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Alai, Daniel H., Séverine Arnold (-Gaille), and Michael Sherris. "Modelling cause-of-death mortality and the impact of cause-elimination." Annals of Actuarial Science 9, no. 1 (November 17, 2014): 167–86. http://dx.doi.org/10.1017/s174849951400027x.
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AbstractThe analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population-level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. Inherent dependence amongst the competing causes is accounted for in the framework, which also allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality. This has applications to scenario-based forecasting often used to assess the impact of changes in mortality. The multinomial model is shown to be more conservative than commonly used approaches based on the force of mortality. We use the model to demonstrate the impact of cause-elimination on aggregate mortality using residual life expectancy and apply the model to a French case study.
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DICKINSON, J. A., Y. T. WUN, and S. L. WONG. "Modelling death rates for carriers of hepatitis B." Epidemiology and Infection 128, no. 1 (February 2002): 83–92. http://dx.doi.org/10.1017/s0950268801006410.
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Hepatitis B carriers who acquired the infection perinatally die from hepatocellular carcinoma (HCC) and cirrhosis at high rates. Published cohort studies are largely limited to males and are too small to estimate the age-specific risk of death. We therefore used routinely collected Hong Kong data to estimate the risks. Deaths were partitioned between carriers and non-carriers, then current life table calculations determined life expectancy and probability of dying from HCC or cirrhosis. HCC is the dominant cause of death for male carriers in middle adulthood with a lifetime risk of 27% for HCC compared to 4% for females. Predicted life expectancy is 72 years for male carriers, compared to 79 years for non-carriers. Female carriers have a life expectancy of 81 years and non-carriers 83 years. This model probably applies to all southern Chinese populations and emigrants with similar life history, and other populations that acquired infection early in life.
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Pitcher, M. J., S. A. Dobson, T. W. Kelsey, J. Chaplain, D. J. Sloan, S. H. Gillespie, and R. Bowness. "How mechanistic in silico modelling can improve our understanding of TB disease and treatment." International Journal of Tuberculosis and Lung Disease 24, no. 11 (November 1, 2020): 1145–50. http://dx.doi.org/10.5588/ijtld.20.0107.
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TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.
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Davis, Timothy M. E., S. A. Paul Chubb, and Wendy A. Davis. "The relationship between estimated glomerular filtration rate trajectory and all-cause mortality in type 2 diabetes: the Fremantle Diabetes Study." European Journal of Endocrinology 175, no. 4 (October 2016): 273–85. http://dx.doi.org/10.1530/eje-16-0327.
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Objective To investigate the association between estimated GFR (eGFR) and all-cause mortality, including the contribution of temporal eGFR changes, in well-characterised community-based patients with type 2 diabetes. Design Longitudinal observational study. Methods Participants from the Fremantle Diabetes Study Phase 1 were assessed between 1993 and 1996 and followed until end-December 2012. Cox proportional hazards modelling was used to assess the relationship between baseline eGFR category (Stage 1–5) and all-cause death, and between eGFR trajectories assigned by semiparametric group-based modelling (GBM) and all-cause death in patients with five post-baseline annual eGFR measurements. Results In the full cohort (1296 patients; mean±s.d. age 64.1±11.3years, 48.6% males), 738 (56.9%) died during 12.9±6.1years of follow-up. There was a U-shaped relationship between all-cause death and eGFR category. With Stage 3 (45–59mL/min/1.73m2) as reference, the strongest association was for eGFR ≥90mL/min/1.73m2 (hazard ratio (95% CI) 2.01 (1.52–2.66); P<0.001). GBM identified four linear trajectories (‘low’, ‘medium’, ‘high’, ‘high/declining’) in 532 patients with serial eGFR measurements. With medium trajectory as reference, eGFR trajectory displaced baseline eGFR category as an independent predictor of death, with low and high/declining trajectories associated with more than double the risk (2.03 (1.30–3.18) and 2.24 (1.31–3.83) respectively, P≤0.003) and associated median reductions in survival of 6.5 and 8.7years respectively. Conclusion There is a nonlinear relationship between eGFR and death in type 2 diabetes, which is at least partially explained by a sub-group of patients with an initially high but then rapidly declining eGFR.
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Raghu, Ganesh, Brett Ley, Kevin K. Brown, Vincent Cottin, Kevin F. Gibson, Robert J. Kaner, David J. Lederer, et al. "Risk factors for disease progression in idiopathic pulmonary fibrosis." Thorax 75, no. 1 (October 14, 2019): 78–80. http://dx.doi.org/10.1136/thoraxjnl-2019-213620.
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In this retrospective study of a randomised trial of simtuzumab in idiopathic pulmonary fibrosis (IPF), prodromal decline in forced vital capacity (FVC) was significantly associated with increased risk of mortality, respiratory and all-cause hospitalisations, and categorical disease progression. Predictive modelling of progression-free survival event risk was used to assess the effect of population enrichment for patients at risk of rapid progression of IPF; C-index values were 0.64 (death), 0.69 (disease progression), and 0.72 (adjudicated respiratory hospitalisation) and 0.76 (all-cause hospitalisation). Predictive modelling may be a useful tool for improving efficiency of clinical trials with categorical end points.
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Richards, S. J. "Selected Issues in Modelling Mortality by Cause and in Small Populations." British Actuarial Journal 15, S1 (2009): 267–83. http://dx.doi.org/10.1017/s1357321700005602.
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ABSTRACTActuarial practice as regards mortality analysis and projection is changing rapidly. This paper provides a short introduction to some of the limitations and risks in using trends in cause of death as a means for projecting future mortality rates. It also covers recent developments in analysing the mortality of smaller populations, including survival models and “piggyback” models.
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Martin, A., and C. Martin. "Modelling The Likely Impact of The Obesity Epidemic on Mortality And Cause of Death In Older Adults." Value in Health 18, no. 7 (November 2015): A663. http://dx.doi.org/10.1016/j.jval.2015.09.2409.
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Chen, Guang-Xiang, and David E. Fosbroke. "Work-Related Fatal-Injury Risk of Construction Workers by Occupation and Cause of Death." Human and Ecological Risk Assessment: An International Journal 4, no. 6 (December 1998): 1371–90. http://dx.doi.org/10.1080/10807039891284721.
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Kunihama, Tsuyoshi, Zehang Richard Li, Samuel J. Clark, and Tyler H. McCormick. "Bayesian factor models for probabilistic cause of death assessment with verbal autopsies." Annals of Applied Statistics 14, no. 1 (March 2020): 241–56. http://dx.doi.org/10.1214/19-aoas1253.
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Mohammed, Ali Ahmed, Kamarudin Ambak, Ahmed Mancy Mosa, and Deprizon Syamsunur. "A Review of the Traffic Accidents and Related Practices Worldwide." Open Transportation Journal 13, no. 1 (June 30, 2019): 65–83. http://dx.doi.org/10.2174/1874447801913010065.
