural data and providing accurate predictions. Variable selection plays a critical role in crop pest analysis by identifying the most informative and influential features that contribute to pest distribution and risk prediction. The current practice of choosing variable selection methods is mostly based on previous experience and may involve a certain degree of subjectivity. This paper aims to provide empirical comparisons of different variable selection methods for machine learning applications in crop pest spatio-temporal distribution and risk prediction. This study conducted various variable selection methods, including filter methods (information gain, chi-square test, mutual information), wrapper methods (RFE), and embedded methods (Random Forest), using worms pest (Metisa plana) in oil palm trees as the experimental subject. The initial set of variables included bioclimatic, vegetation indices, and terrain variables. The experimental results indicated that there was some overlap in the selected variables across different methods, bioclimatic variables (rainfall (RF), relative humidity (RH)) were selected as important variables by different methods; non-important variables like NDVI and elevation when added to the ANN modelling can clearly contribute to the improvement in prediction accuracy. These empirical findings can provide guidance for relevant data monitoring in the prediction of crop pest and disease outbreaks. Additionally, the results can serve as a reference for variable selection in spatiotemporal prediction of pests and diseases in other agricultural and forestry crops.
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Sharma, V., S. K. Ghosh, and S. Khare. "A PROPOSED FRAMEWORK FOR SURVEILLANCE OF DENGUE DISEASE AND PREDICTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 317–23. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-317-2023.
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Abstract. Recurring outbreaks of dengue during past decades have affected public health and burdened resource constraint health systems across the world. Transmission of such diseases is a conjugation of various complex factors including vector dynamics, transmission mechanism, environmental conditions, cultural behaviours, and public health policies. Modelling and predicting early outbreaks is the key to an effective response to control the spread of disease. In this study, a comprehensive framework has been proposed to model dengue disease by integrating significant factors using different inputs, such as remote sensing, epidemiological data, and health infrastructure inputs. This framework for Dengue Disease Monitoring (DDM) model provides a conceptual architecture for integrating different data sources, visualization and assessment of disease status, and prediction analysis. The developed model will help forewarn the public health administration about the outbreak for planning interventions to limit the spread of dengue. Further, this forecasting model may be applied to manage the existing public health resources for medical and health infrastructure, also to determine the efficacy of vector surveillance and intervention programmes.
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Velasquez-Camacho, Luisa, Marta Otero, Boris Basile, Josep Pijuan, and Giandomenico Corrado. "Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards." Microorganisms 11, no. 1 (2022): 73. http://dx.doi.org/10.3390/microorganisms11010073.
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Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized farming system characterized by high crop protection cost and increasing adoption of precision agriculture techniques. Different methodological approaches, from traditional mechanistic or statistic models to machine and deep learning, are outlined with their main features, potential, and constraints. Our analysis indicated that strategies are being continuously developed to achieve the required goals of ease of monitoring and timely prediction of diseases. We also discuss that scientific and technological advances (e.g., in weather data, omics, digital solutions, sensing devices, data science) still need to be fully harnessed, not only for modelling plant–pathogen interaction but also to develop novel, integrated, and robust predictive systems and related applied technologies. We conclude by identifying key challenges and perspectives for predictive modelling of phytopathogenic disease in vineyards.
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Alodat, Iyas. "Analysing and predicting COVID-19 AI tracking using artificial intelligence." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 03 (2021): 2141005. http://dx.doi.org/10.1142/s1793962321410051.
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In this paper, we will discuss prediction methods to restrict the spread of the disease by tracking contact individuals via mobile application to individuals infected with the COVID-19 virus. We will track individuals using bluetooth technology and then we will save information in the central database when they are in touch. Monitoring cases and avoiding the infected person help with social distance. We also propose that sensors used by people to obtain blood oxygen saturation level and their body temperature will be used besides bluetooth monitoring. The estimation of the frequency of the disease is based on the data entered by the patient and also on the data gathered from the users who entered the program on the state of the disease. In this study, we will also propose the best way to restrict the spread of COVID-19 by using methods of artificial intelligence to predict the disease in Jordan using Tensorflow.
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Helget, Lindsay N., David J. Dillon, Bethany Wolf, et al. "Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis." Lupus Science & Medicine 8, no. 1 (2021): e000489. http://dx.doi.org/10.1136/lupus-2021-000489.
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ObjectiveLupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinically meaningful machine learning early decision support tool that predicts LN outcomes at approximately 1 year.MethodsTo address this hypothesis, patients with LN from a prospective longitudinal registry at the Medical University of South Carolina enrolled between 2003 and 2017 were identified if they had renal biopsies with International Society of Nephrology/Renal Pathology Society pathological classification. Clinical laboratory values at the time of diagnosis and outcome variables at approximately 1 year were recorded. Machine learning models were developed and cross-validated to predict suboptimal response.ResultsFive machine learning models predicted suboptimal response status in 10 times cross-validation with receiver operating characteristics area under the curve values >0.78. The most predictive variables were interstitial inflammation, interstitial fibrosis, activity score and chronicity score from renal pathology and urine protein-to-creatinine ratio, white blood cell count and haemoglobin from the clinical laboratories. A web-based tool was created for clinicians to enter these baseline clinical laboratory and histopathology variables to produce a probability score of suboptimal response.ConclusionGiven the heterogeneity of disease presentation in LN, it is important that risk prediction models incorporate several data elements. This report provides for the first time a clinical proof-of-concept tool that uses the five most predictive models and simplifies understanding of them through a web-based application.
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Chua, Felix, Rama Vancheeswaran, Adrian Draper, et al. "Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score." Thorax 76, no. 7 (2021): 696–703. http://dx.doi.org/10.1136/thoraxjnl-2020-216425.
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IntroductionRisk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.MethodsMultivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).Results983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).ConclusionThe SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
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Masih, Adven, and Alexander N. Medvedev. "Evaluating the performance of support vector machines based on different kernel methods for forecasting air pollutants." Вестник ВГУ. Серия: Системный анализ и информационные технологии, no. 3 (September 30, 2020): 5–14. http://dx.doi.org/10.17308/sait.2020.3/3035.
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The alarming level of air pollution in urban centres is an urgent threat to human health. Its consequences can be measured in terms of health issues experienced by children, an increasing numbers of heart and lung diseases, and, most importantly, the number of pollution related deaths. That is why a lot of attention has recently been paid to air pollution monitoring and prediction modelling. In order to develop prediction models, the study uses Support Vector Machines (SVM) with linear, polynomial, radial base function, normalised polynomial, and Pearson VII function kernels to predict the hourly concentration of pollutants in the air. The paper analyses the monitoring dataset of air pollutants and meteorological parameters as input variable to predict the concentrations of various air pollutants. The prediction performance of the models was assessed by using evaluation metrics, namely the correlation coefficient, root mean squared error, relative absolute error, and relative root squared error. To validate the model, the accuracy of the predictive algorithm was tested against two widely and commonly applied regression approaches called multilayer perceptron and linear regression. Furthermore, back check prediction test was performed to examine the consistency of the models. According to the results, the Pearson VII function and normalised polynomial kernel yield the most accurate results in terms of the correlation coefficient and error values to predict the concentrations of atmospheric pollutants as compared to other SVM kernels and traditional prediction models.
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Mrara, Busisiwe, Fathima Paruk, Constance Sewani-Rusike, and Olanrewaju Oladimeji. "Development and validation of a clinical prediction model of acute kidney injury in intensive care unit patients at a rural tertiary teaching hospital in South Africa: a study protocol." BMJ Open 12, no. 7 (2022): e060788. http://dx.doi.org/10.1136/bmjopen-2022-060788.
