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Results for “Bayesian modelling”

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At least 55 records · Page 3Linked to original sources
PubMed2026

Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype‑linked clinical severity.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), caused by Dabie bandavirus (SFTSV), exhibits geographically decoupled incidence and fatality patterns across East Asia. We aimed to elucidate the distinct ecological drivers and phylogeographic dynamics underlying this inland-coastal epidemiological divergence. METHODS: Integrating 1820 high-quality global genomes of SFTSV with well-characterized clinical cohorts (936 patients) and nationwide surveillance data (27,457 cases) from China, we constructed a comprehensive analytical framework. Ecological modeling, Bayesian phylogeography, and genotype-phenotype association analyses were employed to trace the evolutionary trajectories and clinical implications of the virus. RESULTS: A pronounced "inland-high-incidence vs. coastal-high-fatality" pattern of SFTS was identified. The incidence of SFTS exhibited divergent sensitivities to meteorological factors; inland transmission was sensitive to thermal fluctuations, whereas coastal dynamics were constrained by a sunshine threshold (>&#x2009;200&#xa0;h/month). In contrast, spatial divergence in clinical severity correlated with the distribution of regional viral genetic structures. Inland regions mainly co-circulated genotypes A, C, and D, while coastal regions were dominated by genotype B. Zhejiang province was identified as a genetic hub with significantly higher recombination frequencies than inland regions (11.0% vs. 3.5%, P < 0.001). Bayesian phylogeographic inference indicated frequent lineage exchange of Zhejiang province in China with the Republic of Korea and Japan. Clinically, genotypes B and D were associated with elevated mortality in coastal and inland regions, respectively, suggesting that the severe coastal phenotype is shaped by its genotype B-dominated structure. Additionally, the RdRp-N828S mutation emerged as a robust molecular correlate of fatal outcomes, warranting further functional validation. CONCLUSIONS: Divergent meteorological factors and plausible maritime transmission networks may underlie the geographically decoupled epidemiology of SFTS. These findings highlight that risk assessment must extend beyond incidence alone and provide a phylogeographically informed framework for targeted surveillance and genotype-specific interventions in high-risk hotspots.

Humans↗
PubMed1990

Bayesian forecasting of serum gentamicin concentrations in intensive care patients.

This study retrospectively evaluated the predictive performance of a 1-compartment Bayesian forecasting program in adult intensive care unit (ICU) patients with stable renal function. A comparison was made of the reliability of 3 sets of population-based parameter estimates and 2 serum concentration monitoring strategies. A larger mean error for prediction of peak gentamicin concentrations was seen with literature-derived parameters than when ICU population-based parameter estimates were used. Bias and precision improved when non-steady-state peak and trough concentrations were used to predict those at steady-state; the addition of steady-state values did not provide additional information for predictions once non-steady-state feedback concentrations were incorporated. The addition of 4 serial gentamicin concentrations obtained at both non-steady-state and steady-state did not noticeably improve the predictive performance. The results demonstrate that initial ICU pharmacokinetic parameter estimates for a 1-compartment Bayesian model provide accurate prediction of steady-state gentamicin concentrations. Prediction bias and precision showed the greatest improvement when non-steady-state gentamicin concentrations were used to determine individualised pharmacokinetic parameters.

Adult↗
PubMed2026

Shielding the First 24 Postnatal Months of Life: A Proposal for a Prospective Cohort Study of Early-Life Electromagnetic Exposure and Autism Risk.

