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Opportunistic Screening for Chagas Disease Using an Artificial Intelligence-Enabled ECG: Prospective Evaluation of Feasibility and Diagnostic Accuracy.

BACKGROUND: Chagas disease (ChD), a neglected cardiovascular condition, affects 7.5 to 10.5 million people worldwide. Opportunistic screening during routine electrocardiography may allow earlier detection of unrecognized infections, enabling timely antiparasitic therapy or optimized cardiac care. This study prospectively evaluated the feasibility and diagnostic accuracy of an artificial intelligence (AI)-enabled ECG model enriched with 3 epidemiological questions (AI-ECG-EPI model) as an opportunistic screening for ChD in endemic regions of Brazil. METHODS: We conducted a prospective study em

Demographic, epidemiological, and health transitions: are they relevant to population health patterns in Africa?

BackgroundStudies of trends in population changes and epidemiological profiles in the developing world have overwhelmingly relied upon the concepts of demographic, epidemiological, and health transitions, even though their usefulness in describing and understanding population and health trends in developing countries has been repeatedly called into question. The issue is particularly relevant for the study of population health patterns in Africa and sub-Saharan Africa, as the history and experience there differs substantially from that of Western Europe and North America, for which these conce

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… WHO uses numerous data sources and epidemiological models to estimate the worldwide cause-of-death patterns. …

A metapopulation model to simulate West Nile virus circulation in Western Africa, Southern Europe and the Mediterranean basin.

In Europe, virological and epidemiological data collected in wild birds and horses suggest that a recurrent circulation of West Nile virus (WNV) could exist in some areas. Whether this circulation is permanent (due to overwintering mechanisms) or not remains unknown. The current conception of WNV epidemiology suggests that it is not: this conception combines an enzootic WNV circulation in tropical Africa with seasonal introductions of the virus in Europe by migratory birds. The objectives of this work were to (i) model this conception of WNV global circulation; and (ii) evaluate whether the mo

Preventing Atherosclerotic Cardiovascular Disease in Young Male Androgen Abusers.

BACKGROUND: Androgen abuse among young men is a prevalent yet under recognized risk factor for atherosclerotic cardiovascular disease (ASCVD). Supraphysiological androgen exposure adversely affects lipoprotein profiles, blood pressure, and vascular function, potentially accelerating atherosclerosis even in otherwise healthy individuals. Conventional cardiovascular risk prediction models may underestimate risk in this population due to fluctuating biomarker profiles and other unaccounted risk-increasing factors that are not captured in these models. OBJECTIVES: To review the mechanisms by whic

Низкочастотное магнитное поле и нейродегенеративные заболевания: систематический обзор исследований животных.

Эпидемиологические исследования выявили связь между профессиональным воздействием низкочастотных магнитных полей и возникновением заболевания моторными нейронами и болезнью Альцгеймера. Не было обнаружено никакой связи с болезнью Паркинсона, и доказательств множественного склероза недостаточно. Модели животных, в которых изучается влияние низкочастотных магнитных полей на индукцию или прогрессирование нейродегенеративных заболеваний, могут дать больше доказательств причинно-следственной связи и основополагающих механизмов. Был проведен систематический поиск и обзор рецензируемых научных статей

Causal association between long-term exposure to air pollution and incident Parkinson's disease.

Epidemiological evidence for long-term air pollution exposure and Parkinson's disease (PD) is controversial, and analysis of causality is limited. We identified 293,888 participants who were free of PD at baseline in the UK Biobank (2006-2010). Time-varying air pollution [fine particulate (PM2.5) and ozone (O3)] exposures were estimated using spatio-temporal models. Incident cases of PD were identified using validated algorithms. Four methods were used to investigate the associations between air pollution and PD, including (1) standard time-varying Cox proportional-hazard model; (2) Cox models

Performance analysis of mathematical methods used to forecast the 2022 New York City Mpox outbreak.

In mid-2022, New York City (NYC) became the epicenter of the US mpox outbreak. We provided real-time mpox case forecasts to the NYC Department of Health and Mental Hygiene to aid in outbreak response. Forecasting methodologies evolved as the epidemic progressed. Initially, lacking knowledge of at-risk population size, we used exponential growth models to forecast cases. Once exponential growth slowed, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Retrospectively, we explored if forecasts could have been improved using an SEIR model in place of our early exponential growth mo

Model design for non-parametric phylodynamic inference and applications to pathogen surveillance.

Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based

Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa.

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guidi

Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution.

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-ter

Predictions in an illness-death model.

