What to consider when developing a new molecular HIV surveillance tool: Perspectives of key stakeholders working in HIV prevention and treatment.

Developing and validating novel molecular HIV surveillance (MHS) tools capable of predicting the growth and trajectory of localized outbreaks driven by specific transmission clusters is key to the . This study explored stakeholders' perspectives on HIV prevention and treatment regarding a developing deep-learning framework, and its ability to predict HIV transmission cluster trajectories and inform decision-making on HIV prevention and treatment scale-up approaches in Florida. We conducted five virtual focus group discussions with 16 clinical health professionals and state and local public health personnel.

SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.

Background: The COVID-19 pandemic has caused over 776 million infections and 7 million deaths globally between December 2019 and November 2024. Since the emergence of the original Wuhan strain, SARS-CoV-2 has evolved into multiple variants-including Alpha, Delta, and Omicron-primarily through mutations in the Spike glycoprotein. The S1 subunit, which binds the human angiotensin-converting enzyme 2 (ACE2) receptor, mutates frequently and plays a key role in infectivity and immune escape, while the more conserved S2 subunit mediates membrane fusion.

Variational temporal deconfounder network for individualized treatment effect estimation with longitudinal observational data.

Objective: By leveraging real-world electronic health record (EHR) data, this study set out to estimate individualized treatment effects (ITE) in longitudinal observational settings to advance personalized medicine, addressing key challenges that are often observed in real-world clinical scenarios and pose statistical challenges, including hidden confounding and dynamic treatment regimens.

Methods: We propose the Variational Temporal Deconfounder Network (VTDNet), a novel framework designed to account for time-varying hidden confounding using a variational recurrent transformer-based autoencoder. Specifically, VTDNet comprises three critical components: a temporal Encoder-Decoder structure to capture hidden representation, a Treatment Block that captures interdependencies among multiple treatments, and a Potential Outcome Block that predicts both factual and counterfactual outcomes.

Leveraging large language models to predict antibiotic resistance in Mycobacterium tuberculosis.

Motivation: Antibiotic resistance in Mycobacterium tuberculosis (MTB) poses a significant challenge to global public health. Rapid and accurate prediction of antibiotic resistance can inform treatment strategies and mitigate the spread of resistant strains. In this study, we present a novel approach leveraging large language models (LLMs) to predict antibiotic resistance in MTB (LLMTB).

Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.

Background: Human Immunodeficiency Virus (HIV) pre-exposure prophylaxis (PrEP) prevents HIV transmission but has low uptake among women. Identifying women who could benefit from PrEP remains a challenge. This study developed a women-specific model to predict HIV risk within a year using electronic health record (EHR) data and social determinants of health (SDoH).

Behavioral and Demographic Profiles of HIV Transmission and Exposure Networks in Florida: Network Analysis of HIV Contact Tracing Data.

Background: To complete the Ending the HIV Epidemic initiative in areas with high HIV incidence, there needs to be a greater understanding of the demographic, behavioral, and geographic factors that influence the rate of new HIV diagnoses. This information will aid the creation of targeted prevention and intervention efforts.

Objective: This study aims to identify the geographic distribution of risk groups and their role within potential transmission networks in Florida.

Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.

Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders, is increasing. Previous in vitro studies have shown that bacteria can become resistant to antibiotics when exposed to SSRIs.

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.

Portable genomic sequencers such as Oxford Nanopore's MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g.

Dynamic Networks of Methicillin-Resistant in Communities Drive Hospital Transmission.

Background: Although methicillin-resistant (MRSA) transmission has traditionally been viewed separately in hospital and community settings, this distinction is increasingly blurred. We used whole-genome sequencing and epidemiologic analyses to characterize the movement of MRSA across these interfaces in a rural-urban population.

Methods: Serial cross-sectional sampling of MRSA isolates occurred at a tertiary care hospital between 2010 and 2019.

HIV Risk Score and Prediction Model in the United States: A Scoping Review.

Human immunodeficiency virus (HIV) remains a public health issue in the U.S., affecting approximately 1.

Content hubs, information flows, and reactions for pesticide-related discussions on Twitter/X.

