Готові списки джерел за темами / PROBAST

Добірка наукової літератури з теми "PROBAST"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "PROBAST"

1

Collins, Gary S., Paula Dhiman, Constanza L. Andaur Navarro, Jie Ma, Lotty Hooft, Johannes B. Reitsma, Patricia Logullo, et al. "Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence." BMJ Open 11, no. 7 (July 2021): e048008. http://dx.doi.org/10.1136/bmjopen-2020-048008.

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IntroductionThe Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques.Methods and analysisTRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation.Ethics and disseminationEthical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications.PROSPERO registration numberCRD42019140361 and CRD42019161764.
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2

Kaiser, Isabelle, Sonja Mathes, Annette B. Pfahlberg, Wolfgang Uter, Carola Berking, Markus V. Heppt, Theresa Steeb, Katharina Diehl, and Olaf Gefeller. "Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies." Cancers 14, no. 12 (June 20, 2022): 3033. http://dx.doi.org/10.3390/cancers14123033.

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Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.
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3

Zheng, Yadi, Jiang Li, Zheng Wu, He Li, Maomao Cao, Ni Li, and Jie He. "Risk prediction models for breast cancer: a systematic review." BMJ Open 12, no. 7 (July 2022): e055398. http://dx.doi.org/10.1136/bmjopen-2021-055398.

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ObjectivesTo systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesPubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteriaWe included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. Search was limited to English language only.Data extraction and synthesisTwo reviewers independently screened, reviewed, extracted and assessed studies with discrepancies resolved through discussion or a third reviewer. Risk of bias was assessed according to the PROBAST framework.Results63 894 studies were screened and 40 studies with 47 risk prediction models were included in the review. Most of the studies used logistic regression to develop breast cancer risk prediction models for Caucasian women by case–control data. The most widely used risk factor was reproductive factors and the highest area under the curve was 0.943 (95% CI 0.919 to 0.967). All the models included in the review had high risk of bias.ConclusionsNo risk prediction models for breast cancer were recommended for different ethnic groups and models incorporating mammographic density or single-nucleotide polymorphisms among Asian women are few and poorly needed. High-quality breast cancer risk prediction models assessed by PROBAST should be developed and validated, especially among Asian women.PROSPERO registration numberCRD42020202570.
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4

Du, M., D. Haag, Y. Song, J. Lynch, and M. Mittinty. "Examining Bias and Reporting in Oral Health Prediction Modeling Studies." Journal of Dental Research 99, no. 4 (February 6, 2020): 374–87. http://dx.doi.org/10.1177/0022034520903725.

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Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.
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5

Geng, Chanyu, Liming Huang, Yi Li, Amanda Ying Wang, Guisen Li, and Yunlin Feng. "Prediction Models of Primary Membranous Nephropathy: A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 12, no. 2 (January 10, 2023): 559. http://dx.doi.org/10.3390/jcm12020559.

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Background: Several statistical models for predicting prognosis of primary membranous nephropathy (PMN) have been proposed, most of which have not been as widely accepted in clinical practice. Methods: A systematic search was performed in MEDLINE and EMBASE. English studies that developed any prediction models including two or more than two predictive variables were eligible for inclusion. The study population was limited to adult patients with pathologically confirmed PMN. The outcomes in eligible studies should be events relevant to prognosis of PMN, either disease progression or response profile after treatments. The risk of bias was assessed according to the PROBAST. Results: In all, eight studies with 1237 patients were included. The pooled AUC value of the seven studies with renal function deterioration and/or ESRD as the predicted outcomes was 0.88 (95% CI: 0.85 to 0.90; I2 = 77%, p = 0.006). The paired forest plots for sensitivity and specificity with corresponding 95% CIs for each of these seven studies indicated the combined sensitivity and specificity were 0.76 (95% CI: 0.64 to 0.85) and 0.84 (95% CI: 0.80 to 0.88), respectively. All seven studies included in the meta-analysis were assessed as high risk of bias according to the PROBAST tool. Conclusions: The reported discrimination ability of included models was good; however, the insufficient calibration assessment and lack of validation studies precluded drawing a definitive conclusion on the performance of these prediction models. High-grade evidence from well-designed studies is needed in this field.
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6

Wolff, Robert F., Karel G. M. Moons, Richard D. Riley, Penny F. Whiting, Marie Westwood, Gary S. Collins, Johannes B. Reitsma, Jos Kleijnen, and Sue Mallett. "PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies." Annals of Internal Medicine 170, no. 1 (January 1, 2019): 51. http://dx.doi.org/10.7326/m18-1376.

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7

Crowson, Matthew G., Dana Moukheiber, Aldo Robles Arévalo, Barbara D. Lam, Sreekar Mantena, Aakanksha Rana, Deborah Goss, David W. Bates, and Leo Anthony Celi. "A systematic review of federated learning applications for biomedical data." PLOS Digital Health 1, no. 5 (May 19, 2022): e0000033. http://dx.doi.org/10.1371/journal.pdig.0000033.

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Objectives Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. Methods We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. Results 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Conclusion Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.
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Ogero, Morris, Rachel Jelagat Sarguta, Lucas Malla, Jalemba Aluvaala, Ambrose Agweyu, Mike English, Nelson Owuor Onyango, and Samuel Akech. "Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review." BMJ Open 10, no. 10 (October 2020): e035045. http://dx.doi.org/10.1136/bmjopen-2019-035045.

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ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included.ResultsOur search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias.ConclusionThis review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores.PROSPERO registration numberCRD42018088599.
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9

Monahan, Ann Corneille, and Sue S. Feldman. "Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS." JMIR Medical Informatics 9, no. 9 (September 16, 2021): e30022. http://dx.doi.org/10.2196/30022.

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Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.
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Petre, Maria-Alexandra, Bibek Saha, Shugo Kasuya, Marina Englesakis, Nan Gai, Arie Peliowski, and Kazuyoshi Aoyama. "Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review." BMJ Open 11, no. 1 (January 2021): e043968. http://dx.doi.org/10.1136/bmjopen-2020-043968.

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ObjectivesEmergence delirium (ED) occurs in approximately 25% of paediatric general anaesthetics and has significant adverse effects. The goal of the current systematic review was to identify the existing literature investigating performance of predictive models for the development of paediatric ED following general anaesthesia and to determine their usability.DesignSystematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sourcesMedline (Ovid), PubMed, Embase (Ovid), Cochrane Database of Systematic Reviews (Ovid), Cochrane CENTRAL (Ovid), PsycINFO (Ovid), Scopus (Elsevier) and Web of Science (Clarivate Analytics), ClinicalTrials.gov, International Clinical Trials Registry Platform and ProQuest Digital Dissertations and Theses International through 17 November 2020.Eligibility criteria for selecting studiesAll randomised controlled trials and cohort studies investigating predictive models for the development of ED in children undergoing general anaesthesia.Data extraction and synthesisFollowing title, abstract and full-text screening by two reviewers, data were extracted from all eligible studies, including demographic parameters, details of anaesthetics and performance characteristics of the predictive scores for ED. Evidence quality and predictive score usability were assessed according to the PROBAST framework.ResultsThe current systematic review yielded 9242 abstracts, of which only one study detailing the development and validation of the Emergence Agitation Risk Scale (EARS) met the inclusion criteria. EARS had good discrimination with c-index of 0.81 (95% CI 0.72 to 0.89). Calibration showed a non-significant Homer-Lemeshow goodness-of-fit test (p=0.97). Although the EARS demonstrated low concern of applicability, the high risk of bias compromised the overall usability of this model.ConclusionsThe current systematic review concluded that EARS has good discrimination performance but low usability to predict ED in a paediatric population. Further research is warranted to develop novel models for the prediction of ED in paediatric anaesthesia.PROSPERO registration numberCRD42019141950.
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Дисертації з теми "PROBAST"

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Zhu, Liyu. "Discrete Brand Choice Models: Analysis and Applications." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-07102007-142035/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008.
Esogbue, Augustine, Committee Chair ; Griffin, Paul, Committee Member ; Lu, Jye-Chyi (JC), Committee Member ; Li, MinQiang, Committee Member ; McCarthy, Patrick, Committee Member.
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2

Probst, Melanie [Verfasser]. "Rechtsfragen des regulären Börsenrückzugs / Melanie Probst." Baden-Baden : Nomos Verlagsgesellschaft mbH & Co. KG, 2013. http://d-nb.info/1110055099/34.

