University of Florida Health Science Center

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition whose clinical severity may be influenced by social factors. We aimed to identify distinct COPD clinical phenotypes and assess variation by social determinants of health. In this retrospective cohort study, we identified adults aged 50-80 years with a diagnosis of COPD (n=59 797) at a tertiary academic medical centre in North-Central Florida using codes from International Classification of Diseases. Latent class analysis defined COPD clinical phenotypes using indicators of clinical severity, including frequency of acute care encounters (urgent care, emergency department visits and hospitalisations), presence of COPD as the principal diagnosis, comorbidity burden and use of Global Initiative for Chronic Obstructive Lung Disease Group D medications. Kaplan-Meier survival curves and Cox proportional hazards models assessed mortality across phenotypes. Multinomial logistic regression models estimated associations between phenotype membership and race/ethnicity, income, rurality and smoking status, using the minimal phenotype as reference. Five clinical phenotypes were identified: minimal (20.9%), mild (35.2%), moderate (22.5%), severe (12.2%) and very severe (9.3%). The very severe phenotype had the highest mortality (adjusted HR 2.94; 95% CI 2.72 to 3.18). Odds of very severe COPD were higher among non-Hispanic Black (adjusted OR (aOR) 1.29; 95% CI 1.21 to 1.36) and Hispanic individuals (aOR 1.75; 95% CI 1.63 to 1.87), those in the lowest income communities (aOR 1.25; 95% CI 1.18 to 1.32), rural residents (aOR 1.80; 95% CI 1.68 to 1.92) and individuals who currently smoke (aOR 1.30; 95% CI 1.20 to 1.42). Most patients with COPD had mild disease; however, the very severe phenotype, which was associated with higher mortality, was more common among Black and Hispanic individuals, those residing in lower-income and rural areas and those who currently smoke. These clinical phenotypes highlight sociodemographic differences in COPD severity as reflected in healthcare utilisation and outcomes.

Anterior shoulder instability, which includes dislocations and subluxations, is a significant concern in pediatric populations (≤19 years). Following an initial dislocation or subluxation, these patients often develop recurrent instability, which can result in long-term functional compromise. While timely and appropriate treatment is crucial, there remains a lack of consensus in the literature regarding the optimal management of pediatric shoulder instability. This systematic review aims to compare the efficacy of conservative and operative treatments for traumatic, isolated anterior shoulder instability in pediatric populations using recurrence of instability and return to play (RTP) at pre-injury levels as outcome measures. This study also provides a contemporary analysis that reflects evolving treatment strategies.This systematic review targeted studies published between 2013 and 2023 that evaluated pediatric patients (≤19 years), most between 13 and 19 years of age, who received conservative or operative treatment for a first-time or recurrent traumatic isolated anterior shoulder dislocation or subluxation. Statistical analysis was performed to compare rates of recurrence of instability and RTP within each group. Pooled effects (odds ratios) were estimated using fixed-effects models where heterogeneity was absent; otherwise, random-effects models were applied.A total of 1,459 patients (1,468 shoulders) met the inclusion criteria. Of these, 593 (40.4%) underwent conservative treatment, while 885 (60.3%) received operative interventions. Recurrence rates of instability were higher in the conservative group (236/543, 43.5%) compared to the primary operative group (182/875, 20.8%) and secondary operative group (2/26, 7.7%). RTP rates included 145/193 (75.1%) of conservatively treated patients, 380/480 (79.2%) of primary operative patients, and 8/11 (72.7%) of secondary operative patients returning to pre-injury levels. Among the four studies that directly compared conservative and primary operative treatments, the nonoperative group was more likely to have recurrence compared to the primary operative group (OR = 6.90; 95% CI, 2.28-20.91; p < 0.001). One of these studies was excluded due to methodological differences. A subsequent meta-analysis revealed a significantly higher likelihood of recurrent instability in conservatively treated patients (odds ratio (OR) = 9.55; 95% confidence interval (CI): 5.10-17.88; p < 0.001). In contrast, there was no statistically significant difference in RTP between groups (OR = 3.11; 95% CI: 0.31-30.97; p = 0.33).The findings support early surgical intervention in pediatric patients to reduce recurrence and improve functional outcomes. Conservative management, while recently shown to be successful for patients with less severe injuries such as subluxations, is still primarily associated with higher recurrence rates compared to operative treatment. Further studies are needed to refine treatment protocols by distinguishing effective strategies for subluxations versus dislocations. Future research should also explore the influence of factors such as sex, skeletal maturity, and activity level in determining optimal management strategies in pediatric patients.