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IntroductionAcute kidney injury (AKI) is a decline in renal function lasting hours to days. The rising global incidence of AKI, and associated costs of renal replacement therapy, is a public health priority. With the only therapeutic option being supportive therapy, prevention and early diagnosis will facilitate timely interventions to prevent progression to chronic kidney disease. While many factors have been identified as predictive of AKI, none have shown adequate sensitivity or specificity on their own. Many tools have been developed in developed-country cohorts with higher rates of non-communicable disease, and few have been validated and practically implemented. The development and validation of a predictive tool incorporating clinical, biochemical and imaging parameters, as well as quantification of their impact on the development of AKI, should make timely and improved prediction of AKI possible. This study is positioned to develop and validate an AKI prediction tool in critically ill patients at a rural tertiary hospital in South Africa.Method and analysisCritically ill patients will be followed from admission until discharge or death. Risk factors for AKI will be identified and their impact quantified using statistical modelling. Internal validation of the developed model will be done on separate patients admitted at a different time. Furthermore, patients developing AKI will be monitored for 3 months to assess renal recovery and quality of life. The study will also explore the utility of endothelial monitoring using the biomarker Syndecan-1 and capillary leak measurements in predicting persistent AKI.Ethics and disseminationThe study has been approved by the Walter Sisulu University Faculty of Health Science Research Ethics and Biosafety Committee (WSU No. 005/2021), and the Eastern Cape Department of Health Research Ethics (approval number: EC 202103006). The findings will be shared with facility management, and presented at relevant conferences and seminars.
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Eswaran, Sarojini, Bharathiraj L.T, and Jayanthi S. "Modelling of ambient air quality, Coimbatore, India." E3S Web of Conferences 117 (2019): 00002. http://dx.doi.org/10.1051/e3sconf/201911700002.
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Air pollution is dispersion of the particulates, biological molecules, or other harmful materials into the Earth’s atmosphere, possibly causing diseases. Air pollutants can be either particles, liquids or gaseous. In the recent era, air pollution has become a major environmental issue because of the enhanced anthropogenic activities such as burning fossil fuels, natural gases, coal and oil, industrial process, advanced technologies and motor vehicles. The proposed project focused on air pollution study of North Coimbatore region (11° 0’ 16.4016’’ N and 76° 57’ 41.8752’’ E), Tamilnadu, India. The area comprises of industries, residential and commercial areas, where plenty of pollution occurs due to emissions from automobiles also. The main aim of the project is to develop models using GIS for the air pollutant concentration of Coimbatore region. In order to run the model, the concentration details of PM2.5 (Particulate mass) were collected. Prediction models have been evolved for the monitoring station to predict the concentration of pollutants (PM2.5) based on the different meteorological parameters and also vice versa. The project concludes that highly polluted places are Koundampalayam and Thudiyalur compared to all other monitoring stations.
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Lin, Lingmin, Kailai Liu, Huan Feng, et al. "Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying." Mathematical Biosciences and Engineering 19, no. 10 (2022): 10096–107. http://dx.doi.org/10.3934/mbe.2022472.
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<abstract> <p>Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one participant with type 2 diabetes in a community. The predictive accuracy was then assessed by RMSE (root mean square error) using blood glucose data. Changes in glycaemic parameters of the participant before and after model intervention were also compared to examine the efficacy of this intelligence-aided health care. Individual Recurrent Neural Network model was developed on glucose data, with an average daily RMSE of 1.59 mmol/L in the application segment. In terms of the glucose variation, the mean glucose decreased by 0.66 mmol/L, and HBGI dropped from 12.99 × 10<sup>2</sup> to 9.17 × 10<sup>2</sup>. However, the participant also had increased stress, especially in eating and social support. Our research presented a personalised care system for people with diabetes based on deep learning. The intelligence-aided health management system is promising to enhance the outcome of diabetic patients, but further research is also necessary to decrease stress in the intelligence-aided health management and investigate the stress impacts on diabetic patients.</p> </abstract>
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Liebenstund, Lisa, Mark Coburn, Christina Fitzner, et al. "Predicting experimental success: a retrospective case-control study using the rat intraluminal thread model of stroke." Disease Models & Mechanisms 13, no. 12 (2020): dmm044651. http://dx.doi.org/10.1242/dmm.044651.
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ABSTRACTThe poor translational success rate of preclinical stroke research may partly be due to inaccurate modelling of the disease. We provide data on transient middle cerebral artery occlusion (tMCAO) experiments, including detailed intraoperative monitoring to elaborate predictors indicating experimental success (ischemia without occurrence of confounding pathologies). The tMCAO monitoring data (bilateral cerebral blood flow, CBF; heart rate, HR; and mean arterial pressure, MAP) of 16 animals with an ‘ideal’ outcome (MCA-ischemia), and 48 animals with additional or other pathologies (subdural haematoma or subarachnoid haemorrhage), were checked for their prognostic performance (receiver operating characteristic curve and area under the curve, AUC). Animals showing a decrease in the contralateral CBF at the time of MCA occlusion suffered from unintended pathologies. Implementation of baseline MAP, in addition to baseline HR (AUC, 0.83, 95% c.i. 0.68 to 0.97), increased prognostic relevance (AUC, 0.89, 95% c.i. 0.79 to 0.98). Prediction performance improved when two additional predictors referring to differences in left and right CBF were considered (AUC, 1.00, 95% c.i. 1.0 to 1.0). Our data underline the importance of peri-interventional monitoring to verify a successful experimental performance in order to ensure a disease model as homogeneous as possible.
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Kulkarni, Mrunalini Harish, Chaitanya Kulkarni, K. Suresh Babu, Saima Ahmed Rahin, Shweta Singh, and D. Dinesh Kumar. "Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT." Scientific Programming 2022 (May 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/3603238.
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As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method’s validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment.
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Sethy, Prabira Kumar, Santi Kumari Behera, Nithiyakanthan Kannan, Sridevi Narayanan, and Chanki Pandey. "Smart paddy field monitoring system using deep learning and IoT." Concurrent Engineering 29, no. 1 (2021): 16–24. http://dx.doi.org/10.1177/1063293x21988944.
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Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.
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Jones, K. L., R. C. A. Thompson, and S. S. Godfrey. "Social networks: a tool for assessing the impact of perturbations on wildlife behaviour and implications for pathogen transmission." Behaviour 155, no. 7-9 (2018): 689–730. http://dx.doi.org/10.1163/1568539x-00003485.
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Abstract Wildlife are increasingly subject to perturbations, which can impact pathogen transmission and lead to disease emergence. While a myriad of factors influence disease dynamics in wildlife, behaviour is emerging as a major influence. In this review, we examine how perturbations alter the behaviour of individuals and how, in turn, disease transmission may be impacted, with a focus on the use of network models as a powerful tool. There are emerging hypotheses as to how networks respond to different types of perturbations. The broad effects of perturbations make predicting potential outcomes and identifying mitigation opportunities for disease emergence critical; yet, the current paucity of data makes identification of underlying trends difficult. Social network analysis facilitates a mechanistic approach to how perturbation-induced behavioural changes result in shifts in pathogen transmission. However, the field is still developing, and future work should strive to address current deficits. There is particular need for empirical data to support modelling predictions and increased inclusion of pathogen monitoring in network studies.
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Zhao, Hongwei, Naveed N. Merchant, Alyssa McNulty, et al. "COVID-19: Short term prediction model using daily incidence data." PLOS ONE 16, no. 4 (2021): e0250110. http://dx.doi.org/10.1371/journal.pone.0250110.
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Background Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. Results We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. Conclusion We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.
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Jombart, Thibaut, Stéphane Ghozzi, Dirk Schumacher, et al. "Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1829 (2021): 20200266. http://dx.doi.org/10.1098/rstb.2020.0266.
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As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker . This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
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Stefanescu, Simona, Relu Cocoș, Adina Turcu-Stiolica, et al. "Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results." Pathogens 10, no. 7 (2021): 789. http://dx.doi.org/10.3390/pathogens10070789.
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Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman’s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein (CRP) to albumin ratio (CAR) and neutrophil to albumin ratio (NAR), were significantly associated with culture negativity. The predictive ability of a composite model of seven biomarkers was superior to that of any single biomarker based on area under the receiver operating characteristic curve (AUC) analysis, indicating an excellent prediction efficacy (AUC:0.892; 95% CI:0.732-1.0). We also found that the highest significant trends and lower levels of CRP and IP-10 were observed in the two-month treated tuberculosis (TB) patients. We believe that our study may be valuable in providing preliminary results for an additional strategy in monitoring and management of the clinical outcome of pulmonary tuberculosis. Using a panel of predictors added a superior value in predicting culture status after anti-TB therapy.
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ANDERSON, D. P., D. S. L. RAMSEY, G. NUGENT, et al. "A novel approach to assess the probability of disease eradication from a wild-animal reservoir host." Epidemiology and Infection 141, no. 7 (2013): 1509–21. http://dx.doi.org/10.1017/s095026881200310x.