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3

Ziegler, Andreas. "Simuliertes klassisches Schätzen und Testen in Mehrperioden-Mehralternativen-Probitmodellen /." [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9030594.

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Probst, Christopher [Verfasser]. "Microfluidic tools for single cell analysis / Christopher Probst." Siegen : Universitätsbibliothek der Universität Siegen, 2017. http://d-nb.info/1127335928/34.

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Faria, Frederico da Costa Marques. "Infiltra??o policial : perspectiva processual e probat?ria." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2015. http://tede2.pucrs.br/tede2/handle/tede/6441.

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Submitted by Setor de Tratamento da Informa??o - BC/PUCRS (tede2@pucrs.br) on 2015-12-22T20:54:00Z No. of bitstreams: 1 476840 - Texto Parcial.pdf: 110948 bytes, checksum: ba798afb4fed4d7dc71db5f0212d4a06 (MD5)
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This study seeks to analyze an investigative tool used in several countries and recently regulated in Brazil. The analysis begins with the description of the phenomenon of organized crime and its change throughout history, as well as the evolution of investigative techniques especially the infiltration of agents. Then we draw the legislative path that finally led to Law 12.850/13, that currently rules this institute. We then proceeded to examine each of the diploma elements to form the basis for the core of the work that are the procedural and evidentiary aspects of the tool. In the last chapter, we analyze the adequacy of the investigative technique to the principles of criminal procedure and those inherent to the police infiltration itself.
O presente trabalho busca analisar uma ferramenta investigativa conhecida como infiltra??o policial, usada em diversos pa?ses e recentemente regulamentada no Brasil. A an?lise tem in?cio com a descri??o do fen?meno da criminalidade organizada e sua mudan?a ao longo da hist?ria, bem como das evolu??es das t?cnicas investigativas, especialmente a infiltra??o de agentes. Tra?amos, ent?o, a caminhada legislativa at? chegarmos ? lei 12.850/13, atual regulamento deste instituto. Passamos a analisar cada um dos elementos do diploma para formar a base para o n?cleo do trabalho, que s?o os aspectos processuais e probat?rios da ferramenta. No ?ltimo cap?tulo analisamos a adequa??o da t?cnica investigativa aos princ?pios do processo penal e aqueles inerentes ? pr?pria infiltra??o policial e, por fim, o seu valor probat?rio.
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6

Sumun, Faizal. "Chemiluminescent gene probes." Thesis, University of Sussex, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.352945.

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Niblock, Trevor. "Micro scanning probes." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395357.

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Pál, Robert. "Ratiometric luminescent probes." Thesis, Durham University, 2007. http://etheses.dur.ac.uk/3658/.