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A traffic accident, a traffic collision or crash occurs when a vehicle collides with another vehicle, pedestrian, animal, road barriers, or any stationary obstruction such as a tree or a utility pole. Traffic collisions may result in injury, death, vehicle damage and possession damage. Motor vehicle collisions cause death and disability as well as a financial burden. Traffic accidents cause many losses especially of human life, property damages, and loss of resources. Indeed, even in strife influenced countries such as Afghanistan, Libya, Pakistan, and Yemen, road traffic remains the most common cause of fatal injuries, causing between two and eight times more fatalities than war and lawful mediation. The World Health Organization (WHO) 2013 assessed the traffic casualty rate in the Eastern Mediterranean Region (EMR) to be the second most elevated rate universally after the African Region and extending a few other countries in the region. The aim of this paper was to enrich the global highway safety knowledge by revealing the catastrophic impact of traffic accidents on the economy of the societies and the safety of the common worldwide.
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Ren, Ziyang, Lirong Nie, Yushan Du, and Jufen Liu. "Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults: a competing risk analysis." General Psychiatry 37, no. 2 (April 2024): e101156. http://dx.doi.org/10.1136/gpsych-2023-101156.
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BackgroundDepressive symptoms and cognitive impairment often interact, rendering their associations controversial. To date, their joint trajectories and associations with dementia and death remain underexplored.AimsTo explore the interactions between depressive symptoms and cognitive function, their developmental trajectories and the associations with all-cause dementia, Alzheimer’s disease (AD) and all-cause death in older adults.MethodsData were from the Health and Retirement Study. Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status, respectively. All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses. All-cause death was determined by interviews. The restricted cubic spline, group-based trajectory modelling and subdistribution hazard regression were used.ResultsSignificant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found, especially in women (p for interaction <0.05). Independent trajectory analysis showed that emerging or high (vs no) depressive trajectories and poor or rapidly decreased cognitive trajectories (vs very good) from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia, AD and all-cause death. 15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined, where rapidly decreased cognitive function was more common in those with no depressive symptoms. Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function, those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death, with subdistribution hazard ratios (95% confidence intervals) of 4.47 (2.99 to 6.67) and 1.84 (1.43 to 2.36), especially in women.ConclusionsTo effectively mitigate the risk of dementia and death, it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms, particularly in women.
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Booth, H., and L. Tickle. "Mortality Modelling and Forecasting: a Review of Methods." Annals of Actuarial Science 3, no. 1-2 (September 2008): 3–43. http://dx.doi.org/10.1017/s1748499500000440.
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ABSTRACTContinuing increases in life expectancy beyond previously-held limits have brought to the fore the critical importance of mortality forecasting. Significant developments in mortality forecasting since 1980 are reviewed under three broad approaches: expectation, extrapolation and explanation. Expectation is not generally a good basis for mortality forecasting, as it is subjective; expert expectations are invariably conservative. Explanation is restricted to certain causes of death with known determinants. Decomposition by cause of death poses problems associated with the lack of independence among causes and data difficulties. Most developments have been in extrapolative forecasting, and make use of statistical methods rather than models developed primarily for age-specific graduation. Methods using two-factor models (age-period or age-cohort) have been most successful. The two-factor Lee–Carter method, and, in particular, its variants, have been successful in terms of accuracy, while recent advances have improved the estimation of forecast uncertainty. Regression-based (GLM) methods have been less successful, due to nonlinearities in time. Three-factor methods are more recent; the Lee–Carter age-period-cohort model appears promising. Specialised software has been developed and made available. Research needs include further comparative evaluations of methods in terms of the accuracy of the point forecast and its uncertainty, encompassing a wide range of mortality situations.
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Machavaram, Vidya Bhargavi, and Sireesha Veeramachaneni. "Age Dependent Analysis of Colon Cancer Tumours Using Mathematical and Statistical Modelling." International Journal of Mathematical, Engineering and Management Sciences 6, no. 3 (June 1, 2021): 944–60. http://dx.doi.org/10.33889/ijmems.2021.6.3.056.
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Colon cancer is the third most commonly diagnosed cancer and the second leading cause of cancer death in men and women combined in the United States. In this work, we performed mathematical and statistical modelling of Tumour sizes as a function of age for four different races. Mathematically, based on the behaviour of the data for each race, we partitioned ages of subjects into several intervals. The mathematical function that characterizes the size of the Tumour as a function of age was determined for each age interval. Statistically, using quantile regression, we designed models that are more robust at specific quantiles using Tumour size and age as dependent and predictor variables.
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Davies, Callum, Amy E. Morgan, and Mark T. Mc Auley. "Computationally Modelling Cholesterol Metabolism and Atherosclerosis." Biology 12, no. 8 (August 14, 2023): 1133. http://dx.doi.org/10.3390/biology12081133.
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Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.
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Falissard, Louis, Claire Morgand, Sylvie Roussel, Claire Imbaud, Walid Ghosn, Karim Bounebache, and Grégoire Rey. "A Deep Artificial Neural Network−Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation." JMIR Medical Informatics 8, no. 4 (April 28, 2020): e17125. http://dx.doi.org/10.2196/17125.
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Background Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d’épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. Objective This article investigates the application of deep neural network methods to coding underlying causes of death. Methods The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject’s age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject’s underlying cause of death was then formulated as a predictive modelling problem. A deep neural network−based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach’s superiority was assessed via bootstrap. Results The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. Conclusions This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.
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Razali, K., J. Amin, GJ Dore, MG Law, and HCV Projections Working Group. "Modelling and calibration of the hepatitis C epidemic in Australia." Statistical Methods in Medical Research 18, no. 3 (November 26, 2008): 253–70. http://dx.doi.org/10.1177/0962280208094689.
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Hepatitis C virus (HCV) infection in Australia is predominantly transmitted through injecting drug use. A reduction in the heroin supply in Australia in late 2000 and early 2001 may have impacted the number of injecting drug users (IDUs) and the number of new hepatitis C infections. This paper updates estimates of HCV incidence between 1960 and 2005 and models long-term sequelae from infection. Outcomes among those with HCV were also recently assessed in a linkage study assessing cancer and causes of death following HCV diagnosis in New South Wales. Linkage study outcomes have been used here to calibrate modelled outcomes. Mathematical models were used to estimate HCV incidence among IDUs, migrants to Australia from high HCV-prevalence countries, and other HCV exposure groups. Recent trends in numbers of IDUs were based on indicators of injecting drug use. A natural history of HCV model was applied to estimate the prevalence of HCV in the population. Model predicted endpoints that were calibrated against the NSW linkage data over the period 1995—2002 were: (i) incident hepatocellular carcinoma (HCC); (ii) opioid overdose deaths; (iii) liver-related deaths; and (iv) all-cause mortality. Modelled estimates and the linkage data show reasonably good calibration for HCC cases and all-cause mortality. The estimated HCC incidence was increased from 70 cases in 1995 to 100 cases in 2002. All-cause mortality estimated at 1000 in 1995 increased to 1600 in 2002. Comparison of annual opioid deaths shows some agreement. However, the models underestimate the rate of increase observed between 1995 and 1999 and do not entirely capture the rapid decrease in overdose deaths from 2000 onwards. The linkage data showed a peak of overdose deaths at 430 in 1999 compared to 320 estimated by the models. Comparison of observed liver deaths with the modelled numbers showed poor agreement. A good agreement would require an increase in liver deaths from the assumed 2 to 5% per annum following cirrhosis in the models. Mathematical models suggest that HCV incidence decreased from a peak of 14,000 infections in 1999 to 9700 infections in 2005, largely attributable to a reduction in injecting drug use. The poor agreement between projected and linked liver deaths could reflect differing coding of causes of deaths, underestimates of the numbers of people with cirrhosis following HCV, or underestimates of rates of liver death following cirrhosis. The reasonably good agreement between most of the modelled estimates with observed linkage data provides some support for the assumptions used in the models.