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SUMMARYSurveying and declaring disease freedom in wildlife is difficult because information on population size and spatial distribution is often inadequate. We describe and demonstrate a novel spatial model of wildlife disease-surveillance data for predicting the probability of freedom of bovine tuberculosis (caused by Mycobacterium bovis) in New Zealand, in which the introduced brushtail possum (Trichosurus vulpecula) is the primary wildlife reservoir. Using parameters governing home-range size, probability of capture, probability of infection and spatial relative risks of infection we employed survey data on reservoir hosts and spillover sentinels to make inference on the probability of eradication. Our analysis revealed high sensitivity of model predictions to parameter values, which demonstrated important differences in the information contained in survey data of host-reservoir and spillover-sentinel species. The modelling can increase cost efficiency by reducing the likelihood of prematurely declaring success due to insufficient control, and avoiding unnecessary costs due to excessive control and monitoring.
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Przybilla, Jens, Peter Ahnert, Holger Bogatsch, et al. "Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia." Journal of Clinical Medicine 9, no. 2 (2020): 393. http://dx.doi.org/10.3390/jcm9020393.
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Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient’s risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.
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Shi, Lei, Xiaoliang Feng, Longxing Qi, Yanlong Xu, and Sulan Zhai. "Modeling and Predicting the Influence of PM2.5 on Children’s Respiratory Diseases." International Journal of Bifurcation and Chaos 30, no. 15 (2020): 2050235. http://dx.doi.org/10.1142/s0218127420502351.
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In this paper, the influence of PM[Formula: see text] on children’s respiratory diseases is taken as the main research focus. Based on the real monitoring data of children’s respiratory diseases in Anhui province, the traditional model is modified substantially, leading to the establishment of two mathematical models. First of all, considering that the PM[Formula: see text] changes over time, a nonautonomous air pollution-related disease model is constructed to study its permanence and extinction. Furthermore, regarding lag days of PM[Formula: see text] exposure, an air pollution-related disease model with the lag effect is installed and its local and global stabilities and Hopf bifurcation are investigated. Meanwhile, the above two models are numerically simulated, respectively. Our study demonstrates that the threshold conditions of permanence and extinction are obtained by the nonautonomous air pollution-related disease model, and the optimal parameters are obtained through the annual revision of the data by integrating the mathematical model, such that the number of children with respiratory diseases in the future can be checked and predicted. Also our study finds that the lag days of PM[Formula: see text] exposure have little effect on children with respiratory diseases in the air pollution-related disease model with a lag effect, but the PM[Formula: see text] has a tremendous influence on the number of patients. Once the lag days are combined with the effect of the PM[Formula: see text], it can have a significant impact on the patients’ number, e.g. an emergence of periodic oscillations, with an approximate period of 11 days in Anhui Province, due to the Hopf bifurcation.
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Suzuki, Ayako, and Hiroshi Nishiura. "Transmission dynamics of varicella before, during and after the COVID-19 pandemic in Japan: a modelling study." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5998–6012. http://dx.doi.org/10.3934/mbe.2022280.
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<abstract> <p>Public health and social measures (PHSMs) targeting the coronavirus disease 2019 (COVID-19) pandemic have potentially affected the epidemiological dynamics of endemic infectious diseases. In this study, we investigated the impact of PHSMs for COVID-19, with a particular focus on varicella dynamics in Japan. We adopted the susceptible-infectious-recovered type of mathematical model to reconstruct the epidemiological dynamics of varicella from Jan. 2010 to Sep. 2021. We analyzed epidemiological and demographic data and estimated the within-year and multi-year component of the force of infection and the biases associated with reporting and ascertainment in three periods: pre-vaccination (Jan. 2010–Dec. 2014), pre-pandemic vaccination (Jan. 2015–Mar. 2020) and during the COVID-19 pandemic (Apr. 2020–Sep. 2021). By using the estimated parameter values, we reconstructed and predicted the varicella dynamics from 2010 to 2027. Although the varicella incidence dropped drastically during the COVID-19 pandemic, the change in susceptible dynamics was minimal; the number of susceptible individuals was almost stable. Our prediction showed that the risk of a major outbreak in the post-pandemic era may be relatively small. However, uncertainties, including age-related susceptibility and travel-related cases, exist and careful monitoring would be required to prepare for future varicella outbreaks.</p> </abstract>
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Sibarani, Imelda Juliana Br, Katherina Meylda Loy S, and Suharjito Suharjito. "Enhancing Predictive Accuracy for Differentiated Thyroid Cancer (DTC) Recurrence Through Advanced Data Mining Techniques." TIN: Terapan Informatika Nusantara 5, no. 1 (2024): 11–22. http://dx.doi.org/10.47065/tin.v5i1.5237.
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Thyroid cancer is becoming more common, and its 20% recurrence rate of which almost half are discovered more than five years after surgery, highlights how difficult it is to distinguish between a true disease relapse and chronic disease brought on by insufficient initial treatment. This ambiguity highlights the complicated dynamics that drive the mortality rates in patients with thyroid cancer. The purpose of this study is to be refining these predictions to control Differentiated Thyroid Cancer recurrence and minimize the risk of recurrence. The dataset was obtained by monitoring a total of 383 patients with 17 attributes. This study adopted a data mining modelling strategy to evaluate the performance, classification accuracy, and cluster distribution, utilizing the Orange data mining software. The Exploratory Data Analysis was conducted to pinpoint the most significant contributors. Subsequently, a variety of supervised techniques were applied to assess the precision of both single and ensemble models in classification. For cluster determination, we implemented several unsupervised learning techniques, including k-means, hierarchical, and Louvain Clustering. The result shows that ensemble stacking algorithm demonstrated superior performance and classification accuracy, achieving impressive scores of 0.971. The analysis of clustering methods, notably k-means and hierarchical clustering, suggested that the dataset could be segmented into two distinct clusters. The most dominant factors in influencing the recurrence of thyroid cancer with strong correlation revealed 'Response', 'Risk', 'Adenopathy', and 'N'. The refinement of the diagnostic model, through the identification of accurate models and key factors, enhances the prediction of Differentiated Thyroid Cancer recurrence.
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Thomas, Charlotte M., Joseph F. Standing, Catherine Smith, et al. "BT34 Minimizing drug exposure in psoriasis using a therapeutic drug monitoring dashboard." British Journal of Dermatology 191, Supplement_1 (2024): i204—i205. http://dx.doi.org/10.1093/bjd/ljae090.431.
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Abstract There are growing numbers of individuals with psoriasis who have clear or nearly clear skin (i.e. disease control) on high-cost biologic drugs. They currently continue biologic treatment indefinitely, resulting in a long-term drug and healthcare burden. Prior research indicates that some sustain disease control on less frequent doses than the current ‘one-size-fits-all’ standard dosing regimen, particularly those with higher serum drug concentrations. This indicates a role for therapeutic drug monitoring (TDM) in enabling personalized drug minimization strategies. We sought to develop a TDM dashboard, incorporating a pharmacokinetic (PK) model to deliver real-time predictions for informing personalized dosing decisions. We searched the PKPDAI and PubMed databases for models describing population pharmacokinetics of the exemplar psoriasis biologic risankizumab. The PK model was run using the MAPBAYR R package, incorporating serum drug concentrations as Bayesian priors. The model was validated using an independent real-world psoriasis dataset (n = 30, BSTOP study) by calculating the mean prediction error (MPE) and root-mean-square error (RMSE). Finally, the model was incorporated into a bespoke TDM dashboard, developed using open-source R Shiny software. We identified a two-compartment PK model with first-order absorption and elimination processes derived from pooled phase I–III risankizumab clinical trial data (13 123 observations from 1899 patients, 71% male, median age 47 years, median bodyweight 87 kg). The PK model was developed using a nonlinear mixed-effects modelling approach and accounted for interindividual variability in drug clearance, volume of distribution, absorption constant and bioavailability. Covariates incorporated into the model included bodyweight and serum concentrations of anti-risankizumab antibody, albumin, creatinine, and high-sensitivity C-reactive protein. We validated the model using the independent real-world psoriasis dataset (MPE 0.74 mg L−1, RMSE 2.77 mg L−1) and incorporated it into a bespoke TDM dashboard (i.e. a real-time dosing interval calculator). The TDM dashboard is a user-friendly online interface that generates personalized dose interval recommendations following input of relevant variables (date of last dose, serum drug concentration, covariates and Psoriasis Area and Severity Index score) and a graphic of the PK profile. A randomized controlled feasibility trial (launch Q2 2024) will determine the patient and clinician acceptability and real-world practicality of TDM dashboard-informed biologics dosing. The dashboard enables proactive TDM, so that drug exposure can be minimized in individuals with psoriasis who have clear or nearly skin for ≥ 12 months on risankizumab. A pharmacodynamic turnover model is being developed to link to the PK model, with the potential to include other biologic drugs for optimal impact on overall drug exposure, healthcare burden and cost.