PURPOSE:The purpose of this study was to identify clinically important candidate predictors of hospital-acquired pressure injuries among adult, medical-surgical patients.DESIGN:Modified Delphi technique.METHODS:Interdisciplinary clinicians were recruited for the expert panel, and multiple rounds were administered as electronic questionnaires using a modified Delphi approach. The opening round was informed by a literature review that was used to extract candidate predictors from prognostic model development studies of hospital-acquired pressure injury (HAPI). The panel evaluated clinical concepts by answering either “Yes” or “No” to whether each concept was “important” in the assessment of HAPI risk and categorized the concept as a predisposing or precipitating factor.RESULTS:The panel comprised 16 clinicians (8 physical/occupational therapists, 7 registered nurses, and 1 physician) from 2 large academic health care centers located in the US (Florida and California). The Delphi study comprised 3 rounds, with an overall attrition rate of 6.25%. The panel evaluated the clinical importance of 205 concepts that described HAPI risk. More than half of the panel agreed that 132 (64%) concepts were clinically “important,” including 99 (75%) predisposing factors and 33 (25%) precipitating factors. Thirteen (6%) concepts reached 100% agreement.CONCLUSION:Variable selection is a key component of prognostic model development study. Based on expert recommendations that highlight the use of clinical expertise in variable selection, we conducted a rigorous consensus study to gather feedback from clinicians who were experts in assessing, preventing, and treating pressure injuries. The final compilation of 132 “important” concepts provides a foundation for designing new or refining existing HAPI risk assessment tools. Future research should focus on identifying which concepts are valid predictors of HAPI and integrating them into standardized risk assessment tools.

Fascioliasis is a parasitic liver disease of mammals induced by liver flukes, Fasciola hepatica and Fasciola gigantica. Fasciola spp. rely on their definitive hosts, ruminants, and intermediate hosts, snails, to survive and can incidentally infect humans as definitive hosts. Ruminant (goat, sheep, and cattle) liver and fecal samples were collected from the Kharaa River Basin (KRB) of Mongolia during 2018-2020. A total of 807 adult liver flukes were found in livers of 18 goats, 21 sheep, and 1 cattle, with morphological identification of F. hepatica species. A total of 350 fecal samples selected using a "risk-based surveillance" method in the KRB were tested for Fasciola spp. eggs with 50.3% (n = 151/350) positive. By animal, cattle had the highest percentage of Fasciola spp. positive samples of 70% (14/20), followed by sheep with 49.4% (87/176) and goats with 32.5% (50/154). Six-hundred Lymnaied spp. snails, an intermediate host of Fasciola, were obtained from 18 locations in six provinces and one provincial municipality and morphometrically identified as Radix bactriana (94.8%) and Lymnaea stagnalis (5.2%). This study represents the first report of fascioliasis in indigenous animals in Mongolia and when paired with the detected prevalence of Fasciola spp. eggs in feces, suggests endemic circulation in the KRB region.

Genomic language models (gLMs) have recently emerged as powerful numerical surrogates for DNA, but existing architectures are largely focused on human DNA or trained on limited viral references, and no dedicated benchmark currently exists for viral genome understanding. Here we introduce vir2vec, a 422-million-parameter, decoder-only gLM obtained by continual pretraining of Mistral-DNA on a rigorously curated pan-viral corpus of 565,747 complete genomes spanning 295 viral species. vir2vec operates on byte-pair–encoded DNA subwords and exposes fixed-length genome-level embeddings that are reused across tasks. Additionally, we present vGUE, a unified benchmark for viral representation learning. In vGUE, we precompute vir2vec embeddings and feed them to simple classifiers (logistic regression, support vector machines, random forests) trained under nested cross-validation, to quantify how well they capture biologically motivated axes of viral variation. Using this framework, vGUE assesses genomic viral prediction tasks across: (i) organism-level discrimination (virus vs non-virus genomes and reads), (ii) genome-wide evolutionary fingerprints (DNA vs RNA viruses, host-range prediction), (iii) intra-genus species separation (HIV-1 vs HIV-2), (iv) fine-grained variant and subtype typing (SARS-CoV-2 lineages), and (v) phenotypic context signal detection (HIV-1 brain vs plasma Tropism). vir2vec attains the highest balanced accuracy across seven out of eight heterogenous classification tasks consistently outperforming both a human-trained genomic foundation model and a viral-specific one. By coupling a pan-viral gLM with a standardized evaluation suite, vir2vec and vGUE provide an open foundation for future viral genomic models, surveillance tools, and discovery pipelines. vir2vec is released as a controlled-access resource with the understanding that it is designed for discriminative/classification embedding tasks, and not generative; responsible deployment of viral genomic models requires consideration of dual-use implications and appropriate ethical, governance oversight.