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Hassouna, Fady M. A., and Ian Pringle. "Analysis and Prediction of Crash Fatalities in Australia." Open Transportation Journal 13, no. 1 (September 26, 2019): 134–40. http://dx.doi.org/10.2174/1874447801913010134.
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Introduction: As fatalities, injuries, and economic losses from road accidents are a major concern for governments and their citizens, Australia, like other countries, has designed and implemented a wide range of strategies to reduce the rate of road accidents. Methods: As part of the strategy design process, data on crash deaths were collected and then analyzed to develop more effective strategies. The data of crash deaths in Australia during the years 1965 to 2018 were analyzed based on gender, causes of crash deaths, and type of road users, and then the results were compared with global averages, then a prediction model was developed to forecast the future annual crash fatalities. Results: The results indicate that, based on gender, the rate of male road fatalities in Australia was significantly higher than that of female road fatalities. Whereas based on the cause of death, the first cause of death was over speeding. Based on the type of road users, the drivers and passengers of 4-wheel vehicles had the highest rate of fatalities. Conclusion: The prediction model was developed based on Autoregressive Integrated Moving Average (ARIMA) methodology, and annual road fatalities in Australia for the next five years 2019-2022 have been forecast using this model.
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Dembek, Z. F., T. Chekol, and A. Wu. "Best practice assessment of disease modelling for infectious disease outbreaks." Epidemiology and Infection 146, no. 10 (May 8, 2018): 1207–15. http://dx.doi.org/10.1017/s095026881800119x.
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AbstractDuring emerging disease outbreaks, public health, emergency management officials and decision-makers increasingly rely on epidemiological models to forecast outbreak progression and determine the best response to health crisis needs. Outbreak response strategies derived from such modelling may include pharmaceutical distribution, immunisation campaigns, social distancing, prophylactic pharmaceuticals, medical care, bed surge, security and other requirements. Infectious disease modelling estimates are unavoidably subject to multiple interpretations, and full understanding of a model's limitations may be lost when provided from the disease modeller to public health practitioner to government policymaker. We review epidemiological models created for diseases which are of greatest concern for public health protection. Such diseases, whether transmitted from person-to-person (Ebola, influenza, smallpox), via direct exposure (anthrax), or food and waterborne exposure (cholera, typhoid) may cause severe illness and death in a large population. We examine disease-specific models to determine best practices characterising infectious disease outbreaks and facilitating emergency response and implementation of public health policy and disease control measures.
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Yi, Jie, Fang-Bao Tian, Anne Simmons, and Tracie Barber. "Impact of Modelling Surface Roughness in an Arterial Stenosis." Fluids 7, no. 5 (May 21, 2022): 179. http://dx.doi.org/10.3390/fluids7050179.
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Arterial stenosis is a problem of immediate significance, as cardiovascular disease is the number one leading cause of death worldwide. Generally, the study of stenotic flow assumes a smooth, curved stenosis and artery. However, the real situation is unlikely to present an infinitely smooth-surfaced arterial stenosis. Here, the impact of surface roughness on the flow in an arterial stenosis was studied via a computational fluid dynamics analysis. A patient-specific geometry with a smooth surface was reconstructed, and a partially rough model was built by artificially adding random roughness only on the stenotic region of the smooth model. It was found that the flow was oscillatory downstream of the stenosis in the models. A slightly lower velocity near the wall and more oscillatory flows were observed due to the presence of the roughness in the stenotic region. However, the pressure distributions did not vary significantly between the smooth and rough models. The differences in the wall shear metrics were slight in the stenotic region and became larger in the downstream region of the models.
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TAO, YOUSHAN, and QIAN GUO. "A FREE BOUNDARY PROBLEM MODELLING CANCER RADIOVIROTHERAPY." Mathematical Models and Methods in Applied Sciences 17, no. 08 (August 2007): 1241–59. http://dx.doi.org/10.1142/s0218202507002261.
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This paper deals with a procedure for cancer radiovirotherapy which requires not only injection of replication-competent viruses but also administration of radioiodide. The viruses infect tumor cells, replicate inside them and eventually cause their death. As infected cells die, the viruses inside them are released and then proceed to infect adjacent tumor cells. Radioiodide is in a continuous state of flux between the tumor and the remaining part. Iodide undergoes beta particle decay and the emitted beta particles have a significant effect on tumor cells. The combination of virotherapy with radiotherapy has recently been shown to be significantly more effective than treatment with virotherapy alone. Cancer radiovirotherapy can be described by a free boundary problem for a nonlinear system of partial differential equations, where the free boundary is the surface of a tumor. Global existence and uniqueness of solutions to this free boundary problem is proved, and a new explicit parameter condition corresponding to the success of therapy is also found. Furthermore, numerical simulations are given to show that there is an optimal timing for radio-iodine administration, and that there is an optimal dose for the radioactive iodide.
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Goggs, Robert, Sage De Rosa, and Daniel J. Fletcher. "Multivariable analysis of the association between electrolyte disturbances and mortality in cats." Journal of Feline Medicine and Surgery 20, no. 12 (December 5, 2017): 1072–81. http://dx.doi.org/10.1177/1098612x17743564.
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Objectives Electrolyte disorders have been individually associated with mortality in small populations of cats with specific conditions, but the associations and interactions between electrolyte disturbances and outcome have not been evaluated in a large, heterogeneous population. It was hypothesized that abnormalities of sodium, chloride, potassium and calcium concentrations would be independently and proportionately associated with death from natural causes and with all-cause mortality in cats. Methods An electronic database containing 7064 electrolyte profiles was constructed to assess the association between disorders of sodium, potassium, corrected-chloride and ionized calcium concentrations with non-survival by multivariable modelling. A second database containing 2388 records was used to validate the models constructed from the first database. Results All four electrolytes assessed had non-linear U-shaped associations with case fatality rates, wherein concentrations clustered around the reference interval had the lowest case fatality rates, while progressively abnormal concentrations were associated with proportionately increased risk of non-survival (area under the receiver operator characteristic curve [AUROC] 0.689) or death (AUROC 0.750). Conclusions and relevance Multivariable modelling suggested that these electrolyte disturbances were associated with non-survival and with death from natural causes independent of each other. The present study suggests that measurement of electrolyte concentrations is an important component of the assessment of cats in emergency rooms or intensive care units. Future studies should focus on confirming these associations in a prospective manner accounting for disease severity.
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Jalajakumari, Suji, Swathi Devaraj, Thangatamilan Manivel, and Sureshkumar Ramasamy. "Analysis of Cell Proliferation and Apoptosis in Virtual Model." Proceedings of the Bulgarian Academy of Sciences 75, no. 10 (October 30, 2022): 1483–90. http://dx.doi.org/10.7546/crabs.2022.10.11.
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Breast cancer is the second leading cause of cancer among women globally. Several treatments are involved in breast cancer like surgery, chemotherapy, radiotherapy, and hormone therapy; chemotherapy being used most often. Multicellular systems complications can be deeply understood by analyzing and studying how cells grow, move, divide, die and interact. To examine these factors, we use PhysiCell as our modelling platform. Virtual cell growth analysis is essential to view the cancer cell growth daily. PhysiCell physics-based multicellular simulator is an open-source agent-based simulator used to design a virtual model to analyze the changing cell cycle progression, volume, death, motility, mechanics and processes. Analysis was made on the cancer cell death rate, cell damage rate, and cell repair rate. Data were taken for every 6 hours of simulation and the result confirms that the chemotherapeutic agent kills 45% of cancer cells.