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Ferrari, Simone, Alessandro Santus, and Luca Tendas. "Validation of a numerical software for the simulation of the pollutant dispersion from traffic in a real case: Some preliminary results." EPJ Web of Conferences 299 (2024): 01010. http://dx.doi.org/10.1051/epjconf/202429901010.
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An increasing attention of citizens and policy-makers is devoted to the monitoring and modelling of urban traffic-related air pollution (TRAP), as there is a demonstrated relationship among this and human health effects (e.g. circulatory and ischemic heart diseases, lung cancer, asthma onset in children and adults, and acute lower respiratory infections in children). In this work, we investigate the capability of the ENVI- met® software to reproduce the concentrations of pollutants, emitted from vehicular traffic, and the meteorological parameters, both measured by a specific monitoring station, to evaluate its potential use for the TRAP prediction. Starting from the meteorological and traffic flow data of a specific day, a number of simulations, with different configurations, have been run and the results (temporal and spatial distribution of meteorological parameters and pollutants concentrations) have been compared with the monitored data, provided by the ARPAS (Agenzia Regionale per la Protezione dell’Ambiente della Sardegna – Regional Agency for the Protection of the Sardinian Environment) and measured by the weather station and the air quality monitoring station CENCA1 in Cagliari (Italy). The results of these comparisons are encouraging and can help, among the others, in better understanding the urban traffic pollutant dispersion and in optimizing the location of the air quality monitoring stations.
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Maciukiewicz, M., J. Schniering, H. Gabrys, et al. "OP0150 MACHINE LEARNING APPROACHES FOR RISK MODELLING IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH SYSTEMIC SCLEROSIS USING HIGH DIMENSIONAL IMAGE ANALYSIS." Annals of the Rheumatic Diseases 80, Suppl 1 (2021): 90. http://dx.doi.org/10.1136/annrheumdis-2021-eular.2517.
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Background:The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).Objectives: •bTo develop robust, machine learning (ML) workflow for “radiomics” data in SSc-ILD to select optimal methods for prediction. •oTo predict the time to individual lung function decline defined as defined by the time to a relative decline of ≥ 15% in Forced Vital Capacity (FVC)% as previously (3), using workflow.Methods:We investigated two cohorts of SSc-ILD: 90 patients (76.7% female, median age 57.5 years) from the University Hospital Zurich and 66 patients (75.8% female, median age 61.0 years) from Oslo University Hospital’s. Patients were retrospectively selected if (3): a) diagnosed with early/mild SSc according to the Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria, b) presence of ILD on HRCT as determined by a senior radiologist. For every subject, we defined 1,355 robust radiomic features from HRCT images. The follow-up period was defined as the time interval between baseline visit and the last available follow-up visit.We have developed a systematic computational workflow to build predictive ML models. To reduce the number of redundant radiomic features, we applied correlation thresholds. We applied distinct methods including 1) Lasso Penalized Regression for feature selection, and 2) Random Forest (RF) for modeling using the R package ‘caret’. To select the optimal ML model, we randomly divided derivation cohort into Training (70%) and Holdout (30%) sets and applied fivefold cross-validation (5kCV) for feature and classifier selection on Training set only.Results:We have investigated various methods to select the optimal set of predictive radiomic features. Since the ML model performance is affected by both, feature, and classifier selection, we assessed these factors first.Results from feature filtering and selection, suggested that the combination of correlation threshold of 0.9 with Lasso regression proved best. As we perform feature selection in 5k CV workflow, features present in at least 2 sets entered model optimization step.During model selection, we selected RF classifier. We detected positive correlation between actual and predicted values with Spearman’s rho = 0.313, p = 0.167 and Spearman’s rho = 0.341, p = 0.015 in Oslo and Holdout sets respectively, as shown on Figure 1. The percentage of variance remained modest for both Holdout (Rsq = 0.104) and Oslo (Rsq = 0.126) datasets.Figure 1.Performance of the best, RF classifier shown as scatterplot between actual and predicted values of individual time to lung decline.Conclusion:In summary, we: (1) developed ML workflow that allowed to select o optimal methodology for modeling (i.e., feature and classifier selection), and (2) provide models that predicted time to individual lung function decline, characterized by significant correlation between predicted and actual values.References:[1]Hansell DM, Goldin JG, King TE, Jr., Lynch DA, Richeldi L, Wells AU. CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015;3(6):483-96.[2]Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).[3]Schniering J. et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. https://www.medrxiv.org/content/10.1101/2020.06.09.20124800v1Disclosure of Interests:None declared
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Kantasiripitak, W., S. G. WIcha, D. Thomas, et al. "P531 A model-based tool for guiding infliximab induction dosing to maximise long-term deep remission in children with inflammatory bowel diseases." Journal of Crohn's and Colitis 17, Supplement_1 (2023): i659—i661. http://dx.doi.org/10.1093/ecco-jcc/jjac190.0661.
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Abstract Background Adequate infliximab (IFX) concentrations during induction treatment are predictive for deep remission (corticosteroid-free clinical and endoscopic remission) at six months in children with Crohn’s disease (CD) and ulcerative colitis (UC).1 Under standard IFX induction dosing, children often have low IFX trough concentrations.2 Model-informed precision dosing (MIPD) is advocated as a promising IFX dosing strategy. We aimed to develop and validate an MIPD framework for guiding paediatric IFX induction treatment. Methods Data from 31 children (4-18 years, 15-76 kg, 20:11 CD:UC) receiving standard IFX induction dosing (5 mg/kg at week [w]0, w2, and w6) were repurposed.1 Eight paediatric population pharmacokinetic models were evaluated (six 2-compartment models and two 1-compartment models). Modelling and simulation were used to identify exposure targets, an optimal sampling strategy, and to develop a multi-model prediction algorithm for implementation into an MIPD software tool.3 A role for IFX clearance monitoring was evaluated. Results A total of 251 IFX concentrations were available (6-10 samples per patient). One patient had undetectable IFX concentrations at w6 and w12 but had no measurable antibodies to IFX (ATI). At six months after start of the IFX treatment, 58% (18/31) of patients achieved deep remission. The measured IFX concentration at w12 was the best predictor and classifier for deep remission. A 7.5 mg/L IFX concentration target at w12 was associated with a 64% probability of deep remission at six months (Figure 1). With standard dosing, less than 80% of simulated children &lt;40 kg attained this target (Figure 2). Immunomodulator combo-therapy only subtly improved probability of target attainment. Presence of ATI lowered the target attainment considerably. The w12 target was most accurately and precisely achieved by implementing MIPD at w6 using the w6 IFX concentration. The multi-model algorithm outperformed single models when optimising the w6 dose based on combined w2 and w4 concentrations. MIPD using only the w2 concentration resulted in biased and imprecise predictions. The predictive performance of the MIPD multi-model algorithm was robust to misspecification of ATI status. IFX clearances at w6 and w12 were predictive for deep remission (P=0.02) (Figure 3). An IFX module was added to the TDMx software tool to facilitate MIPD and clearance monitoring (https://tdmx.shinyapps.io/Infliximab_paediatric/). Conclusion A freely available, interactive, multi-model MIPD software tool is provided to facilitate IFX induction dosing and improve deep remission rates in children with CD and UC. 1van Hoeve et al. J Pediatr 2022. 2van Rheenen et al. J Crohns Colitis 2020. 3Kantasiripitak et al. CPT:PSP 2022.
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Bose, Sanjukta N., Adam Verigan, Jade Hanson, et al. "Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data." Cardiology in the Young 29, no. 11 (2019): 1340–48. http://dx.doi.org/10.1017/s1047951119002002.
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AbstractObjective:To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.Methods:We performed a single-institution retrospective cohort study (11 January 2013–16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children’s hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model’s discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.Results:The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.Conclusions:Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.