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A ratiometric, luminescent Eu probe was sought for use in measuring intracellular pH, that can be excited in the range 355-405 nm. In order to achieve this, a series of 1- azathiaxanthone based chromophores has been synthesised and studied to find the best candidate to promote Eu emission. A series of 'D02A' derivatives was synthesised incorporating both the chosen chromophore (2-methyl-l-azathioxanthone) and a pH- dependent binding moiety (a tethered sulfonamide). Measurements were carried out in order to study and understand the properties of the complexes, especially the nature of the pH dependence. The protonation constants of the pendant pH 'switch' in each system were determined by luminescence titration. The influence on Eu emission of some endogenous anions and protein has also been examined by luminescence spectroscopy, along with calculations of their apparent binding constants in order to find the best candidate for measuring intracellular pH. A novel bicarbonate sensor has also been synthesised and studied incorporating a 7-(methylcarbamoylmethyl)-azathioxanthone sensitiser moiety. The luminescence properties have been thoroughly studied, and the changes in the photophysical properties and the sensitivity towards anion and protein binding rationalised. The complex displayed sensitivity towards endogenous anions and protein binding, however, a suitable calibration curve was obtained in the desired 5 - 30 mM HCO(_3) range in simulated endogenous anion mixture, using intensity ratio vs. pH plots. A series of ratiometric Eu(111)complexes has been synthesised incorporating an efficient sensitiser for measuring citrate concentrations in seminal and prostate fluid samples. Their luminescent properties were thoroughly studied using simulated prostate fluid as the background to find the best candidate for prostate adenocarcinoma detection. Preliminary studies have been undertaken to determine citrate levels in 'clinical' prostate and seminal fluid samples. Cellular uptake and localisation studies have also been undertaken with each complex revealing the complex uptake profile and the time-dependent localisation behaviour within the cell. Each complex showed no significant evidence for toxicity. Images were observed using unfixed cells, appropriate for live cell imaging applications.
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9

Probst, Axel [Verfasser]. "Reynoldsspannungsmodellierung für das Überziehen in der Flugzeugaerodynamik / Axel Probst." Aachen : Shaker, 2013. http://d-nb.info/1050345843/34.

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Briggs, Amanda C. "Probit and ordered probit analysis of the demand for fresh sweet corn." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001185.

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Книги з теми "PROBAST"

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Karen, Irvine, Bate David 1956-, and Meinhardt Johannes, eds. Barbara Probst: Exposures. Göttingen: Steidl, 2007.

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2

Pulkrabek, Julie L. Probate. 2nd ed. [St. Paul, Minn.]: Thomson/West, 2004.

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Pulkrabek, Julie L. Probate. 2nd ed. [St. Paul, Minn.]: Thomson/West, 2003.

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4

Folsom, Ralph Haughwout. Probate litigation. Rochester, N.Y: Lawyers Cooperative Pub., 1992.

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5

Jasper, Margaret C. Probate law. Dobbs Ferry, N.Y: Oceana Publications, 1997.

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6

New York State Archives and Records Administration. Probate records. Albany, NY (Cultural Education Center, Rm. 11D40, 12230): New York State Archives, 1993.

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7

Secor, Albert W. Tennessee probate. [St. Paul, MN]: Thomson/West, 2002.

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8

Jasper, Margaret C. Probate law. Dobbs Ferry, N.Y: Oceana Publications, 1997.

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9

Durkin, Rosemary D. Ohio probate. 2nd ed. [Rochester, N.Y.]: Lawyers Cooperative Pub., 1996.

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10

Wee, Nicole. Probate handbook. Petaling Jaya, Selangor Darul Ehsan: LexisNexis, 2007.

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Частини книг з теми "PROBAST"

1

Lowder, Lanet L., and Marian J. Ster. "Probate." In Encyclopedia of Women’s Health, 1081–83. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-0-306-48113-0_361.

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2

Gooch, Jan W. "Probit." In Encyclopedic Dictionary of Polymers, 590. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9468.

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3

Bowler, Nicola. "Probes." In Eddy-Current Nondestructive Evaluation, 141–66. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9629-2_8.

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4

Uhl, George, S. Watson, J. Kelsey, Michel Goedert, and W. Scott Young. "Probes." In In Situ Hybridization in Brain, 227–38. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4615-9486-4_14.

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5

Mensching, Guido, and Eva-Maria Remberger. "Probes." In Linguistik Aktuell/Linguistics Today, 173–201. Amsterdam: John Benjamins Publishing Company, 2006. http://dx.doi.org/10.1075/la.86.09men.

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6

Rendell, Catherine. "Probate Jurisdiction." In Law of Succession, 162–68. London: Macmillan Education UK, 1997. http://dx.doi.org/10.1007/978-1-349-13510-3_8.

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7

Cecere, Julie T. "Probang Apparatus." In Equine Reproductive Procedures, 501–3. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118904398.ch155.