BACKGROUND: Autism Spectrum Disorder (ASD) involves Mirror Neuron System (MNS) dysfunction, driving core social and imitative impairments. Systemic physiological alterations such as autonomic dysregulation, mitochondrial dysfunction and neuroinflammation are known to impair synchronization and plasticity of neuronal clusters. A less-evident environmental cofactor, coinciding with rising ASD prevalence, is the considerable world-wide increase in electromagnetic radiation (EMR) overall exposure among children. Experimental evidence shows how low-intensity EMR influences cellular processes, via voltage-gated calcium channels (VGCCs), oxidative stress, and mitochondrial metabolism. The Resonant Convergence framework, allow to predict how chronic EMR exposure during the first 24 postnatal months of life can act as a factor in ASD pathogenesis. The best candidate mechanism is chronic Ion Cyclotron Resonance (ICR) detuning the Ca2+-calmodulin pathway, thus disrupting MNS synchronization. METHODS AND ANALYSIS: A prospective observational pilot cohort study (24-month follow-up) proposes to enroll 1000 full-term newborns into two arms: an EMR-reduced cohort (n = 500, rest and sleep-phase Faraday shielding) and a standard exposure cohort (n = 500). Exposure is quantified via radiofrequency (RF)/extremely low frequency(ELF) measurements, proximity analysis, device inventories and wearable dosimetry. The primary endpoint is a continuous neurodevelopmental trajectory score (joint attention, language, electroencephalogram (EEG) mu-rhythm); binary ASD diagnosis (Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), Autism Diagnostic Interview-Revised (ADI-R)) is a secondary, exploratory endpoint. Moreover, an optional genomic screening will evaluate gene-environment interactions within extremely low-frequency electromagnetic field (ELF-EMF) vulnerable pathways, including ASD-associated genes upregulated by RF via bromodomain and extraterminal protein (BET)-mediated epigenetic mechanisms. Analyses will employ risk ratios, Fisher's exact tests and logistic regression adjusted for confounders; mixed-effects and Bayesian modeling will evaluate longitudinal outcomes and exposure reduction effects. Given a 2-3% baseline prevalence, approximately 20-30 ASD cases are expected. The study is therefore powered for exploratory signal detection rather than definitive causal inference, providing the critical baseline data required to justify and design future confirmatory trials. Sex-stratified modeling will address the 4:1 male-to-female prevalence ratio. ETHICS AND DISSEMINATION: Ethics committee approval is not yet sought; full protocol review and approval will be obtained prior to the study initiation, in strict accordance with the Declaration of Helsinki. Written parental informed consent will be mandatory for all participants prior to enrollment. Study findings and methodological milestones will be disseminated through peer-reviewed international scientific publications. This protocol provides a structured methodological framework for the first prospective investigation of sleep-phase EMR reduction as a potential modulator of ASD incidence during early neurodevelopment. Results will inform adequately powered confirmatory trials in electromagnetic neurodevelopmental epidemiology.

autism spectrum disorder↗
PubMed1986

A microcomputer program for critical evaluation of diagnostic tests.

We developed a microcomputer program that provides a Bayesian model of diagnostic performance and a simple decision tree model of clinical utility. We have used this program to review diagnostic performance and clinical utility for proposed new services at our 360-bed university hospital. We believe that significant benefits can be achieved if medical journals report complete data on test performance. First, this allows physicians to perform their own evaluations of diagnostic performance. Second, this allows physicians to evaluate clinical utility using either standard decision trees or decision trees that reflect specific clinical problems.

Bayes Theorem↗
PubMed1984

[Aid to diagnosis and decision making in an acute abdominal pain syndrome].

Diagnostic and decision making performances of a Bayesian model have been compared with clinically performances in abdominal pain of acute onset. Diagnostic accuracy of computer (63.3 p. cent) was lower than diagnostic accuracy of clinicians (72.6 p. cent). A two-fold increase in the number of diagnoses similarly decreased both performances. When the computer came to use national data base (6 916 patients) instead of local data base (571 patients), diagnostic accuracy of low-prevalence diseases increased and diagnostic accuracy of high-prevalence diseases decreased. Decision making accuracy of computer and clinicians were identical.

Abdomen↗
PubMed1991

A computational model of approximate Bayesian inference for associating clinical algorithms with decision analyses.

The lack of rationale or explanation is a major deficiency of clinical algorithms. To address this issue, the authors present a computational model for associating decision analyses with clinical algorithms. Automata theory is used to model categorical reasoning with approximate Bayesian inference based on probability intervals. This approximation reduces the number of computations to linear-order instead of the exponential-order combinations of clinical findings in exact Bayes. The linkage of decision analyses and clinical algorithms by means of this model exploits a new concept of "regular" clinical algorithms and their equivalency in theory and provides valuable perspectives in practice for developers of clinical algorithms.

Algorithms↗
PubMed1979

The Chatham Blood Pressure Study. An application of Bayesian growth curve models to a longitudinal study of blood pressure in children.

Recent developments in statistics have produced powerful methods that facilitate the analysis of longitudinal studies. These methods are illustrated by an analysis of a longitudinal study of blood pressure in children. The results of the study show a clear tendency for blood pressure to increase with age, and Asian children tend to have lower blood pressures than their Caucasian counterparts of the same age. There is evidence to support the hypothesis that blood pressures track.

Age Factors↗
PubMed1992

Suramin: rapid loading and weekly maintenance regimens for cancer patients.