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Drake, Wonder P., Connie Hsia, Lobelia Samavati, et al. "Risk Indicators of Sarcoidosis Evolution-Unified Protocol (RISE-UP): protocol for a multi-centre, longitudinal, observational study to identify clinical features that are predictive of sarcoidosis progression." BMJ Open 13, no. 4 (2023): e071607. http://dx.doi.org/10.1136/bmjopen-2023-071607.
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IntroductionSarcoidosis is a pulmonary and systemic granulomatous disease with a wide range of potential outcomes, from spontaneous resolution to end-stage organ damage and death. Currently, clinicians have no easy-to-use risk stratification tools for important clinical outcomes in sarcoidosis, such as progressive lung disease. This study will address two clinical practice needs: (1) development of a risk calculator that provides an estimate of the likelihood of pulmonary progression in sarcoidosis patients during the follow-up period and (2) determine the optimal interval for serial clinical monitoring (eg, 6, 12, 18 months) using these risk prediction tools.Methods and analysisThe Risk Indicators of Sarcoidosis Evolution-Unified Protocol study is a National Institutes of Health-sponsored, longitudinal observational study of adults with pulmonary sarcoidosis who will be enrolled at five US tertiary care centres. Participants will be evaluated at approximately 6-month intervals for up to 60 months with collection of lung function, blood samples and clinical data. The target sample size is 557 and the primary objective is to determine which clinical features measured during a routine clinic visit carry the most prognostic information for predicting clinical progression of pulmonary sarcoidosis over the follow-up period. The primary outcome measure will be quantified by a clinically meaningful change in forced vital capacity, forced expiratory volume in 1 s or diffusing capacity of the lung for carbon monoxide. The secondary objective is to determine if blood biomarkers measured during a routine clinic visit can improve the risk assessment modelling for progression of pulmonary sarcoidosis over the follow-up period.Ethics and disseminationThe study protocol has been approved by the Institutional Review Boards at each centre and the reliance Institutional Review Board overseeing the study (WCG, Protocol #20222400). Participants will provide informed consent prior to enrolment. Results will be disseminated via publication in a relevant peer-reviewed journal.Trial registration numberNCT05567133.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Gerasimenko, Petr V. "Modeling the number of COVID-19 cases in St. Petersburg in the period 2020–2022." City Healthcare 3, no. 3 (2022): 30–38. http://dx.doi.org/10.47619/2713-2617.zm.2022.v.3i3;30-38.
Pełny tekst źródłaStreszczenie:
Intoduction. The construction of mathematical models of changes in the total and daily amounts of the coronavirus of the population of St. Petersburg in various segments and the period from 2020 to 2022. The need for research is dictated by the presence of a dysfunctional situation in the city, as well as the need to develop a methodological apparatus for short-term operational assessment of changes and forecasting of key indicators of the spread of coronavirus.
 Purpose. To assess the change in the total and daily indicators of coronavirus disease in the population of St. Petersburg in the periods May-August 2020 and 2021 and to carry out a short-term forecast.
 Methods. The solution of the problem was carried out by modeling and performing short-term prediction of the folding situation of coronavirus in St. Petersburg by the total (integral) and daily (differential) number of diseases in the region. Modelling is based on statistics that are generated through monitoring by coordinating councils to combat the spread of COVID-19 in regions and in the country.
 Results. An approach and mathematical apparatus for modeling and forecasting the dynamics of regional key indicators of the spread of the pandemic in the regions of Russia are proposed.
 Practical relevance. The proposed solution to the problem will enable the administration and health authorities to receive scientific information for evaluating and adjusting their work to create normal economic and social living conditions for residents of Russian regions.
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Cowled, Brendan D., Fiona Giannini, Sam D. Beckett, et al. "Feral pigs: predicting future distributions." Wildlife Research 36, no. 3 (2009): 242. http://dx.doi.org/10.1071/wr08115.
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Feral pig populations are expanding in many regions of the world following historically recent introductions. Populations are controlled to reduce damage to agriculture and the environment, and are also a recreational hunting resource. Knowledge of the area over which feral pigs may expand in the future could be used regionally to assist biosecurity planning, control efforts and the protection of biodiversity assets. The present study sought to estimate the future distribution of a recently introduced, expanding feral pig population in the remote Kimberley region of north-western Australia. An existing survey of feral pig distributions was enhanced and remote-sensing and weather data, reflecting or correlated with factors that may affect feral pig distributions, were collated and analysed. Relationships between feral pig distributions and these data were identified by using a generalised additive modelling approach. By the use of the model, the distribution of favourable habitat was estimated across the study region (89 125 km2). The potential future distribution of feral pigs in the Kimberley was then estimated, assuming only natural dispersal of feral pigs from areas of known feral pig status (cf. hunter-assisted movements or escape of domestic pigs). The modelling suggests that feral pigs could expand their distribution by realistic natural dispersal in the future (to 61 950 km2). This expansion possibility contains several strategically important areas (such as sea ports and biologically significant wetlands). This approach has the potential to improve biosecurity planning for the containment of the feral pig in the Kimberley and may have utility for other recently introduced invasive species in other regions. These results may also be used to improve pest-management programmes and contingency planning for exotic-disease incursions.
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Britton, Tom, and Gianpaolo Scalia Tomba. "Estimation in emerging epidemics: biases and remedies." Journal of The Royal Society Interface 16, no. 150 (2019): 20180670. http://dx.doi.org/10.1098/rsif.2018.0670.
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When analysing new emerging infectious disease outbreaks, one typically has observational data over a limited period of time and several parameters to estimate, such as growth rate, the basic reproduction numberR0, the case fatality rate and distributions of serial intervals, generation times, latency and incubation times and times between onset of symptoms, notification, death and recovery/discharge. These parameters form the basis for predicting a future outbreak, planning preventive measures and monitoring the progress of the disease outbreak. We study inference problems during the emerging phase of an outbreak, and point out potential sources of bias, with emphasis on: contact tracing backwards in time, replacing generation times by serial intervals, multiple potential infectors and censoring effects amplified by exponential growth. These biases directly affect the estimation of, for example, the generation time distribution and the case fatality rate, but can then propagate to other estimates such asR0and growth rate. We propose methods to remove or at least reduce bias using statistical modelling. We illustrate the theory by numerical examples and simulations.
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Glauche, Ingmar, Hendrik Liebscher, Christoph Baldow, et al. "A New Computational Method to Predict Long-Term Minimal Residual Disease and Molecular Relapse after TKI-Cessation in CML." Blood 128, no. 22 (2016): 3099. http://dx.doi.org/10.1182/blood.v128.22.3099.3099.