BackgroundTicks continue to represent a significant threat to human and animal health worldwide. In the absence of comprehensive databases that compile the current published records of tick diversity and distributions, the information from hundreds of articles remains in isolation, incapable of being easily incorporated into surveillance gap analyses and tick-borne pathogen risk modeling. Here, our group systematically reviews and extracts data from published studies that detail ticks collected within seven target countries in North and West Africa.MethodsWe conducted a systematic literature review of documented tick distribution and tick-borne pathogen associations published from Algeria, Morocco, Nigeria, Niger, Senegal, Sierra Leone, and Tunisia between 1901 and 2022. Three databases were screened, and after a priori exclusion criteria, 372 articles were deemed eligible for review and underwent data extraction. Relevant information pertaining to ticks and tick-borne pathogens was collected and compiled into a dataset allowing for the articles to be georeferenced.ResultsThe articles included within the final dataset reported 74 hard tick species and 30 soft tick species throughout the seven target countries. Species of the genus Rhipicephalus dominated (n = 24 species), followed by Hyalomma (n = 14), Ornithodoros (n = 14), and Haemaphysalis (n = 13). Almost 80% of collection events involved ticks removed from nonhuman vertebrates. Approximately 12% of collection events included evidence of an associated microbial species, including 17 bacterial genera, 3 eukaryotic genera, and 16 viruses. Through mapping of the collection events, we begin to characterize surveillance gaps within each country.ConclusionsThis systematic review highlights notable surveillance gaps that vary by country and region. While Morocco, Nigeria, and Senegal report numerous studies concerning tick diversity, certain regions appear oversampled, while reports from other regions are largely absent. Information on the ticks of Sierra Leone and Niger remains largely uncharacterized. Across all target countries, reports are heavily biased toward sampling and testing ticks collected from domestic animals. Future tick surveillance efforts need to include sampling and testing of ticks collected from the environment and wildlife species to obtain a more complete assessment of tick distribution and tick-borne pathogen ecology.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s13071-025-07153-8.

Background/Objectives: The Developing and Evaluating a Machine Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE) trial aims to assess the usability, acceptability, feasibility, and effectiveness of implementing a machine learning (ML)-based clinical decision support (CDS) tool-the Overdose Prevention Alert-which predicts a patient's risk of opioid overdose within three months. Methods: This single-arm study uses a pre-post implementation design with mixed-methods evaluation in 13 University of Florida Health, Gainesville, internal medicine and family medicine clinics. Eligible patients are aged ≥18 years, received an opioid prescription within the year prior to their upcoming primary care visit, are not receiving hospice care, do not have a malignant cancer diagnosis, and are identified by the ML algorithm as high risk for overdose. The Overdose Prevention Alert triggers when a primary care provider (PCP) signs an opioid order in electronic health records. We will evaluate effectiveness by comparing pre- and post-implementation outcomes using a composite patient-level measure defined by the presence of any of the following 6 favorable indicators: (1) evidence of naloxone access; (2) absence of opioid overdose diagnoses and naloxone administration; (3) absence of emergency department (ED) visits or hospitalizations due to opioid overdose or opioid use disorder (OUD); (4) absence of overlapping opioid and benzodiazepine use within a 7-day window; (5) absence of opioid use ≥50 morphine milligram equivalent daily average; (6) receipt of referrals to non-pharmacological pain management. Additional quantitative metrics will include alert penetration, usage patterns, and clinical actions taken. Usability and acceptability will be assessed using a 12-item questionnaire for PCPs and semi-structured interviews. Expected Results: The trial will provide insights into real-world ML-driven CDS implementation and inform future strategies to reduce opioid-related harm.