PURPOSE: Suramin is an anticancer agent with a narrow therapeutic window and a terminal half-life of 45 to 55 days. These characteristics make it necessary to control accurately the serum concentrations of the drug. Therefore, the aim of the present study was to develop a rapid loading regimen, followed by weekly administration of suramin to maintain serum concentrations of between 150 and 300 micrograms/mL for 8 weeks. PATIENTS AND METHODS: Eligible patients were treated with five different loading regimens. Initially, weekly maintenance doses were estimated manually by the treating physician. Subsequently, computer-assisted dosing that used Bayesian pharmacokinetic modeling was used. RESULTS: Thirty-eight courses of suramin that were administered to 35 patients were studied. The optimal loading regimen consisted of a continuous infusion of 600 mg/m2 during a 24-hour period, which resulted in a mean serum concentration of 319 micrograms/mL. Potentially toxic concentrations that were observed with shorter infusions were avoided. Maintenance treatment, which used the weekly administration of suramin during a 6-hour period, seemed to be able to maintain mean suramin serum trough concentrations of 150 micrograms/mL, while preventing mean peak concentrations of more than 300 micrograms/mL. The use of Bayesian pharmacokinetics was superior to manual estimation in tailoring the optimal dose to the therapeutic window. CONCLUSIONS: Continuous infusion is the optimal way of delivering suramin during the loading phase. To maintain trough levels and peak levels within a narrower therapeutic window, suramin will have to be administered more frequently than once a week. Bayesian modeling based on individual serum levels and population pharmacokinetics allows accurate dosing to maintain suramin levels within the therapeutic window.

Adult↗
PubMed2026

Genome-wide association studies for feed efficiency, production and feeding behavior traits in Canadian purebred Duroc pigs.

This study aimed to identify potential genetic variants and candidate genes associated with feed efficiency (FE), production, and feeding behavior traits in Canadian purebred Duroc pigs. Genome-wide association studies (GWAS) were conducted using 8,861 individuals and an imputed Affymetrix PigGen Canada 50K panel v2.0 using a linear mixed model (LMM) and a Bayesian B model. This analysis used an adjusted P-value threshold (ranging from 6.6&#x202f;&#xd7;&#x202f;10-5 to 1.3&#x202f;&#xd7;&#x202f;10-4) using a false-discovery rate to determine significance. The number of significant SNPs identified for each trait was as follows: average daily gain (ADG, 48), daily feed intake (DFI, 85), feed conversion ratio (FCR, 101), residual feed intake (RFI, 37), residual gain (RG, 64), residual intake and gain (RIG, 55), backfat thickness (BF, 100), loin depth (LD, 6), Kleiber's ratio (KR, 0), total time spent eating per day (TPD, 7), and number of visits to the feeder per day (NVD, 6). Several traits (BF, DFI, FCR, RFI, RG, and RIG) showed strong overlapping signals on chromosomes 7 and 10 with 24 shared significant SNPs, indicating potential shared genetic mechanisms. These traits also had 71 overlapping candidate genes, such as PACSIN1, PTCH1, ADIPOR1, and ITPR3, associated with glucose, lipid, and cholesterol metabolism. Well-known candidate genes in literature associated with growth and fatness such as MC4R and CDH20 were also identified to be associated with ADG, BF, FCR, and DFI in this study. Gene ontology enrichment analysis revealed that a set of the candidate genes were involved in the gonadotropin-releasing hormone (GnRH) and the platelet-derived growth factor (PDGF) signaling pathways. Overall, this study contributed to understanding the genetic architecture and provided a biological foundation for improving FE, production, and feeding behavior traits in Canadian Duroc pigs, facilitating the selection of more efficient pigs.

Sus scrofa↗
PubMed1991

A Monte Carlo method for Bayesian inference in frailty models.

Many analyses in epidemiological and prognostic studies and in studies of event history data require methods that allow for unobserved covariates or "frailties." Clayton and Cuzick (1985, Journal of the Royal Statistical Society, Series A 148, 82-117) proposed a generalization of the proportional hazards model that implemented such random effects, but the proof of the asymptotic properties of the method remains elusive, and practical experience suggests that the likelihoods may be markedly nonquadratic. This paper sets out a Bayesian representation of the model in the spirit of Kalbfleisch (1978, Journal of the Royal Statistical Society, Series B 40, 214-221) and discusses inference using Monte Carlo methods.

Algorithms↗
PubMed1992

Advanced computer programs for drug dosing that combine pharmacokinetic and symbolic modeling of patients.

In this paper, we describe our design for advanced drug dosing programs that "reason" using a combination of Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends this previous work by incorporating a Bayesian pharmacokinetic model, performing a "meta-level" analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of this analysis in reasoning for dosing and therapeutic monitoring recommendations. The design has been implemented in a program for aminoglycoside antibiotics called Aminoglycoside Therapy Manager. The program is user-friendly and runs on low-cost general-purpose hardware. The initial validation study showed that the program was as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software and that its dosing recommendations were similar to those of an expert.