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Abstract Predicting minimal residual disease (MRD) levels in tyrosine kinase inhibitor (TKI)-treated chronic myeloid leukemia (CML) patients is of major clinical relevance. The reason is that residual leukemic (stem) cells are the source for both, potential relapses of the leukemicclone but also for its clonal evolution and, therefore, for the occurrence of resistance. The state-of-the art method for monitoring MRD in TKI-treated CML is the quantification of BCR-ABL levels in the peripheral blood (PB) by PCR. However, the question is whether BCR-ABL levels in the PB can be used as a reliable estimate for residual leukemic cells at the level of hematopoietic stem cells in the bone marrow (BM). Moreover, once the BCR-ABL levels have been reduced to undetectable levels, information on treatment kinetics is censored by the PCR detection limit. Clearly, BCR-ABL negativity in the PB suggests very low levels of residual disease also in the BM, but whether the MRD level remains at a constant level or decreases further cannot be read from the BCR-ABL negativity itself. Thus, also the prediction of a suitable time point for treatment cessation based on residual disease levels cannot be obtained from PCR monitoring in the PB and currently remains a heuristic decision. To overcome the current lack of a suitable biomarker for residual disease levels in the BM, we propose the application of a computational approach to quantitatively describe and predict long-term BCR-ABL levels. The underlying mathematical model has previously been validated by the comparison to more than 500 long-term BCR-ABL kinetics in the PB from different clinical trials under continuous TKI-treatment [1,2,3]. Here, we present results that show how this computational approach can be used to estimate MRD levels in the BM based on the measurements in the PB. Our results demonstrate that the mathematical model can quantitatively reproduce the cumulative incidence of the loss of deep and major molecular response in a population of patients, as published by Mahon et al. [4] and Rousselot et al. [5]. Furthermore, to demonstrate how the model can be used to predict the BCR-ABL levels and to estimate the molecular relapse probability of individual patients, we compare simulation results with more than 70 individual BCR-ABL-kinetics. For this analysis we use patient data from different clinical studies (e.g. EURO-SKI: NCT01596114, STIM(s): NCT00478985, NCT01343173) where TKI-treatment had been stopped after prolonged deep molecular response periods. Specifically, we propose to combine statistical (non-linear regression) and mechanistic (agent-based) modelling techniques, which allows us to quantify the reliability of model predictions by confidence regions based on the quality (i.e. number and variance) of the clinical measurements and on the particular kinetic response characteristics of individual patients. The proposed approach has the potential to support clinical decision making because it provides quantitative, patient-specific predictions of the treatment response together with a confidence measure, which allows to judge the amount of information that is provided by the theoretical prediction. References [1] Roeder et al. (2006) Dynamic modeling of imatinib-treated chronic myeloid leukemia: functional insights and clinical implications, Nat Med 12(10):1181-4 [2] Horn et al. (2013) Model-based decision rules reduce the risk of molecular relapse after cessation of tyrosine kinase inhibitor therapy in chronic myeloid leukemia, Blood 121(2):378-84. [3] Glauche et al. (2014) Model-Based Characterization of the Molecular Response Dynamics of Tyrosine Kinase Inhibitor (TKI)-Treated CML Patients a Comparison of Imatinib and Dasatinib First-Line Therapy, Blood 124:4562 [4] Mahon et al. (2010) Discontinuation of imatinib in patients with chronic myeloid leukaemia who have maintained complete molecular remission for at least 2 years: the prospective, multicentre Stop Imatinib (STIM) trial. Lancet Oncol 11(11):1029-35 [5] Rousselot et al. (2014) Loss of major molecular response as a trigger for restarting TKI therapy in patients with CP- CML who have stopped Imatinib after durable undetectable disease, JCO 32(5):424-431 Disclosures Glauche: Bristol Meyer Squib: Research Funding. von Bubnoff:Amgen: Honoraria; Novartis: Honoraria, Research Funding; BMS: Honoraria. Saussele:ARIAD: Honoraria; Novartis: Honoraria, Other: Travel grants, Research Funding; Pfizer: Honoraria, Other: Travel grants; BMS: Honoraria, Other: Travel grants, Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Novartis: Honoraria, Research Funding. Guilhot:CELEGENE: Consultancy. Mahon:NOVARTIS PHARMA: Honoraria, Research Funding; BMS: Honoraria; PFIZER: Honoraria; ARIAD: Honoraria. Roeder:Bristol-Myers Squibb: Honoraria, Research Funding.
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Heasley, Cole, J. Johanna Sanchez, Jordan Tustin, and Ian Young. "Systematic review of predictive models of microbial water quality at freshwater recreational beaches." PLOS ONE 16, no. 8 (2021): e0256785. http://dx.doi.org/10.1371/journal.pone.0256785.
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Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18–24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.
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Marston, Christopher, Clare Rowland, Aneurin O’Neil, et al. "Developing the Role of Earth Observation in Spatio-Temporal Mosquito Modelling to Identify Malaria Hot-Spots." Remote Sensing 15, no. 1 (2022): 43. http://dx.doi.org/10.3390/rs15010043.
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Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors—Anopheles gambiae s.l., An. funestus, and An. paludis—in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. funestus, and 120 for An. paludis. Model validation produced R2 values of 0.717 for An. gambiae s.l., 0.861 for An. funestus, and 0.448 for An. paludis. Monthly abundance values were extracted for 248,089 individual buildings, demonstrating how species abundance, and therefore biting pressure, varies spatially and seasonally on a building-to-building basis. These methods advance previous broader regional mosquito mapping and can provide a crucial tool for designing bespoke control programs and for improving the targeting of resource-constrained disease control activities to reduce malaria transmission and subsequent mortality in endemic regions, in line with the WHO’s ‘High Burden to High Impact’ initiative. The developed method was designed to be widely applicable to other areas, where suitable in situ mosquito monitoring data are available. Training materials were also made freely available in multiple languages, enabling wider uptake and implementation of the methods by users without requiring prior expertise in EO.
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Ejma-Multański, Adam, Anna Wajda, and Agnieszka Paradowska-Gorycka. "Cell Cultures as a Versatile Tool in the Research and Treatment of Autoimmune Connective Tissue Diseases." Cells 12, no. 20 (2023): 2489. http://dx.doi.org/10.3390/cells12202489.
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Cell cultures are an important part of the research and treatment of autoimmune connective tissue diseases. By culturing the various cell types involved in ACTDs, researchers are able to broaden the knowledge about these diseases that, in the near future, may lead to finding cures. Fibroblast cultures and chondrocyte cultures allow scientists to study the behavior, physiology and intracellular interactions of these cells. This helps in understanding the underlying mechanisms of ACTDs, including inflammation, immune dysregulation and tissue damage. Through the analysis of gene expression patterns, surface proteins and cytokine profiles in peripheral blood mononuclear cell cultures and endothelial cell cultures researchers can identify potential biomarkers that can help in diagnosing, monitoring disease activity and predicting patient’s response to treatment. Moreover, cell culturing of mesenchymal stem cells and skin modelling in ACTD research and treatment help to evaluate the effects of potential drugs or therapeutics on specific cell types relevant to the disease. Culturing cells in 3D allows us to assess safety, efficacy and the mechanisms of action, thereby aiding in the screening of potential drug candidates and the development of novel therapies. Nowadays, personalized medicine is increasingly mentioned as a future way of dealing with complex diseases such as ACTD. By culturing cells from individual patients and studying patient-specific cells, researchers can gain insights into the unique characteristics of the patient’s disease, identify personalized treatment targets, and develop tailored therapeutic strategies for better outcomes. Cell culturing can help in the evaluation of the effects of these therapies on patient-specific cell populations, as well as in predicting overall treatment response. By analyzing changes in response or behavior of patient-derived cells to a treatment, researchers can assess the response effectiveness to specific therapies, thus enabling more informed treatment decisions. This literature review was created as a form of guidance for researchers and clinicians, and it was written with the use of the NCBI database.
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Sánchez-pérez, Isabel, Jorge Melones Herrero, Alicia Villacampa, et al. "P160 MODELLING CARDIOVASCULAR TOXICITY IN CELLULO ASSOCIATED WITH ANTITUMORALS." Journal of Hypertension 42, Suppl 3 (2024): e119. http://dx.doi.org/10.1097/01.hjh.0001063512.42008.51.
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Background and Objective: Gastrointestinal cancer (GIC) stands as a leading cause of cancer-related mortality worldwide. Currently combination chemotherapy, particularly metal-based drugs, often induces side effects, limiting their clinical efficacy. Cardiotoxicity, a common complication associated with various therapeutic agents, manifests through vascular endothelial dysfunction, a hallmark of ischemic coronary disease. Our aim is to design novel metal-based drugs with enhanced efficacy, specificity, and mitigated off-target cytotoxicity. We identified the isomers trans and cis-[PtI2(isopropylamine)2] (I5 and I6, respectively), as promising candidates against GIC, both in vitro and in vivo 1. RNA seq was performed in 6 different GIC cells. The Gene Set Enrichment Analysis (GSEA) revealed a significant enrichment of inflammation-related genes and downregulation of angiogenesis-associated pathways in treated cells. Our objective now is to develop preclinical studies of the cardiovascular toxicity of I5 and I6. Methods: Cardiovascular effects of these drugs were studies using a in cellulo model system (AC16, HUVEC cell lines). Biological and biochemistry approaches and techniques were used to assess senescence, mitochondrial respiration, viability by MTS and wound healing assays. Results: Our findings indicate that both I5 and I6 decrease cellular viability, induce senescence (as evidenced by β-galactosidase activity), and generate reactive oxygen species compared to commonly used cisplatin. Additionally, I5 impact oxidative phosphorylation (OXPHOS) and glycolysis metabolism. Furthermore, a wound healing assay suggests that the migration of cells is modulated in response to treatment with these compounds. Conclusions: Accurate preclinical assessment of the effects of novel drugs on the vascular endothelium is imperative to address treatment-induced endothelial dysfunction before envisioning clinical trials. Understanding the impact of anti-cancer agents on the vascular endothelium will not only inform therapeutic strategies to prevent or reverse treatment-induced cardiotoxicity but may also serve as a vital tool for predicting, monitoring, and preventing adverse cardiovascular outcomes in patients undergoing cancer treatment. 1 Commun Biol 7, 353, doi:10.1038/s42003-024-06052-5 (2024).