Pesticides are essential in modern agriculture for controlling pests and enhancing food production. However, concerns about their human and environmental health impacts have broadened discussions on their use, regulation, ethics, and sustainability. Scientific research, media coverage, and input from corporations, governments, and nongovernmental organizations (NGOs) shape public opinions and potentially influence regulatory decisions.

Predicting HIV Diagnosis Among Emerging Adults Using Electronic Health Records and Health Survey Data in All of Us Research Program.

The global decline in HIV incidence has not been mirrored in the United States, where young adults (ages 18-29) continue to account for a significant portion of new infections. In this study, we leverage the All of Us (AoU) Research Program's extensive electronic health records (EHRs) and health survey data to develop machine learning models capable of predicting HIV diagnoses at least three months before clinical identification. Among various models tested, the Support Vector Machine (SVM) model demonstrated a balanced performance, integrating clinically relevant features with robust predictive accuracy (AUC = 0.

A comparative study of antibiotic resistance patterns in Mycobacterium tuberculosis.

This study leverages the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) to analyze over 27,000 Mycobacterium tuberculosis (MTB) genomic strains, providing a comprehensive and large-scale overview of antibiotic resistance (AMR) prevalence and resistance patterns. We used MTB++, which is the newest and most comprehensive AI-based MTB drug resistance profiler tool, to predict the resistance profile of each of the 27,000 MTB isolates and then used feature analysis to identify key genes that were associated with the resistance. There are three main contributions to this study.

Receipt of long-acting injectable antiretroviral therapy among people with HIV in Southern US states: an assessment using electronic health records and claims data.

Background: In January 2021, the United States (US) Food and Drug Administration (FDA) approved the first long-acting injectable antiretroviral therapy (LAI ART) regimen for the treatment of HIV providing an alternative to daily oral regimens. We analyzed electronic health records (EHRs) to provide real-world evidence of demographic and clinical characteristics associated with the receipt of LAI ART among people with HIV (PWH).

Methods: Leveraging EHRs from a large clinical research network in the Southern US - OneFlorida + linked with Medicaid (updated to 08/2022) - we identified a cohort of PWH who have been prescribed at least one dose of LAI ART since January 2021 and characterized their demographics, clinical characteristics, and HIV care outcomes.

Brief Report: What Matters Most for Long-Acting Antiretroviral Therapy? A Best-Worst Scaling Discrete Choice Experiment.

Introduction: Florida remains a high-incidence, high-prevalence setting for HIV. Long-acting (LA) antiretroviral therapies (ART) could improve HIV-related outcomes and reduce transmission. This study identifies preferred LA ART characteristics and classes of preference among persons with HIV (PWH) in Florida.

Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, and sublineages that outcompete earlier strains, largely due to factors like increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system, to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute >10% of all the viral sequences added to the GISAID, a public database supporting viral genetic sequence sharing, in a given week.

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.

Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.

Materials And Methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.

An average-case efficient two-stage algorithm for enumerating all longest common substrings of minimum length between genome pairs.

A problem extension of the longest common substring (LCS) between two texts is the enumeration of all LCSs given a minimum length (ALCS- ), along with their positions in each text. In bioinformatics, an efficient solution to the ALCS- for very long texts -genomes or metagenomes- can provide useful insights to discover genetic signatures responsible for biological mechanisms. The ALCS- problem has two additional requirements compared to the LCS problem: one is the minimum length , and the other is that all common strings longer than must be reported.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods.

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time.

Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics.

Respiratory tract infections are a serious threat to health, especially in the presence of antimicrobial resistance (AMR). Existing AMR detection methods are limited by slow turnaround times and low accuracy due to the presence of false positives and negatives. In this study, we simulate 1,116 clinical metagenomics samples on both Illumina and Nanopore sequencing from curated, real-world sequencing of A.

Who wants long-Acting injectable antiretroviral therapy? Treatment preferences among adults with HIV in Florida.

Long-acting injectable (LAI) antiretroviral therapy (ART) is available to people with HIV (PWH), but it is unknown which PWH prefer this option. Using the Andersen Behavioral Model this study identifies characteristics of PWH with greater preference for LAI ART. Cross-sectional data from the Florida Cohort, which enrolled adult PWH from community-based clinics included information on predisposing (demographics), enabling (transportation, income), and need (ART adherence <90%) factors.

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

Motivation: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations.