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PubMed1991

Bayesian image processing in magnetic resonance imaging.

In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.

Algorithms↗
PubMed1992

[Data analysis by statistical models].

The basic idea for the realization of effective statistical data analysis is illustrated with an example. The use of statistical models is explained and the feasibility of objective comparison of the models by an information criterion AIC is demonstrated. Further, the possibility of practical use of Bayesian models for complex data analysis is explained. Finally, the necessity of cooperation between the experts of respective fields and statisticians for further development of statistical data analysis is mentioned.

Adult↗
PubMed1989

A Bayesian methodology for scaling radiation studies from animals to man.

This paper describes a Bayesian methodology for integrating studies in experimental animals and humans to obtain a risk estimate for a radionuclide for which no data or very limited human data are available. The method is quite general and is not limited to radiation studies. In fact, it was first developed for chemical toxicants. The methodology is illustrated using studies with rats, beagles, and humans exposed to isotopes of Ra and Pu. The goal is a quantitative risk estimate for bone cancer in humans exposed to internally deposited Pu. The choice of bone cancer as an end point and of Pu as the source of exposure was made partially because of its inherent interest but also because of issues of data availability and suitability. We performed Poisson regression analyses on 13 of 15 data sets. These analyses form the basis for the unifying method of interpreting the entire ensemble of studies. Each of the studies is summarized by the estimated dose-response slope and its estimated standard error. These summary statistics are combined with other available biological and physical information about species differences, physical and metabolic characteristics of isotopes, disease mechanisms, and the like. This information enters the analysis in the form of prior assumptions about the parameters of the Bayesian model combining the studies. The posterior distribution for the bone cancer rate in man from the Bayesian analysis of the 13 studies is updated with the limited data on Pu in humans. This update gives the final probability density for the bone cancer rate in humans exposed to internally deposited Pu. This density has a median of about three cancers per 100 Gy and has a 95% probability interval from 0.8 to 11 bone cancers per 100 Gy.

Animals↗
PubMed1987

Bayesian statistical theory in the preoperative diagnosis of pulmonary lesions.

We used a computerized Bayesian algorithm to assist in the preoperative diagnosis of pulmonary lesions. One hundred consecutive patients who were undergoing exploratory thoracotomy for newly discovered pulmonary lesions were prospectively evaluated. The Bayesian model used a total of 44 preoperative clinical and roentgenographic factors to categorize the lesions as benign or malignant. The Bayesian algorithm correctly categorized 96 of the 100 lesions, thereby providing an accuracy of 96 percent. The sensitivity of the model was 98 percent and the specificity was 87 percent. All but two of the 85 malignant lesions were correctly categorized and 13 of the 15 benign lesions were correctly analyzed by the model. These results indicate that computer-assisted diagnosis using the Theorem of Bayes may provide valuable preoperative information for the management of selected patients.

Adolescent↗
PubMed1987

The "constant intake rate" assumption in interim recruitment goal methodology for multicenter clinical trials.

A primary concern of any multihospital clinical trial is the recruitment of a predetermined number of patients during a prespecified interval of time. In several recent papers a Poisson based model was used to estimate the time needed to recruit a predetermined number of patients and the probabilities of recruiting specified fractions of the sample during subintervals. The Poisson model requires the assumption that patients be recruited at a constant rate over the entire length of the interval. In this paper we test the adequacy of this model and assumption using patient intake data from nine multihospital VA clinical trials and propose an alternative Bayesian model.

Bayes Theorem↗
PubMed1992

Ranitidine pharmacokinetics and adverse central nervous system reactions.

BACKGROUND: Treatment with histamine2-receptor antagonists has been associated with adverse central nervous system reactions (CNS-ADRs). Previous studies of cimetidine have shown an association between CNS-ADRs and high cimetidine drug levels. While case reports of ranitidine CNS-ADRs have appeared, we wanted to study a series of patients, some of whom were critically ill, for the presence of CNS-ADRs and to correlate these with ranitidine pharmacokinetics. METHODS: A prospective, observational, open study included 163 consecutive patients, of whom 41 met entry criteria. A nonlinear least-squares regression analysis was used to establish a ranitidine pharmacokinetic dosing model. Ranitidine levels were determined by a high-performance liquid chromatographic assay. Individual ranitidine pharmacokinetics were determined by means of a bayesian model. Observations on 13 possible CNS-ADRs were recorded. The CNS-ADRs were evaluated by the Naranjo rating system. RESULTS: Ranitidine-associated CNS-ADRs, particularly lethargy, confusion, somnolence, and disorientation, occurred more frequently in patients with renal function impairment, and these were associated with higher peak concentrations, average plasma concentrations, and area under the curve. CONCLUSIONS: Ranitidine, when given in conventional doses, can cause CNS-ADRs, particularly in older patients who have substantial renal function impairment. These CNS-ADRs occur as a consequence of altered ranitidine disposition. Ranitidine doses should be reduced when renal function impairment is present, and patients should be carefully observed for CNS-ADRs.