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Uhlig, Stefan, and Wolfgang M. Kuebler. "Difficulties in modelling ARDS (2017 Grover Conference Series)." Pulmonary Circulation 8, no. 2 (March 9, 2018): 204589401876673. http://dx.doi.org/10.1177/2045894018766737.
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Fifty years after the first description of acute respiratory distress syndrome (ARDS), none of the many positive drug studies in animal models have been confirmed in clinical trials and translated into clinical practice. This bleak outcome of so many animal experiments shows how difficult it is to model ARDS. Lungs from patients are characterized by hyperinflammation, permeability edema, and hypoxemia; accordingly, this is what most models aim to reproduce. However, in animal models it is very easy to cause inflammation in the lungs, but difficult to cause hypoxemia. Often – and not unlike in patients – models with hypoxemia are accompanied by cardiovascular failure that necessitates fluid support and ventilation, raising the question as to the role of intensive care measures in models of ARDS. In our opinion, there are two major arguments in favor of modelling intensive care medicine in models of ARDS: (1) preventing death from shock; and (2) modelling ventilation and other ICU measures as a second hit. The preferable predictive endpoints in any model of ARDS remain unclear. At present, the best recommendation is to use endpoints that can be compared across studies (i.e. PaO2/FiO2 ratio, compliance, wet-to-dry weight ratio) rather than percentage data. Another important and often overlooked issue is the fact that the thermoneutral environmental temperatures for mice and rats are 30℃ and 28℃, respectively; thus, at room temperature (20–22℃) they suffer from cold stress with the associated significant metabolic changes. While, by definition, any model is an abstraction, we suggest that clinically relevant models of ARDS will have to closer recapitulate important properties of the disease while taking into account species-specific confounders.
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Mardinoglu, Adil, and P. J. Cregg. "Modelling the Effect of SPION Size in a Stent Assisted Magnetic Drug Targeting System with Interparticle Interactions." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/618658.
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Cancer is a leading cause of death worldwide and it is caused by the interaction of genomic, environmental, and lifestyle factors. Although chemotherapy is one way of treating cancers, it also damages healthy cells and may cause severe side effects. Therefore, it is beneficial in drug delivery in the human body to increase the proportion of the drugs at the target site while limiting its exposure at the rest of body through Magnetic Drug Targeting (MDT). Superparamagnetic iron oxide nanoparticles (SPIONs) are derived from polyol methods and coated with oleic acid and can be used as magnetic drug carrier particles (MDCPs) in an MDT system. Here, we develop a mathematical model for studying the interactions between the MDCPs enriched with three different diameters of SPIONs (6.6, 11.6, and 17.8 nm) in the MDT system with an implanted magnetizable stent using different magnetic field strengths and blood velocities. Our computational analysis allows for the optimal design of the SPIONs enriched MDCPs to be used in clinical applications.
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Herrero, Alba, Elisabeth Knetemann, and Inge Mannaerts. "Review: Challenges of In Vitro CAF Modelling in Liver Cancers." Cancers 13, no. 23 (November 24, 2021): 5914. http://dx.doi.org/10.3390/cancers13235914.
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Primary and secondary liver cancer are the third cause of death in the world, and as the incidence is increasing, liver cancer represents a global health burden. Current treatment strategies are insufficient to permanently cure patients from this devastating disease, and therefore other approaches are under investigation. The importance of cancer-associated fibroblasts (CAFs) in the tumour microenvironment is evident, and many pre-clinical studies have shown increased tumour aggressiveness in the presence of CAFs. However, it remains unclear how hepatic stellate cells are triggered by the tumour to become CAFs and how the recently described CAF subtypes originate and orchestrate pro-tumoural effects. Specialized in vitro systems will be needed to address these questions. In this review, we present the currently used in vitro models to study CAFs in primary and secondary liver cancer and highlight the trend from using oversimplified 2D culture systems to more complex 3D models. Relatively few studies report on the impact of cancer (sub)types on CAFs and the tumour microenvironment, and most studies investigated the impact of secreted factors due to the nature of the models.
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Cunningham, Taylor C., Khadijah Maghrabi, and Shubhayan Sanatani. "Morbidities in the ultra-athlete and marathoner." Cardiology in the Young 27, S1 (January 2017): S94—S100. http://dx.doi.org/10.1017/s1047951116002304.
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AbstractThe cardiovascular benefits of habitual exercise are well documented. In the current era, more of the population is exceeding the recommendations for physical activity as the popularity of endurance events increases. Recent data have proposed a U-shaped relationship between exercise intensity and cardiovascular outcomes. Regular participation in endurance activities has been shown to result in structural and functional changes in the heart. This re-modelling may be the substrate for cardiac dysfunction or arrhythmias. The risk of sudden cardiac death may also be elevated; however, in most cases of sudden cardiac death, the cause can be linked to an underlying cardiac pathology where exercise acted as the trigger for a lethal arrhythmia. This article serves to review whether excessive exercise may result in harm in some athletes.
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Kleynhans, Jackie, Stefano Tempia, Kayoko Shioda, Anne von Gottberg, Daniel M. Weinberger, and Cheryl Cohen. "Estimated impact of the pneumococcal conjugate vaccine on pneumonia mortality in South Africa, 1999 through 2016: An ecological modelling study." PLOS Medicine 18, no. 2 (February 16, 2021): e1003537. http://dx.doi.org/10.1371/journal.pmed.1003537.
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Background Data on the national-level impact of pneumococcal conjugate vaccine (PCV) introduction on mortality are lacking from Africa. PCV was introduced in South Africa in 2009. We estimated the impact of PCV introduction on all-cause pneumonia mortality in South Africa, while controlling for changes in mortality due to other interventions. Methods and findings We used national death registration data in South Africa from 1999 to 2016 to assess the impact of PCV introduction on all-cause pneumonia mortality in all ages, with the exclusion of infants aged <1 month. We created a composite (synthetic) control using Bayesian variable selection of nondiarrheal, nonpneumonia, and nonpneumococcal deaths to estimate the number of expected all-cause pneumonia deaths in the absence of PCV introduction post 2009. We compared all-cause pneumonia deaths from the death registry to the expected deaths in 2012 to 2016. We also estimated the number of prevented deaths during 2009 to 2016. Of the 9,324,638 deaths reported in South Africa from 1999 to 2016, 12·6% were pneumonia-related. Compared to number of deaths expected, we estimated a 33% (95% credible interval (CrI) 26% to 43%), 23% (95%CrI 17% to 29%), 25% (95%CrI 19% to 32%), and 23% (95%CrI 11% to 32%) reduction in pneumonia mortality in children aged 1 to 11 months, 1 to 4 years, 5 to 7 years, and 8 to 18 years in 2012 to 2016, respectively. In total, an estimated 18,422 (95%CrI 12,388 to 26,978) pneumonia-related deaths were prevented from 2009 to 2016 in children aged <19 years. No declines were estimated observed among adults following PCV introduction. This study was mainly limited by coding errors in original data that could have led to a lower impact estimate, and unmeasured factors could also have confounded estimates. Conclusions This study found that the introduction of PCV was associated with substantial reduction in all-cause pneumonia deaths in children aged 1 month to <19 years. The model predicted an effect of PCV in age groups who were eligible for vaccination (1 months to 4 years), and an indirect effect in those too old (8 to 18 years) to be vaccinated. These findings support sustaining pneumococcal vaccination to reduce pneumonia-related mortality in children.