Multi-state models allow subjects to move among a finite number of states during a follow-up period. Most often, the objects of study are the transition intensities. The impact of covariates on them can also be studied by specifying regression models. Thus, estimation in multi-state models is usually focused on the transition intensities (or the cumulative transition intensities) and on the regression parameters. However, from a clinical or epidemiological point of view, other quantities could provide additional information and may be more relevant to answer practical questions. For example, g

Space Radiation and Cancer Risk in Astronauts: Models, Evidence, Uncertainties, and Emerging Imaging Perspectives.

Cancer risk estimation remains one of the main unresolved challenges in human spaceflight beyond low Earth orbit, where astronauts are exposed to galactic cosmic rays, solar particle events, and high-linear energy transfer (high-LET) secondary radiation. This narrative review summarizes the principal quantitative models used to estimate radiation-induced cancer risk in astronauts, including particle fluence-based cross-sections, mixture models, risk of exposure-induced death (REID)-based operational frameworks, uncertainty distribution approaches, and ensemble models. Early studies estimated 1

Classification of chicken Eimeria species through deep transfer learning models: A comparative study on model efficacy.

Eimeria is a protozoan parasite that causes coccidiosis in various animal species, especially in chickens, resulting in infections characterized by intestinal damage, hemorrhagic diarrhea, lethargy, and high mortality rates in the absence of effective control measures. The rapid spread of these parasites through ingestion of food and drinking water can seriously endanger animal health and productivity, leading to significant economic losses in the chicken industry. Chicken Eimeria species are difficult to identify by conventional microscopy due to similarities in oocyst morphologies. In additi

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building.

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequen

Automating chart review for identification of infected patients using natural language processing.

OBJECTIVE: Expert clinician chart review is the most accurate method to identify patients with infection but is laborious and costly. We aimed to develop and externally validate machine-learning models using structured and unstructured data to identify infection in hospitalized patients compared to chart review. MATERIALS AND METHODS: Adult inpatient encounters at 2 health systems (2006-2016) were manually annotated for suspected infection on admission. Data from one site were used to develop gradient-boosted machine-learning models. Structured features included labs, vital signs, and nursing

Establishment of multiple predictive models for early identification of the dengue cases in outpatient healthcare systems.

Dengue affected countries have responded with many surveillance strategies to detect outbreaks early. However, the early identification for the individual case before a confirmed dengue diagnosis is still lacking, limiting the effectiveness of early intervention strategies. This case-control study involved 206 dengue cases and 824 controls, which selected from patients who visited outpatient clinics at any medical institutions affiliated to the Futian District of Shenzhen city, China from January 1, 2018 to December 15, 2024. All medical visit information was obtained from the "Futian District

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data. METHODS: Cross-sectional study of children aged five to 18 years who underwent biometry and autorefraction before and after cycloplegia. Myopia was defined as cycloplegic spherical equivalent refraction (SER) ≤-0.5 Diopter (D). Models were evaluated for predicting SER using R2 and mean absolute error (MAE) and myopia status using area under the receiver operating characteristic (ROC) curve (AUC). Best-performing m

Using human brain imaging studies as a guide toward animal models of schizophrenia.

Schizophrenia is a heterogeneous and poorly understood mental disorder that is presently defined solely by its behavioral symptoms. Advances in genetic, epidemiological and brain imaging techniques in the past half century, however, have significantly advanced our understanding of the underlying biology of the disorder. In spite of these advances clinical research remains limited in its power to establish the causal relationships that link etiology with pathophysiology and symptoms. In this context, animal models provide an important tool for causally testing hypotheses about biological proces

VNTR analysis of selected outbreaks of Burkholderia pseudomallei in Australia.

Molecular typing methods for Burkholderia pseudomallei have been successful at assigning isolates into epidemiologically related groups, but have not been able to detect differences and define evolutionary patterns within groups. Our variable number tandem repeat (VNTR) analysis of a set of 121 Australian B. pseudomallei isolates, 104 of which were associated with nine epidemiological groups, provides fine scale differentiation even among very closely related isolates. We used a Bayesian model based upon mutation accumulation patterns to define the close phylogenetic relationships within these

Clinical decision support for ExtraCorporeal Membrane Oxygenation: Will we fly by wire?

Prognostic modelling techniques have rapidly evolved over the past decade and may greatly benefit patients supported with ExtraCorporeal Membrane Oxygenation (ECMO). Epidemiological and computational physiological approaches aim to provide more accurate predictive assessments of ECMO-related risks and benefits. Implementation of these approaches may produce predictive tools that can improve complex clinical decisions surrounding ECMO allocation and management. This Review describes current applications of prognostic models and elaborates on upcoming directions for their clinical applicability