Aged↗
PubMed2025

Inferring the sensitivity of wastewater metagenomic sequencing for early detection of viruses: a statistical modelling study.

BACKGROUND: Metagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We aimed to quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. METHODS: In this statistical modelling study, we analysed sequencing data from four studies of untargeted W-MGS to estimate the relative abundance of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public health reports. We combined these estimates using a hierarchical Bayesian model to predict relative abundance at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridisation capture enrichment panel. FINDINGS: After controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponded to a predicted SARS-CoV-2 relative abundance ranging from 3·8 × 10-10 to 2·4 × 10-7 across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies greatly increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 27-fold (from US$7·87 million to $287 000) and 29-fold (from $1·98 million to $69 100) for a system able to detect a SARS-CoV-2-like pathogen before reaching 0·01% cumulative incidence. INTERPRETATION: The large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and laboratory protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well chosen hybridisation capture panels can greatly increase sensitivity and reduce cost for viruses in the panel, but might reduce sensitivity to unknown or unexpected pathogens. FUNDING: The Wellcome Trust, Open Philanthropy, and Musk Foundation.

Humans↗
PubMed2025

BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.

MOTIVATION: statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. RESULTS: We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100× more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION: Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

Genome-Wide Association Study↗
PubMed2026

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Over 75 Alzheimer's disease (AD) and dementia-associated variants have been identified through genome-wide association studies, but the utility of polygenic risk scores (PRS) for predicting AD and dementia in diverse and admixed populations remains unclear. We compared how PRS approaches differing in p-value thresholds, variant weights, and source ancestry perform in predicting dementia in 6338 African American, Chinese, Hispanic, and White individuals from the Multi-Ethnic Study of Atherosclerosis. We tested clumping and thresholding (C+T) methods with varying parameters against Bayesian approaches (PRS-CS, PRS-CSx). We compared the ability of each method to predict incident dementia in all participants and in groups stratified by self-reported race/ethnicity. We additionally analyzed performance across groups stratified by estimated proportion of non-Finnish European (NFE)-like ancestry. Including more variants does not improve performance. We found comparable associations between dementia and PRS when comparing a C+T method with only 15 SNPs and PRS derived from Bayesian models that include >&#x2009;800,000 SNPs (HR5e-08 = 1.18, 95% CI: 1.08-1.28; HRCSx = 1.17, 95% CI: 1.07-1.27). The p&#x2009;<&#x2009;5e-08 C+T method was more strongly associated with incident dementia in populations genetically dissimilar from the source data (HRlowNFE_5e-08 = 1.27, 95% CI: 1.08-1.50; HRlowNFE_CSx = 1.12, 95% CI: 0.94-1.33). More selective PRS models using genome-wide significant SNPs may be preferable for dementia prediction in diverse populations.

Aged↗
PubMed1991

Ruling out acute myocardial infarction. A prospective multicenter validation of a 12-hour strategy for patients at low risk.

BACKGROUND: Although previous investigations have suggested that 24 hours is required to exclude acute myocardial infarction in patients who are admitted to a coronary care unit for the evaluation of acute chest pain, we hypothesized that a 12-hour period might be adequate for patients with a low probability of infarction at the time of admission. METHODS: Using a Bayesian model, we developed a strategy to identify candidates for a shorter period of observation from an analysis of a derivation set of 976 patients with acute chest pain who were admitted to three teaching and four community hospitals. In the derivation set, patients whose clinical characteristics in the emergency room predicted a low (less than or equal to 7 percent) probability of myocardial infarction had only a 0.4 percent risk of infarction if they had neither abnormal levels of cardiac enzymes nor recurrent ischemic pain during the first 12 hours of hospitalization. In an independent testing set of 2684 patients from the seven hospitals, 957 admitted patients (36 percent) were classified as candidates for this 12-hour period of observation according to a previously published multivariate algorithm. Few of these patients were actually transferred from a monitored setting at 12 hours. RESULTS: Of the 771 candidates for a 12-hour period of observation who did not have enzyme abnormalities or recurrent pain during the first 12 hours, 4 (0.5 percent) were subsequently found to have acute myocardial infarction, and only 3 (0.4 percent) died after primary cardiac arrests, all of which occurred three to five days after admission. Rates of other major cardiovascular complications were low in the patients who might have been transferred from the coronary care unit after 12 hours with this strategy. In patients with a higher initial risk of infarction, the standard strategy of 24-hour observation identified all but 11 of 739 acute myocardial infarctions (1 percent). CONCLUSIONS: Emergency room clinical data can be used to identify a large subgroup of patients for whom a 12-hour period of observation is normally sufficient to exclude acute myocardial infarction. Patient-specific evaluation and treatment can then proceed without the restrictions imposed by "rule-out" protocols for myocardial infarction.

Adult↗
PubMed2026

Mutational signatures in blood-brain barrier: mechanisms, computational insights, and clinical applications in precision oncology.