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NEWALL, A. T., C. VIBOUD, and J. G. WOOD. "Influenza-attributable mortality in Australians aged more than 50 years: a comparison of different modelling approaches." Epidemiology and Infection 138, no. 6 (November 27, 2009): 836–42. http://dx.doi.org/10.1017/s095026880999118x.
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SUMMARYThis study aimed to compare systematically approaches to estimating influenza-attributable mortality in older Australians. Using monthly age-specific death data together with viral surveillance counts for influenza and respiratory syncytial virus, we explored two of the most frequently used methods of estimating excess influenza-attributable disease: Poisson and Serfling regression models. These approaches produced consistent age and temporal patterns in estimates of influenza-attributable mortality in older Australians but some variation in the magnitude of the disease burden. Of Australians aged >50 years, average annual estimated influenza-attributable deaths (all cause) ranged from 2314 to 3457 for the Serfling and Poisson regression models, respectively. The excess influenza-attributable disease burden was substantial under all approaches.
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Purnama, Ujang, Marcos Castro-Guarda, Om Saswat Sahoo, and Carolyn A. Carr. "Modelling Diabetic Cardiomyopathy: Using Human Stem Cell-Derived Cardiomyocytes to Complement Animal Models." Metabolites 12, no. 9 (September 3, 2022): 832. http://dx.doi.org/10.3390/metabo12090832.
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Diabetes is a global epidemic, with cardiovascular disease being the leading cause of death in diabetic patients. There is a pressing need for an in vitro model to aid understanding of the mechanisms driving diabetic heart disease, and to provide an accurate, reliable tool for drug testing. Human induced-pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have potential as a disease modelling tool. There are several factors that drive molecular changes inside cardiomyocytes contributing to diabetic cardiomyopathy, including hyperglycaemia, lipotoxicity and hyperinsulinemia. Here we discuss these factors and how they can be seen in animal models and utilised in cell culture to mimic the diabetic heart. The use of human iPSC-CMs will allow for a greater understanding of disease pathogenesis and open up new avenues for drug testing.
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Kitajima, Hiroyuki, and Toru Yazawa. "Bifurcation Analysis on a Generation of Early Afterdepolarization in a Mathematical Cardiac Model." International Journal of Bifurcation and Chaos 31, no. 12 (September 25, 2021): 2150179. http://dx.doi.org/10.1142/s0218127421501790.
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Electrical activity occurs in the cell membrane of cardiomyocytes. This electrical activity forms the action potential that generates pumping of the heart. An abnormality in the action potential turns into arrhythmia, which may cause sudden death. Studies of arrhythmias using mathematical models are important to reduce the risk of sudden death. In this study, we investigate bifurcations related to the generation of early afterdepolarizations (EADs) in a mathematical model. We clarify the transition process from a normal state to a persistent EAD through a transient EAD while changing only one parameter (multiple of conductance of L-type calcium channel current) value. The dependence of the transient EAD generation on parameters is shown through bifurcation analysis in a [Na]i-parameterized system.
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Cortigiani, Lauro, Clara Carpeggiani, Rosa Sicari, Claudio Michelassi, Francesco Bovenzi, and Eugenio Picano. "Simple six-item clinical score improves risk prediction capability of stress echocardiography." Heart 104, no. 9 (October 14, 2017): 760–66. http://dx.doi.org/10.1136/heartjnl-2017-312122.
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ObjectivesTo assess the value of a simple score integrating non-ischaemia-related variables in expanding the wall motion abnormalities risk power during stress echocardiography (SE).MethodsStudy includes 14 279 patients who underwent SE for evaluation of coronary artery disease. All-cause death was the end point. Patients were randomly divided into the modelling and validation group of equal size. In the modelling group, multivariate analysis was conducted using clinical, rest and SE data, and a score was obtained from the number of non-ischaemia-related independent prognostic predictors. The score prognostic capability was compared in both groups.ResultsDuring a median follow-up of 31 months, 1230 patients died: 622 (9%) in the modelling and 608 (9%) in the validation group (p=0.68). Independent predictors of mortality were ischaemia at SE (HR 1.77, 95% CI 1.49 to 2.12; p<0.0001) and six other parameters: age>65 years, wall motion at rest, diabetes, left bundle branch block, anti-ischaemic therapy and male sex. Risk score resulted prognostically effective in the modelling and validation groups, both with and without inducible ischaemia subset. When risk score was included in the multivariate analysis, besides ischaemia at SE it was the only independent predictor of mortality in the modelling (HR 1.70, 95% CI 1.60 to 1.82; p<0.0001), in the validation (HR 1.77, 95% CI 1.65 to 1.90; p<0.0001) and in the overall group (HR 1.73, 95% CI 1.66 to 1.82; p<0.0001).ConclusionsSimple clinical variables may be able to optimise SE risk stratification.
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Videm, Vibeke, Marthe Halsan Liff, and Mari Hoff. "Relative importance of inflammation and cardiorespiratory fitness for all-cause mortality risk in persons with rheumatoid arthritis: the population-based Trøndelag Health Study." RMD Open 9, no. 3 (August 2023): e003194. http://dx.doi.org/10.1136/rmdopen-2023-003194.
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ObjectiveInflammation and reduced cardiorespiratory fitness (CRF) are associated with increased mortality rates in rheumatoid arthritis (RA). We aimed at directly comparing the relative importance of inflammation and reduced CRF as mediators of all-cause mortality in persons with RA compared with controls, quantifying direct and indirect (mediated) effects.MethodsPersons with (n=223, cases) and without (n=31 684, controls) RA from the third survey of the Trøndelag Health Study (HUNT3, 2006–2008) were included. Inflammation was quantified using C reactive protein (CRP) and estimated CRF (eCRF) was calculated using published formulae. All-cause mortality was found by linkage to the Norwegian Cause of Death Registry, with follow-up from inclusion in HUNT3 until death or 31 December 2018. Data were analysed using standardised equation modelling, permitting complex correlations among variables.ResultsPersons with RA had increased all-cause mortality rates (24.1% vs 9.9%, p<0.001). Both eCRF (p<0.001) and CRP ≥3 mg/L (p<0.001) were mediators of this excess mortality, rendering the direct effect of RA non-significant (p=0.19). The indirect effect of RA mediated by eCRF (standardised coefficient 0.006) was approximately three times higher than the indirect effect mediated by CRP (standardised coefficient 0.002) in a model adjusted for other mortality risk factors.ConclusionEven with CRP concentrations <3 mg/L in all patients with RA, excess mortality mediated by low CRF would still play an important role. Improved inflammation control in RA does not necessarily lead to better CRF. Therefore, our study strongly supports recommendations for development and implementation of exercise programmes aimed at improving CRF in persons with RA.
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Xiao, Lin, Fahui Wu, Dingcheng Yang, Tiankui Zhang, and Xiaoya Zhu. "Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3831810.