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Skendžić, Sandra, Monika Zovko, Ivana Pajač Živković, Vinko Lešić, and Darija Lemić. "The Impact of Climate Change on Agricultural Insect Pests." Insects 12, no. 5 (2021): 440. http://dx.doi.org/10.3390/insects12050440.
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Climate change and global warming are of great concern to agriculture worldwide and are among the most discussed issues in today’s society. Climate parameters such as increased temperatures, rising atmospheric CO2 levels, and changing precipitation patterns have significant impacts on agricultural production and on agricultural insect pests. Changes in climate can affect insect pests in several ways. They can result in an expansion of their geographic distribution, increased survival during overwintering, increased number of generations, altered synchrony between plants and pests, altered interspecific interaction, increased risk of invasion by migratory pests, increased incidence of insect-transmitted plant diseases, and reduced effectiveness of biological control, especially natural enemies. As a result, there is a serious risk of crop economic losses, as well as a challenge to human food security. As a major driver of pest population dynamics, climate change will require adaptive management strategies to deal with the changing status of pests. Several priorities can be identified for future research on the effects of climatic changes on agricultural insect pests. These include modified integrated pest management tactics, monitoring climate and pest populations, and the use of modelling prediction tools.
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Zhao, Wei, Daolun Zhang, Thomas Storme, André Baruchel, Xavier Declèves, and Evelyne Jacqz-Aigrain. "POPULATION PHARMACOKINETICS AND DOSING OPTIMIZATION OF TEICOPLANIN IN CHILDREN WITH MALIGNANT HAEMATOLOGICAL DISEASE." Archives of Disease in Childhood 101, no. 1 (2015): e1.41-e1. http://dx.doi.org/10.1136/archdischild-2015-310148.46.
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BackgroundChildren with haematological malignancy represent an identified subgroup of the paediatric population with specific pharmacokinetic parameters. In these patients, inadequate empirical antibacterial therapy may result in infection-related morbidity and increased mortality, making optimization of the dosing regimen essential. As paediatric data are limited, our aim was to evaluate the population pharmacokinetics of teicoplanin in order to define the appropriate dosing regimen in this high-risk population.MethodsThe current dose of teicoplanin was evaluated in children with haematological malignancy. Population pharmacokinetics of teicoplanin was analysed using NONMEM software. The dosing regimen was optimised based on the final model.ResultsEighty-five children (age range: 0.5 to 16.9 years) were included. Therapeutic drug monitoring and opportunistic samples (n=143) were available for analysis. With the current recommended dose of 10 mg/kg/day, 41 children (48%) had sub-therapeutic steady-state trough concentrations (Css,min<10 mg/liter). A two-compartment pharmacokinetic model with first-order elimination was developed. Systematic covariate analysis identified that bodyweight (size) and creatinine clearance significantly influenced teicoplanin clearance. The model was validated internally. Its predictive performance was further confirmed in an external validation. In order to reach the target AUC of 750 mg·h/L, 18 mg/kg was required for infants, 14 mg/kg for children and 12 mg/kg for adolescents. A patient-tailored dose regimen was further developed and reduced variability in AUC and Css,min values compared to the mg/kg-basis dose, making the modelling approach an important tool for dosing individualization.ConclusionsThis first population pharmacokinetic study of teicoplanin in children with haematological malignancy provided evidence-based support to individualize teicoplanin therapy in this vulnerable population.
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Perera, Rafael, Richard Stevens, Jeffrey K. Aronson, et al. "Long-term monitoring in primary care for chronic kidney disease and chronic heart failure: a multi-method research programme." Programme Grants for Applied Research 9, no. 10 (2021): 1–218. http://dx.doi.org/10.3310/pgfar09100.
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Background Long-term monitoring is important in chronic condition management. Despite considerable costs of monitoring, there is no or poor evidence on how, what and when to monitor. The aim of this study was to improve understanding, methods, evidence base and practice of clinical monitoring in primary care, focusing on two areas: chronic kidney disease and chronic heart failure. Objectives The research questions were as follows: does the choice of test affect better care while being affordable to the NHS? Can the number of tests used to manage individuals with early-stage kidney disease, and hence the costs, be reduced? Is it possible to monitor heart failure using a simple blood test? Can this be done using a rapid test in a general practitioner consultation? Would changes in the management of these conditions be acceptable to patients and carers? Design Various study designs were employed, including cohort, feasibility study, Clinical Practice Research Datalink analysis, seven systematic reviews, two qualitative studies, one cost-effectiveness analysis and one cost recommendation. Setting This study was set in UK primary care. Data sources Data were collected from study participants and sourced from UK general practice and hospital electronic health records, and worldwide literature. Participants The participants were NHS patients (Clinical Practice Research Datalink: 4.5 million patients), chronic kidney disease and chronic heart failure patients managed in primary care (including 750 participants in the cohort study) and primary care health professionals. Interventions The interventions were monitoring with blood and urine tests (for chronic kidney disease) and monitoring with blood tests and weight measurement (for chronic heart failure). Main outcome measures The main outcomes were the frequency, accuracy, utility, acceptability, costs and cost-effectiveness of monitoring. Results Chronic kidney disease: serum creatinine testing has increased steadily since 1997, with most results being normal (83% in 2013). Increases in tests of creatinine and proteinuria correspond to their introduction as indicators in the Quality and Outcomes Framework. The Chronic Kidney Disease Epidemiology Collaboration equation had 2.7% greater accuracy (95% confidence interval 1.6% to 3.8%) than the Modification of Diet in Renal Disease equation for estimating glomerular filtration rate. Estimated annual transition rates to the next chronic kidney disease stage are ≈ 2% for people with normal urine albumin, 3–5% for people with microalbuminuria (3–30 mg/mmol) and 3–12% for people with macroalbuminuria (> 30 mg/mmol). Variability in estimated glomerular filtration rate-creatinine leads to misclassification of chronic kidney disease stage in 12–15% of tests in primary care. Glycaemic-control and lipid-modifying drugs are associated with a 6% (95% confidence interval 2% to 10%) and 4% (95% confidence interval 0% to 8%) improvement in renal function, respectively. Neither estimated glomerular filtration rate-creatinine nor estimated glomerular filtration rate-Cystatin C have utility in predicting rate of kidney function change. Patients viewed phrases such as ‘kidney damage’ or ‘kidney failure’ as frightening, and the term ‘chronic’ was misinterpreted as serious. Diagnosis of asymptomatic conditions (chronic kidney disease) was difficult to understand, and primary care professionals often did not use ‘chronic kidney disease’ when managing patients at early stages. General practitioners relied on Clinical Commissioning Group or Quality and Outcomes Framework alerts rather than National Institute for Health and Care Excellence guidance for information. Cost-effectiveness modelling did not demonstrate a tangible benefit of monitoring kidney function to guide preventative treatments, except for individuals with an estimated glomerular filtration rate of 60–90 ml/minute/1.73 m2, aged < 70 years and without cardiovascular disease, where monitoring every 3–4 years to guide cardiovascular prevention may be cost-effective. Chronic heart failure: natriuretic peptide-guided treatment could reduce all-cause mortality by 13% and heart failure admission by 20%. Implementing natriuretic peptide-guided treatment is likely to require predefined protocols, stringent natriuretic peptide targets, relative targets and being located in a specialist heart failure setting. Remote monitoring can reduce all-cause mortality and heart failure hospitalisation, and could improve quality of life. Diagnostic accuracy of point-of-care N-terminal prohormone of B-type natriuretic peptide (sensitivity, 0.99; specificity, 0.60) was better than point-of-care B-type natriuretic peptide (sensitivity, 0.95; specificity, 0.57). Within-person variation estimates for B-type natriuretic peptide and weight were as follows: coefficient of variation, 46% and coefficient of variation, 1.2%, respectively. Point-of-care N-terminal prohormone of B-type natriuretic peptide within-person variability over 12 months was 881 pg/ml (95% confidence interval 380 to 1382 pg/ml), whereas between-person variability was 1972 pg/ml (95% confidence interval 1525 to 2791 pg/ml). For individuals, monitoring provided reassurance; future changes, such as increased testing, would be acceptable. Point-of-care testing in general practice surgeries was perceived positively, reducing waiting time and anxiety. Community heart failure nurses had greater knowledge of National Institute for Health and Care Excellence guidance than general practitioners and practice nurses. Health-care professionals believed that the cost of natriuretic peptide tests in routine monitoring would outweigh potential benefits. The review of cost-effectiveness studies suggests that natriuretic peptide-guided treatment is cost-effective in specialist settings, but with no evidence for its value in primary care settings. Limitations No randomised controlled trial evidence was generated. The pathways to the benefit of monitoring chronic kidney disease were unclear. Conclusions It is difficult to ascribe quantifiable benefits to monitoring chronic kidney disease, because monitoring is unlikely to change treatment, especially in chronic kidney disease stages G3 and G4. New approaches to monitoring chronic heart failure, such as point-of-care natriuretic peptide tests in general practice, show promise if high within-test variability can be overcome. Future work The following future work is recommended: improve general practitioner–patient communication of early-stage renal function decline, and identify strategies to reduce the variability of natriuretic peptide. Study registration This study is registered as PROSPERO CRD42015017501, CRD42019134922 and CRD42016046902. Funding This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 9, No. 10. See the NIHR Journals Library website for further project information.