The blood - brain barrier (BBB) plays a central role in maintaining central nervous system (CNS) homeostasis, and its disruption is a defining feature of malignant brain tumors such as glioblastoma. Emerging evidence indicates that BBB dysfunction not only alters the tumor microenvironment but also shapes the mutational processes that drive genomic instability in CNS malignancies. This review synthesizes current understanding of the biological mechanisms linking BBB breakdown with distinct mutational signatures, including those arising from oxidative stress, hypoxia-induced replication stress, lipid peroxidation, inflammation, and metabolic reprogramming. Advances in next-generation sequencing, coupled with computational tools such as non-negative matrix factorization, Bayesian modeling, and deep learning, have enabled precise extraction of these signatures and their integration with multi-omics data. Clinically, BBB-associated mutational signatures offer significant promise for therapeutic stratification, prediction of treatment response, and noninvasive monitoring through cerebrospinal fluid - derived circulating tumor DNA. Despite these advances, challenges persist due to limited tissue accessibility, low-yield CSF samples, incomplete mechanistic models, and the lack of CNS-specific analytical frameworks. A deeper understanding of BBB-driven mutational processes, supported by improved computational approaches and integrative datasets, holds potential to advance precision oncology in neuro-oncology.

Humans↗
PubMed2026

Identifying multigenic modules under selection in the tumor genome.

MOTIVATION: Genomic alterations in cancer arise from selective pressures acting on hallmark molecular modules, layered over a background of random mutagenic events. Methods to detect selection at the level of modules, as opposed to genes or nucleotides, are relatively underdeveloped. RESULTS: Here we present CanSRMaPP (Cancer Selection Recovery by Maximum Posterior Probability), a Bayesian model of the cancer genome that infers mutational selection on single genes and multi-genic modules while simultaneously modeling background events. Applying CanSRMaPP to lung adenocarcinoma genomes, we identify positive selection on 63 modules, yielding a model that parsimoniously explains the observed pattern of genetic alterations observed in new cancer cohorts. We further show that CanSRMaPP is adaptable to more tumor types and to alternative module definitions. We show that these modules serve as an effective scaffold for translating the cancer genome to molecular states, with prediction of cancer biomarker status as demonstration. AVAILABILITY: CanSRMaPP is freely available on GitHub. SUPPLEMENTARY INFORMATION: Supplementary Figs. S1-5, Supplementary Tables S1-5, and Supplementary Notes 1 and 2 are available at Bioinformatics online.

Journal Article↗
PubMed1989

Evaluation of a two-compartment Bayesian forecasting program for predicting vancomycin concentrations.

The application of a two-compartment Bayesian forecasting program for vancomycin was tested retrospectively in 45 adult patients with stable renal function. Serial blood samples from 25 of these patients were used to determine population-based parameter estimates. The predictive performance of the Bayesian program was assessed by using both non-steady-state and steady-state vancomycin concentrations as feedback information. Overall, the program tended to underpredict peak and trough steady-state vancomycin serum concentrations. A larger mean prediction error (ME) was seen when non-steady-state feedback serum concentrations were used compared with using population-based parameter estimates (no feedback). In contrast, a marked improvement in ME (peaks: -1.03 versus -2.61; troughs: -1.60 versus -2.07) was seen when steady-state feedback serum concentrations were used compared with no feedback data. Precision improved when either feedback serum concentrations were used to predict steady-state peak and trough vancomycin concentrations. The results from this clinical evaluation demonstrate that the initial pharmacokinetic parameter estimates for a two-compartment Bayesian model provided accurate prediction of steady-state vancomycin concentrations. Prediction bias and precision were improved when steady-state vancomycin concentrations were used to determine individualized pharmacokinetic parameters.

Adult↗
PubMed1992

Predictive performance of Bayesian and nonlinear least-squares regression programs for lidocaine.

The predictive performance of two computer programs for lidocaine dosing were evaluated. Two-compartment Bayesian and nonlinear least-squares regression programs were used in two groups of patients (15 acute arrhythmia patients and 14 chronic arrhythmia patients). Lidocaine was given as a 1.5 mg/kg bolus and a 2.8 mg/min infusion for 48 h. A second bolus (0.5 mg/kg) was given 10 min after the first bolus over 2 min. Serum samples of the patients receiving lidocaine were drawn at 2, 15, 30 min and 1, 2, and 4 h and were used in forecasting the serum concentrations at 6, 8, 12, and 48 h. Predictive performance was assessed by mean error and mean-squared error. The results (mean +/- 95% confidence intervals) demonstrated the Bayesian program predicted a significant (p less than 0.05) difference at 12 h between the two arrhythmia groups (acute 0.52 [-0.95; -0.09] and chronic 0.28 [0.12; 0.44]). The results also demonstrated the Bayesian method was significantly more precise compared to the nonlinear least-squares regression program at 8, 12, and 48 h for the acute group. While caution is warranted, this study demonstrated that the predictive performance by a two-compartment Bayesian model is more accurate in predicting future lidocaine serum concentrations than that by nonlinear least-squares regression.

Acute Disease↗
PubMed2026

Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype&#x2011;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↗