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The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy efficiency of the whole network. Optimal number of multiple sink nodes of the WSN topology is proposed for optimizing energy efficiency. Then, the hierarchical clustering analysis is applied to implement this clustering of the sensor nodes and pick up the sink nodes from the sensor nodes as the clustering head. Meanwhile, the update method is proposed to determine the sink node when the death of certain sink node happened which can cause the paralysis of network. Simulation results verify the energy efficiency of the proposed model and validate the updating of the sink nodes to ensure the normal operation of the WSN.
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Vegesna, Dr Vinod. "AI-Driven Predictive Modelling for Cardiovascular Disease Risk Assessment." International Journal of Innovative Research in Advanced Engineering 11, no. 08 (August 15, 2024): 760–65. http://dx.doi.org/10.26562/ijirae.2024.v1108.01.
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Advanced predictive modelling is necessary for early identification and risk assessment of cardiovascular disease (CVD), as it continues to be a significant cause of death globally. This study compares established algorithms with novel ideas to investigate AI-driven predictive modelling tools for estimating the risk of cardiovascular disease. Support vector machines (SVM), random forests, and logistic regression are examples of machine learning methods used in the current models. Although these have demonstrated effectiveness in managing organised clinical data, they frequently struggle to include diverse data sources such genetic, lifestyle, and imaging data.To improve prediction accuracyand we present an approach that introduces deep learning models: recurrent neural networks (RNN) for time-series data and convolutional neural networks (CNN) for picture analysis. In order to take use of the advantages of both paradigms, we also investigate hybrid models that integrate neural networks with gradient boosting machines (GBM). The outcomes show that, in important categories like accuracy, sensitivity, and specificity, our suggested models perform better than conventional algorithms. Additional strengthening of the model resilience is achieved by integrating generative adversarial networks (GAN) for data augmentation. Clinical decision-making is improved by the comparative analysis, which shows a notable decrease in false positives and negatives.
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Fine, Nowell M., Sunil V. Kalmady, Weijie Sun, Russ Greiner, Jonathan G. Howlett, James A. White, Finlay A. McAlister, Justin A. Ezekowitz, and Padma Kaul. "Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data." PLOS Digital Health 3, no. 10 (October 25, 2024): e0000636. http://dx.doi.org/10.1371/journal.pdig.0000636.
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Aims Patients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system. Methods and results Patients visiting the ED or hospitalized with HF between 2002–2016 in Alberta, Canada were included. Outcomes of interest were 30-day and 1-year HF-related ED visits, HF hospital readmission or all-cause mortality. We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. The area under receiver operating characteristic curve (AUC-ROC) was used to assess model performance. We included 50,630 patients with 93,552 HF ED visits/hospitalizations. At 30-day follow-up in the holdout validation cohort, the AUC-ROC for the combined endpoint of HF ED visit, HF hospital readmission or death for the Catboost and logistic regression models was 74.16 (73.18–75.11) versus 62.25 (61.25–63.18), respectively. At 1-year follow-up corresponding values were 76.80 (76.1–77.47) versus 69.52 (68.77–70.26), respectively. AUC-ROC values for the endpoint of all-cause death alone at 30-days and 1-year follow-up were 83.21 (81.83–84.41) versus 69.53 (67.98–71.18), and 85.73 (85.14–86.29) versus 69.40 (68.57–70.26), for the CatBoost and logistic regression models, respectively. Conclusions ML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.
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Berehan, Haymanot Berelie, Zelalem G. Dessie, and Lijalem Melie Tesfaw. "Exploring functional abilities and competing risks among stroke patients: a longitudinal and survival analysis study at Felege Hiwot Referral Hospital, Ethiopia." BMJ Open 14, no. 5 (May 2024): e073384. http://dx.doi.org/10.1136/bmjopen-2023-073384.
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ObjectivesThis study aimed to evaluate competing risks and functional ability measures among patients who had a stroke.DesignA joint model comprising two related submodels was applied: a cause-specific hazard submodel for competing drop-out and stroke-related death risks, and a partial proportional odd submodel for longitudinal functional ability.SettingFelege Hiwot Referral Hospital, Ethiopia.ParticipantsThe study included 400 patients who had a stroke from the medical ward outpatient stroke unit at Felege Hiwot Referral Hospital, who were treated from September 2018 to August 2021.ResultsAmong the 400 patients who had a stroke, 146 (36.5%) died and 88 (22%) dropped out. At baseline, 14% of patients had no symptoms and/or disability while 24% had slight disability, and 25% had severe disability. Most patients (37.04%) exhibited moderate functional ability. The presence of diabetes increased the cause-specific hazard of death by 3.95 times (95% CI 2.16 to 7.24) but decreased the cause-specific hazard of drop-out by 95% (aHR 0.05; 95% CI 0.01 to 0.46) compared with non-diabetic patients who had a stroke.ConclusionA substantial proportion of patients who had a stroke experienced mortality and drop-out during the study period, highlighting the importance of considering competing risks in stroke research. Age, diabetes, white cell count and stroke complications were significant covariates affecting both longitudinal and survival submodels. Compared with stand-alone models, the joint competing risk modelling technique offers comprehensive insights into the disease’s transition pattern.
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Henshall, D. C. "Apoptosis signalling pathways in seizure-induced neuronal death and epilepsy." Biochemical Society Transactions 35, no. 2 (March 20, 2007): 421–23. http://dx.doi.org/10.1042/bst0350421.
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Delineating the molecular pathways underlying seizure-induced neuronal death may yield novel strategies for brain protection against prolonged or repetitive seizures. Glutamate-mediated excitotoxicity and necrosis is a primary contributing mechanism but seizures also activate programmed (apoptotic) cell death pathways. Apoptosis signalling pathways are typically initiated following perturbation of intracellular organelle function (intrinsic pathway) or by activated cell-surface-expressed death receptors (extrinsic pathway), with signalling cascades orchestrated in part by the Bcl-2 and caspase gene families. In this review, evidence for these pathways from experimental seizure modelling and clinical material from patients with intractable temporal lobe epilepsy is examined. Seizures cause mitochondrial dysfunction and activate intrinsic pathway components including pro-apoptotic Bcl-2 family proteins and caspases, processes that may be partly calcium-induced. The ER (endoplasmic reticulum) has emerged as a major intrinsic pathway trigger for apoptosis and its function may also be compromised following seizures and in epilepsy. The extrinsic, death-receptor-dependent pathway is also rapidly engaged following experimental seizures and in patient brain, supporting a previously unexpected apical role for a calcium-independent pathway. When considered alongside emerging functions of apoptosis-regulatory proteins in non-cell-death processes, including regulating intracellular calcium release and neuronal (re)structuring, apoptosis signalling pathways can be viewed as an important developing focus of research into how to obviate the deleterious impact of seizures on the brain.
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Vihinen, Mauno. "Poikilosis – pervasive biological variation." F1000Research 9 (June 12, 2020): 602. http://dx.doi.org/10.12688/f1000research.24173.1.
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Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.
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Vihinen, Mauno. "Poikilosis – pervasive biological variation." F1000Research 9 (September 18, 2020): 602. http://dx.doi.org/10.12688/f1000research.24173.2.
Full textAbstract:
Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.
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TAO, YOUSHAN, and HUI ZHANG. "A PARABOLIC–HYPERBOLIC FREE BOUNDARY PROBLEM MODELLING TUMOR TREATMENT WITH VIRUS." Mathematical Models and Methods in Applied Sciences 17, no. 01 (January 2007): 63–80. http://dx.doi.org/10.1142/s0218202507001838.