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Akhgar, Ahmad, Dominic Sinibaldi, Lingmin Zeng, et al. "Urinary markers differentially associate with kidney inflammatory activity and chronicity measures in patients with lupus nephritis." Lupus Science & Medicine 10, no. 1 (2023): e000747. http://dx.doi.org/10.1136/lupus-2022-000747.
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ObjectiveLupus nephritis (LN) is diagnosed by biopsy, but longitudinal monitoring assessment methods are needed. Here, in this preliminary and hypothesis-generating study, we evaluate the potential for using urine proteomics as a non-invasive method to monitor disease activity and damage. Urinary biomarkers were identified and used to develop two novel algorithms that were used to predict LN activity and chronicity.MethodsBaseline urine samples were collected for four cohorts (healthy donors (HDs, n=18), LN (n=42), SLE (n=17) or non-LN kidney disease biopsy control (n=9)), and over 1 year for patients with LN (n=42). Baseline kidney biopsies were available for the LN (n=46) and biopsy control groups (n=9). High-throughput proteomics platforms were used to identify urinary analytes ≥1.5 SD from HD means, which were subjected to stepwise, univariate and multivariate logistic regression modelling to develop predictive algorithms for National Institutes of Health Activity Index (NIH-AI)/National Institutes of Health Chronicity Index (NIH-CI) scores. Kidney biopsies were analysed for macrophage and neutrophil markers using immunohistochemistry (IHC).ResultsIn total, 112 urine analytes were identified from LN, SLE and biopsy control patients as both quantifiable and overexpressed compared with HDs. Regression analysis identified proteins associated with the NIH-AI (n=30) and NIH-CI (n=26), with four analytes common to both groups, demonstrating a difference in the mechanisms associated with NIH-AI and NIH-CI. Pathway analysis of the NIH-AI and NIH-CI analytes identified granulocyte-associated and macrophage-associated pathways, and the presence of these cells was confirmed by IHC in kidney biopsies. Four markers each for the NIH-AI and NIH-CI were identified and used in the predictive algorithms. The NIH-AI algorithm sensitivity and specificity were both 93% with a false-positive rate (FPR) of 7%. The NIH-CI algorithm sensitivity was 88%, specificity 96% and FPR 4%. The accuracy for both models was 93%.ConclusionsLongitudinal predictions suggested that patients with baseline NIH-AI scores of ≥8 were most sensitive to improvement over 6–12 months. Viable approaches such as this may enable the use of urine samples to monitor LN over time.
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Perlini, Cinzia, Simone Garzon, Massimo Franchi, et al. "Risk perception and affective state on work exhaustion in obstetrics during the COVID-19 pandemic." Open Medicine 17, no. 1 (2022): 1599–611. http://dx.doi.org/10.1515/med-2022-0571.
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Abstract A multicenter cross-sectional survey study involving four Italian University Hospitals was performed to test the hypothesis that negative affect and positive affect (affective dimensions) mediate the association between risk perception (perceived risk of infection and death; cognitive dimensions) and the feeling of work exhaustion (WE) among obstetrics healthcare providers (HCPs) during the Coronavirus Disease 2019 (COVID-19) pandemic. Totally, 570 obstetrics HCPs were invited to complete the 104-item IPSICO survey in May 2020. A theoretical model built on the tested hypothesis was investigated by structural equation modelling. The model explained 32.2% of the WE variance. Only negative affect mediated the association between cognitive dimensions and WE and also the association between WE and psychological well-being before the pandemic, experiences of stressful events, female gender, and dysfunctional coping. Non-mediated associations with WE were observed for work perceived as a duty, experience of stressful events, support received by colleagues, and the shift strategy. Only previous psychological well-being, support by colleagues, and shift strategies were inversely associated with WE. Based on study results, monitoring negative than positive affect appears superior in predicting WE, with practical implications for planning psychological interventions in HCPs at the individual, interpersonal, and organizational levels.
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Zhang, Xianyu, Shiyao Lu, Hui Li, et al. "Abstract P1-05-27: Liquid Biopsy for HER2 Status Assessment in Breast Cancer Using Surrogate DNA Methylation Markers." Cancer Research 83, no. 5_Supplement (2023): P1–05–27—P1–05–27. http://dx.doi.org/10.1158/1538-7445.sabcs22-p1-05-27.
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Abstract Background: Emerging HER2-targeted drugs especially antibody–drug conjugates (ADCs) are promising and provide more options for breast cancer management. Current assessment of HER2 status and treatment decisions are mainly dependent on immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) on the primary tumor tissues. With the disease progression, the molecular status of the tumor may evolve and become discordant with the primary site. However, longitudinal monitoring of HER2 status is limited by infeasible repeated sampling of the tumor tissues. A non-invasive and accurate approach to obtaining real-time samples for measuring HER2 alterations is thus an unmet need for surveillance and guiding treatment selection in breast cancer. Detecting HER2 aberration in cell-free DNA (cfDNA) can allow repeated sampling and avoid effects from tumor heterogeneity of tissue biopsy. Previous approaches for HER2 status determination using liquid biopsy were mostly dependent on the detection of copy number changes in cfDNA, but the limited signal-to-noise ratio poses a great challenge to the accuracy and robustness of the tests. In this study, we identified a group of DNA methylation markers for determining HER2 status in cfDNA for breast cancer. Methods: Genome-wide DNA methylation sequencing was conducted in tissue (25 HER2-positive and 35 HER2-negative) and plasma (32 HER2-positive and 107 HER2-negative) samples to identify specific methylation markers for HER2 status. HER2-positive samples were defined by IHC 3+ and 2+ with FISH positive, while HER2-negative ones were IHC 0/1+ and 2+ with FISH negative. Candidate markers were verified in another two sets of plasma samples (1. 30 HER2-positive and 40 HER2-negative; 2. 33 HER2-positive and 53 HER2-negative) by using quantitative methylation-specific PCR (qMSP). The performance of the markers was estimated by the Wilcoxon test, receiver operating characteristic curve, and logistic regression modelling. Results: 36 HER2 status-specific markers were discovered from genome-wide DNA methylation sequencing. Based on the qMSP results, 11 markers were verified by the performance analyses. The individual area under the curve (AUC) of these markers was from 0.58 to 0.68. From logistic regression modelling and 2-fold cross-validation, a 7-marker diagnostic model was built and validated on plasma samples, with the highest AUC of 0.812. Conclusion: cfDNA methylation detection inferring HER2 overexpression is a novel and non-invasive option for monitoring HER2 status in breast cancer patients, with a potential application in response prediction of HER2-targeted treatments. Further validation of the test is undergoing in large multi-centre cohorts in China. Citation Format: Xianyu Zhang, Shiyao Lu, Hui Li, Xin Liu, Jun Wang, Liuhong Zeng, Zhipeng Lu, Siyu Liu, Yanling Yin, Marina Bibikova, Zhiwei Chen, Jian-Bing Fan, Da Pang. Liquid Biopsy for HER2 Status Assessment in Breast Cancer Using Surrogate DNA Methylation Markers [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-27.