Aminoglycosides↗
PubMed2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled mutt, the largest public database of paired sequencing and load measurements, spanning 35 studies and over 15,000 samples. Using mutt, we show that published machine learning models fail to generalize: on average they perform worse than a naive baseline that always predicted the training set mean. These failures stem from covariate shift-limited shared taxa between studies, differences in community composition, and differences in preprocessing pipelines-that silently derail model inputs. In contrast, Bayesian partially identified models do not attempt to impute microbial load, but instead propagate scale uncertainty through downstream analyses. Across 30 benchmark datasets, Bayesian partially identified models consistently outperformed normalization and machine learning approaches, providing a principled and reproducible foundation for microbiome inference.

16S rRNA-seq↗
PubMed2025

Genomic prediction and genome-wide association study for liver abscesses in crossbred beef cattle.

Liver abscesses are a concern in feedlot cattle, and little is known about the role of genetics in their development. This study aimed to estimate genetic parameters and to identify single-nucleotide polymorphisms (SNPs) associated with liver abscesses. Crossbred cattle representing 18 breeds in the U.S. Meat Animal Research Center Germplasm Evaluation Program were phenotyped for liver abscesses at slaughter (n&#x2005;=&#x2005;9,044). Seventeen percent of cattle had liver abscesses. These cattle had genotypes that were imputed to sequence variant genotypes. After filtering and quality control, 340,723 SNPs were used in the analysis. Liver abscess prevalence was modeled with a single-step genomic best linear unbiased prediction (ssGBLUP) threshold model using a Bayesian framework. The model included contemporary group (sex, treatment group, and slaughter date), additive genomic, and residual effects. Genomic heritability was 0.039 (95% highest posterior density&#x2005;=&#x2005;0.005, 0.081), which was very small. To assess prediction quality, a 5-fold random cross-validation structure was used. Method Linear Regression was used to assess accuracy, bias, and dispersion by comparing estimated breeding values (EBV) from full and reduced analyses. Cross-validation metrics showed EBV based on genotypes had 0.05 reliability (SD&#x2005;<&#x2005;0.01) with no bias relative to EBV based on genotypes and phenotypes. For the genome-wide association study, SNP effects were back calculated from the EBV solutions from ssGBLUP. No SNPs were associated with liver abscesses at a Benjamini-Hochberg adjusted 0.05 significance level. Although a large dataset was used, this result was because of the low genomic heritability and imprecise EBV used to calculate SNP effects. Based on these results, environmental factors contribute to most of the variation in liver abscesses. Genetic selection to reduce liver abscesses would be slow because of the low genomic heritability, measurement late in life, and inability to measure breeding animals. A faster approach would be finding additional environmental interventions that maintain animal performance.

Animals↗
PubMed1987

Disposition of phenytoin in critically ill trauma patients.

Estimates of phenytoin pharmacokinetic variables and protein binding were determined in 10 adult critically ill trauma patients. Each study subject received phenytoin sodium as an intravenous loading dose of 15 mg/kg, followed by an initial intravenous maintenance dose of 6 mg/kg/day. Serial blood samples were obtained throughout the seven-day study period and analyzed for total and unbound serum phenytoin concentrations. The concentration data for each patients were fitted to a one-compartment model with elimination defined by the Michaelis-Menten constant Km and the maximum rate of metabolism (Vmax) and to a one-compartment model with first-order elimination. The Michaelis-Menten model used Bayesian parameter estimation while the linear model used weighted non-linear least-squares regression analysis. Unbound phenytoin fraction ranged from 0.073 to 0.25. Free fraction increased 7% to 108% in 9 of 10 patients (median increase 29%) from day 1 to day 7 of therapy. Variable estimates using the Michaelis-Menten model were as follows: volume of distribution, 0.76 +/- 0.15 L/kg (0.58-1.01 L/kg); Vmax, 568 +/- 197 mg/day (350-937 mg/day); and Km, 4.5 +/- 1.8 mg/L (1.8-6.2 mg/L). These estimates fell within the wide range of values obtained in studies using stable patients or healthy volunteers. The Michaelis-Menten model was significantly less biased and more precise than the linear model. Three of four patients who continued to receive their study maintenance dose had substantially lower measured total serum concentrations of phenytoin than predicted using the study variable estimates.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1985

A Bayesian approach to nonlinear random effects models.

Nonlinear random effects models are considered from the Bayesian point of view. The method of analysis follows closely that of Lindley and Smith (1972, Journal of the Royal Statistical Society, Series B 34, 1-42). The numerical method is related to the EM algorithm.

Analysis of Variance↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