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We consider a procedure for cancer therapy which consists of injecting replication-competent viruses into the tumor. The viruses infect tumor cells, replicate inside them, and eventually cause their death. As infected cells die, the viruses inside them are released and then proceed to infect adjacent tumor cells. However, a major factor influencing the efficacy of virus agents is the immune response that may limit the replication and spread of the replication-competent virus. The competition between tumor cells, a replication-competent virus and an immune response is modelled as a free boundary problem for a nonlinear system of partial differential equations, where the free boundary is the surface of the tumor. In this model, the immune response equation is a semilinear parabolic equation, including a chemotaxis term which is used to describe the movement of the immune response induced by gradients of the infected cell density. Under the assumption that the chemotactic sensitivity coefficient is small compared with the diffusion coefficient of the immune response, we prove the global existence and uniqueness of the solution of this free boundary problem. For large chemotactic coefficient, the global existence is still open.
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Giorgi, Chiara, Giorgia Lombardozzi, Fabrizio Ammannito, Marta Sofia Scenna, Eleonora Maceroni, Massimiliano Quintiliani, Michele d’Angelo, Annamaria Cimini, and Vanessa Castelli. "Brain Organoids: A Game-Changer for Drug Testing." Pharmaceutics 16, no. 4 (March 22, 2024): 443. http://dx.doi.org/10.3390/pharmaceutics16040443.
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Neurological disorders are the second cause of death and the leading cause of disability worldwide. Unfortunately, no cure exists for these disorders, but the actual therapies are only able to ameliorate people’s quality of life. Thus, there is an urgent need to test potential therapeutic approaches. Brain organoids are a possible valuable tool in the study of the brain, due to their ability to reproduce different brain regions and maturation stages; they can be used also as a tool for disease modelling and target identification of neurological disorders. Recently, brain organoids have been used in drug-screening processes, even if there are several limitations to overcome. This review focuses on the description of brain organoid development and drug-screening processes, discussing the advantages, challenges, and limitations of the use of organoids in modeling neurological diseases. We also highlighted the potential of testing novel therapeutic approaches. Finally, we examine the challenges and future directions to improve the drug-screening process.
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Navabharath, Manthena, Varsha Srivastava, Saurabh Gupta, Shoor Vir Singh, and Sayeed Ahmad. "Ursolic Acid and Solasodine as Potent Anti-Mycobacterial Agents for Combating Paratuberculosis: An Anti-Inflammatory and In Silico Analysis." Molecules 28, no. 1 (December 29, 2022): 274. http://dx.doi.org/10.3390/molecules28010274.
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Mycobacterium avium subspecies paratuberculosis (MAP) infection in domestic livestock causes persistent diarrhea, weight loss, and death and is also a potential cause of Crohn’s disease (CD) in humans; notably, treatments against MAP are insufficient, costly, and can cause adverse reactions. Hence, plant-derived bioactive constituents have been taken into consideration in this regard. Herein, we present the results of two bioactive constituents (Solasodine and Ursolic acid) that were evaluated for their safety and efficacy against MAP protein (Dephospho-Coenzyme A kinase (DPCK) by utilizing in vitro assays and different tools of in silico biology. The ADME/t-test, the drug-likeness property test, pharmacophore modelling, and PASS prediction have proven that both the constituents have better binding capacities than the available antibiotic drugs used to target protein inhibition pathways. Through our observations, it can be inferred that these two phytochemicals can be adequately used to treat paratuberculosis, thereby combating inflammatory bowel disorders (IBD) of an autoimmune nature.
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Davis, Wendy A., Eunice Chin, Adelle Jee, Jen Martins, David G. Bruce, John Beilby, and Timothy M. E. Davis. "Apolipoprotein E genotype and mortality in Southern European and Anglo-Celt patients with type 2 diabetes: the Fremantle Diabetes Study." European Journal of Endocrinology 163, no. 4 (October 2010): 559–64. http://dx.doi.org/10.1530/eje-10-0474.
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ObjectiveTo determine whether cardiac and all-cause mortality are lower in Southern European (SE) patients than in Anglo-Celt (AC) patients with type 2 diabetes in an urban Australian setting, and, if so, whether ethnicity-specific differences in apolipoprotein E (APOE) genotypes are contributory.DesignLongitudinal observational cohort study.MethodsWe analysed detailed data from 1057 patients from the community-based Fremantle Diabetes Study, 238 were of an SE migrant background and 819 of AC ethnicity. Cox proportional hazards modelling was used to identify independent predictors of cardiac and all-cause mortality.ResultsDuring 9.8±3.5 years of follow-up, 411 (38.9%) participants died, 161 (39.2%) from cardiac causes. Significant positive baseline independent predictors of cardiac death were age, male gender, coronary heart disease, cerebrovascular disease, peripheral arterial disease, retinopathy and peripheral neuropathy (P≤0.004), while maternal history of diabetes was protective (P=0.014). After adjusting for these variables,APOE4carriage was predictive (hazard ratio (95% confidence interval) 1.61 (1.01–2.58);P=0.048). SE ethnicity did not add significantly to the model either as a single variable or as an interaction term withAPOE4carriage (P≥0.86). Significant independent predictors of all-cause mortality were age, male gender, smoking, coronary heart disease, cerebrovascular disease, peripheral arterial disease, retinopathy, peripheral neuropathy and microalbuminuria (P≤0.047), while overweight/obesity, lipid-lowering therapy and recent exercise were protective (P≤0.008).APOE4carriage, SE ethnicity and their interaction did not add to the model (P≥0.32).ConclusionsSE ethnicity does not confer an independent survival advantage in community-based Australian type 2 diabetic patients, butAPOE4carriers are at higher risk of cardiac death.
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Tourajizadeh, Hami, Zakie Farbodi, Danial Kiaei, and Oveas Gholami. "Numerical modelling and control of tumour mutation using piecewise fuzzy approach." Latin American Applied Research - An international journal 55, no. 1 (January 9, 2025): 123–35. https://doi.org/10.52292/j.laar.2025.3476.
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Mutation dynamics of a cancer tumour is modelled here using numerical non-model-based approach of fuzzy and its metastasis is controlled using a closed loop controller. Cancer is the second cause of death in the world. Metastasis of the cancerous cells is the main source of its fatality and this phenomenon is extremely uncontrollable as a result of drug resistance. Drug resistance itself is the consequence of the mutation of the cancerous stem cells. Thus modelling the metastasis without considering the effect of mutation is not practically efficient. Since, the exact analytic modelling of mutation is usually impossible, here a numeric approach is proposed toward modelling the cancer mutation using the fuzzy method. As a result the number of the cancer cells and their related mutations are fuzzificated as the engaged states and their related performance subject to chemotherapy is predicted using the proposed fuzzy model. In order to increase the accuracy of the numerical modelling, the fuzzification process is modified using piecewise algorithm. Employing the mentioned numerical plant, it is possible to design and implement a closed loop controller using the feedback of the tumour, and consequently improve the schedule of chemotherapy in a way that the mutation and tumour growth would be blocked. State Vector Feedback Control (SVFC) is employed here to stabilize the mutation trigger and cancer development. Previous proposed analytic model of the studied cancer is employed here to provide the required data of fuzzy modelling instead of laboratory data and it is shown by the aid of MATLAB simulation that, using the proposed fuzzy model of cancer and mutation, the behaviour of the tumour can be predicted and its mutation and metastasis can be controlled successfully.