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8

Cai, Kai-Yuan. "Fuzzy Methods in Probist Systems." In The Kluwer International Series in Engineering and Computer Science, 71–85. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1403-5_3.

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9

Kong, Deming. "Oligonucleotide Probes." In Advanced Topics in Science and Technology in China, 483–500. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34303-2_13.

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10

Tserruya, Itzhak. "Electromagnetic Probes." In Relativistic Heavy Ion Physics, 176–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01539-7_7.

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Тези доповідей конференцій з теми "PROBAST"

1

Wu, Wentao, Hongsong Li, Haixun Wang, and Kenny Q. Zhu. "Probase." In the 2012 international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2213836.2213891.

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2

Oh, Soon Y., Gustavo Marfia, and Mario Gerla. "ProbeCast." In the 4th ACM symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454586.1454599.

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3

Hutchinson, Hilary, Heiko Hansen, Nicolas Roussel, Björn Eiderbäck, Wendy Mackay, Bo Westerlund, Benjamin B. Bederson, et al. "Technology probes." In the conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/642611.642616.

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4

Vyas, Dhaval, Anton Eliëns, Marek R. van de Watering, and Gerrit C. van der Veer. "Organizational probes." In the 15th European conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1473018.1473062.

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5

Ladur, A. A., N. D. Maljutin, and E. I. Fedotov. "Microwave probes." In 2010 20th International Crimean Conference "Microwave & Telecommunication Technology" (CriMiCo 2010). IEEE, 2010. http://dx.doi.org/10.1109/crmico.2010.5630862.

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6

DiSalvo, Betsy, and Parisa Khanipour Roshan. "Medium probes." In DIS '14: Designing Interactive Systems Conference 2014. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598510.2598580.

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7

Paulos, Eric, and Tom Jenkins. "Urban probes." In the SIGCHI conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1054972.1055020.

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8

Hulkko, Sami, Tuuli Mattelm�ki, Katja Virtanen, and Turkka Keinonen. "Mobile probes." In the third Nordic conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1028014.1028020.

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9

Luusua, Anna, Johanna Ylipulli, Marko Jurmu, Henrika Pihlajaniemi, Piia Markkanen, and Timo Ojala. "Evaluation Probes." In CHI '15: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2702123.2702466.

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10

Kristav, Per. "Cultural probes." In Nordes 2005: In the Making. Nordes, 2005. http://dx.doi.org/10.21606/nordes.2005.010.

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Звіти організацій з теми "PROBAST"

1

Mullahy, John. Estimation of Multivariate Probit Models via Bivariate Probit. Cambridge, MA: National Bureau of Economic Research, September 2015. http://dx.doi.org/10.3386/w21593.

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2

Primas, Lori Ellen, Gregg Kent Sullivan, and Mark Manley Pickrell. Optical Probes for Hydrotests. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392832.

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3

Yarotski, Dmitry Anatolievitch. Ultrafast Probes for Dirac Materials. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1172825.

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4

Fryer, Christopher Lee, Stefano Gandolfi, Przemyslaw R. Wozniak, Joseph Allen Carlson, Aaron Joseph Couture, Joshua C. Dolence, Wesley Paul Even, et al. Nucleosynthesis Probes of Cosmic Explosions. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1603951.

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5

Hirata, Christopher M. Cosmological Probes of Fundamental Physics. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1573766.

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6

Vineyard, Michael F. Nucleon Structure Studies with Electromagnetic Probes. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1010447.

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7

Olsen, Eric V., Iryna B. Sorokulova, I.-Hsuan Chen, Ben Fiebor, and James M. Barbaree. Landscape Phage Probes for Salmonella Typhimurium. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada426603.

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8

Deutsch. Optical Probes for Laser Induced Shocks. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada256092.

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9

Wang, Lei. Molecular Probes for Pancreatic Cancer Imaging. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3105.

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10

Iandolo, John J., and Stephen K. Chapes. Anti-Idiotype Probes for Toxin Detection. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada242099.

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