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Psilopatis, Iason, Alexandros G. Sykaras, Georgios Mandrakis, Kleio Vrettou, and Stamatios Theocharis. "Patient-Derived Organoids: The Beginning of a New Era in Ovarian Cancer Disease Modeling and Drug Sensitivity Testing." Biomedicines 11, no. 1 (December 20, 2022): 1. http://dx.doi.org/10.3390/biomedicines11010001.
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Ovarian cancer (OC) is the leading cause of death from gynecological malignancies. Despite great advances in treatment strategies, therapeutic resistance and the gap between preclinical data and actual clinical efficacy justify the necessity of developing novel models for investigating OC. Organoids represent revolutionary three-dimensional cell culture models, deriving from stem cells and reflecting the primary tissue’s biology and pathology. The aim of the current review is to study the current status of mouse- and patient-derived organoids, as well as their potential to model carcinogenesis and perform drug screenings for OC. Herein, we describe the role of organoids in the assessment of high-grade serous OC (HGSOC) cells-of-origin, illustrate their use as promising preclinical OC models and highlight the advantages of organoid technology in terms of disease modelling and drug sensitivity testing.
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Muhammad Ammar Shafi, Nur Azia Hazida Mohamad Azmi, Mohd Saifullah Rusiman, Aliya Syaffa Zakaria, Mohd Zarir Yusoff, Hafizah Zulkipli, and Nor Kamariah Kamaruddin. "Predictive Analytics of Comparison Fuzzy Modelling Towards Symptoms of Colorectal Cancer in Malaysia." Journal of Advanced Research in Applied Sciences and Engineering Technology 47, no. 2 (June 28, 2024): 213–22. http://dx.doi.org/10.37934/araset.47.2.213222.
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Colorectal cancer (CRC) is a cancer that begins in the colon and rectum of the human body and is the third leading cause of death among cancer patients. Colorectal cancer develops when cells in the body begin to proliferate uncontrollably with such symptoms. However, the high-risk symptoms of CRC in Malaysia are still ambiguous and unclear. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory’s concept can deal with data not to a precise point value (uncertainty data), this study uses comparison fuzzy modelling as predictive analytics to predict the high-risk symptoms that contribute to the development of colorectal cancer in Malaysia. Secondary data of 180 colorectal cancer patients who received treatment in a general hospital with seventeen independent variables with different combinations of variable types were considered. Other than that, the parameter, error, and explanation for the model were included using two measurement statistical errors. Fuzzy linear regression with symmetric parameters found ovarian symptom is the high-risk symptom to develop colorectal cancer. The weightage value is 25.73, with the results of the least value of the mean square error (MSE) value being 98.212 and the root mean square error (RMSE) value being 9.910.
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Khatkova, S. E., and A. S. Gilweg. "Modelling long-term outcomes and mortality risk in patients with post-stroke spasticity during abobotulinum toxin injections as part of rehabilitation." Neurology, Neuropsychiatry, Psychosomatics 16, no. 2 (April 24, 2024): 60–68. http://dx.doi.org/10.14412/2074-2711-2024-2-60-68.
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To date, there are no Russian or international studies investigating the long-term efficacy of botulinum therapy (abobotulinum toxin injections – AboBTX) during rehabilitation in patients with post-stroke spasticity in terms of its impact on the risk of death from all causes and the development of cardiovascular complications. A 10-year study conducted in the United Kingdom analyzed the effects of AboBTX injections in patients with post-stroke spasticity on long-term clinical (all-cause mortality, cardiovascular events, length and quality of life) and economic (direct medical costs) outcomes. For the first time ever, a model was developed to evaluate the clinical (quality-adjusted life expectancy) and economic benefits of AboBTX injections in the structure of rehabilitation compared to rehabilitation without botulinum therapy in patients with post-stroke spasticity. It was shown that the inclusion of regular injections of AboBTX in the rehabilitation process resulted in an 8.8% reduction in the risk of death from all causes, a 13% increase in life expectancy (and by 59% in quality-adjusted terms compared to rehabilitation without botulinum therapy) and proved to be cost-effective. The use of AboBTX injections in patients with post-stroke spasticity as part of rehabilitation is cost-effective in the long term and improves long-term outcomes, including post-stroke survival.
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Khan, Sarah, Quamrul Hassan, Kaushal Kumar, Saurav Dixit, Kshama Sharma, Vivek Kumar C., Navdeep Dhaliwal, and Bhukya Madhu. "Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach." E3S Web of Conferences 430 (2023): 01176. http://dx.doi.org/10.1051/e3sconf/202343001176.
Full textAbstract:
Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.
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Gowran, Aoife, Maura Brioschi, Davide Rovina, Mattia Chiesa, Luca Piacentini, Sara Mallia, Cristina Banfi, Giulio Pompilio, and Rosaria Santoro. "Multiomic Approaches to Uncover the Complexities of Dystrophin-Associated Cardiomyopathy." International Journal of Molecular Sciences 22, no. 16 (August 19, 2021): 8954. http://dx.doi.org/10.3390/ijms22168954.
Full textAbstract:
Despite major progress in treating skeletal muscle disease associated with dystrophinopathies, cardiomyopathy is emerging as a major cause of death in people carrying dystrophin gene mutations that remain without a targeted cure even with new treatment directions and advances in modelling abilities. The reasons for the stunted progress in ameliorating dystrophin-associated cardiomyopathy (DAC) can be explained by the difficulties in detecting pathophysiological mechanisms which can also be efficiently targeted within the heart in the widest patient population. New perspectives are clearly required to effectively address the unanswered questions concerning the identification of authentic and effectual readouts of DAC occurrence and severity. A potential way forward to achieve further therapy breakthroughs lies in combining multiomic analysis with advanced preclinical precision models. This review presents the fundamental discoveries made using relevant models of DAC and how omics approaches have been incorporated to date.
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Gibson, Savannah, James Harrison, and Jonathan Cox. "Modelling a Silent Epidemic: A Review of the In Vitro Models of Latent Tuberculosis." Pathogens 7, no. 4 (November 15, 2018): 88. http://dx.doi.org/10.3390/pathogens7040088.
Full textAbstract:
Tuberculosis (TB) is the primary cause of death by a single infectious agent; responsible for around two million deaths in 2016. A major virulence factor of TB is the ability to enter a latent or Non-Replicating Persistent (NRP) state which is presumed untreatable. Approximately 1.7 billion people are latently infected with TB and on reactivation many of these infections are drug resistant. As the current treatment is ineffective and diagnosis remains poor, millions of people have the potential to reactivate into active TB disease. The immune system seeks to control the TB infection by containing the bacteria in a granuloma, where it is exposed to stressful anaerobic and nutrient deprived conditions. It is thought to be these environmental conditions that trigger the NRP state. A number of in vitro models have been developed that mimic conditions within the granuloma to a lesser or greater extent. These different models have all been utilised for the research of different characteristics of NRP Mycobacterium tuberculosis, however their disparity in approach and physiological relevance often results in inconsistencies and a lack of consensus between studies. This review provides a summation of the different NRP models and a critical analysis of their respective advantages and disadvantages relating to their physiological relevance.