Knowledge and practices related to louse- and flea-borne diseases among staff providing services to people experiencing homelessness in the United States.

BACKGROUND AND AIMS: Louse-borne Bartonella quintana infection and flea-borne murine typhus are two potentially serious vector-borne diseases that have led to periodic outbreaks among people experiencing homelessness in the United States. Little is known about louse- and flea-borne disease awareness and prevention among staff who provide services to the population. We surveyed staff in seven US states to identify gaps in knowledge and prevention practices for these diseases. METHODS AND RESULTS: Surveys were administered to 333 staff at 89 homeless shelters and outreach teams in California, Colorado, Georgia, Maryland, Minnesota, New York and Washington from August 2022 to April 2023. Most participants (>68%) agreed that body lice and fleas are a problem for people experiencing homelessness. About half were aware that diseases could be transmitted by these vectors; however, most could not accurately identify which diseases. Less than a quarter of staff could describe an appropriate protocol for managing body lice or fleas. Misconceptions included that clients must isolate or be denied services until they are medically cleared. CONCLUSIONS: Our findings reveal significant knowledge gaps among staff who provide services to people experiencing homelessness in the prevention and control of louse- and flea-borne diseases. This demonstrates an urgent need for staff training to both reduce disease and prevent unnecessary restrictions on services and housing.

Spatial and temporal analysis of HIV clinical outcomes in Florida reveals counties with persistent racial and ethnic disparities during 2012-2019.

BACKGROUND: Racial/ethnic disparities in the HIV care continuum have been well documented in the US, with especially striking inequalities in viral suppression rates between White and Black persons with HIV (PWH). The South is considered an epicenter of the HIV epidemic in the US, with the largest population of PWH living in Florida. It is unclear whether any disparities in viral suppression or immune reconstitution-a clinical outcome highly correlated with overall prognosis-have changed over time or are homogenous geographically. In this analysis, we 1) investigate longitudinal trends in viral suppression and immune reconstitution among PWH in Florida, 2) examine the impact of socio-ecological factors on the association between race/ethnicity and clinical outcomes, 3) explore spatial and temporal variations in disparities in clinical outcomes. METHODS: Data were obtained from the Florida Department of Health for 42,369 PWH enrolled in the Ryan White program during 2008-2020. We linked the data to county-level socio-ecological variables available from County Health Rankings. GEE models were fit to assess the effect of race/ethnicity on immune reconstitution and viral suppression longitudinally. Poisson Bayesian hierarchical models were fit to analyze geographic variations in racial/ethnic disparities while adjusting for socio-ecological factors. RESULTS: Proportions of PWH who experienced viral suppression and immune reconstitution rose by 60% and 45%, respectively, from 2008-2020. Odds of immune reconstitution and viral suppression were significantly higher among White [odds ratio =2.34, 95% credible interval=2.14-2.56; 1.95 (1.85-2.05)], and Hispanic [1.70 (1.54-1.87); 2.18(2.07-2.31)] PWH, compared with Black PWH. These findings remained unchanged after accounting for socio-ecological factors. Rural and urban counties in north-central Florida saw the largest racial/ethnic disparities. CONCLUSIONS: There is persistent, spatially heterogeneous, racial/ethnic disparity in HIV clinical outcomes in Florida. This disparity could not be explained by socio-ecological factors, suggesting that further research on modifiable factors that can improve HIV outcomes among Black and Hispanic PWH in Florida is needed.

Convenience, efficacy, safety, and durability of INSTI-based antiretroviral therapies: evidence from the Italian MaSTER cohort.

BACKGROUND: Integrase strand transferase inhibitors (INSTI), including raltegravir (RAL), elvitegravir (ELV), and dolutegravir (DTG), have demonstrated better efficacy and tolerability than other combination antiretroviral therapy (cART) classes in clinical trials; however, studies of sustainability of INSTI-containing therapy in the long-term are sparse. The purpose of this study was to provide an epidemiological overview comparing the outcome performance of different INSTI-based regimens longitudinally, including the metrics of efficacy, safety, convenience, and durability among a large, nationally representative cohort of persons living with HIV in Italy. METHODS: We selected subjects in the MaSTER cohort (an Italian multicenter, hospital-based cohort established in the mid-1990s that currently has enrolled over 24,000 PLWH) who initiated an INSTI-based regimen either when naïve or following a regimen switch. Cox proportional hazards regression models were fitted to evaluate associations between therapy interruptions and age, sex, nationality, transmission risk group, viral suppression status, CD4 + T-cell count, diagnosis year, cART status (naïve or experienced), and hepatitis coinfection. Results were stratified by cART INSTI type. RESULTS: There were 8173 participants who initiated an INSTI-based cART regimen in the MaSTER cohort between 2009 and 2017. The population was majority male (72.6%), of Italian nationality (88.6%), and cART-experienced (83.0%). Mean age was 49.7 (standard deviation: 13.9) years. In total, interruptions of the 1st INSTI-based treatment were recorded in 34% of cases. The most frequently cited reason for interruption among all three drug types was safety problems. In the survival analysis, past history of cART use was associated with higher hazards of interruption due to poor efficacy for all three drug types when compared to persons who were cART naïve. Non-viral suppression and CD4 + T-cell count < 200/mm3 at baseline were associated with higher hazards of interruption due to efficacy, safety, and durability reasons. Non-Italian nationality was linked to higher hazards of efficacy interruption for RAL and EVG. Age was negatively associated with interruption due to convenience and positively associated with interruption due to safety reasons. People who injects drugs (PWID) were associated with higher hazards of interruption due to convenience problems. Hepatitis coinfection was linked to higher hazards of interruption due to safety concerns for people receiving RAL. CONCLUSION: One-third of the population experienced an interruption of any drugs included in INSTI therapy in this study. The most frequent reason for interruption was safety concerns which accounted for one-fifth of interruptions among the full study population, mainly switched to DTG. The hazard for interruption was higher for low baseline CD4 + T-cell counts, higher baseline HIV-RNA, non-Italian nationality, older age, PWID and possible co-infections with hepatitis viruses. The risk ratio was higher for past history of cART use compared to persons who were cART naive, use of regimens containing 3 drugs compared to regimens containing 2 drugs. Durability worked in favor of DTG which appeared to perform better in this cohort compared to RAL and EVG, though length of follow-up was significantly shorter for DTG. These observational results need to be confirmed in further perspective studies with longer follow-up.

Associations of gut microbiome with endogenous estrogen levels in healthy postmenopausal women.

PURPOSE: The gut microbiome is a potentially important contributor to endogenous estrogen levels after menopause. In healthy postmenopausal women, we examined associations of fecal microbiome composition with levels of urinary estrogens, their metabolites, and relevant metabolic pathway ratios implicated in breast cancer risk. METHODS: Eligible postmenopausal women (n = 164) had a body mass index (BMI) ≤ 35 kg/m2 and no history of hormone use (previous 6 months) or cancer/metabolic disorders. Estrogens were quantified in spot urine samples with liquid chromatography-high resolution mass spectrometry (corrected for creatinine). Bacterial DNA was isolated from fecal samples and the V1-V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of gut microbiome's indices of within-sample (alpha) diversity (i.e., Shannon, Chao1, and Inverse Simpson), phylogenetic diversity, and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with individual estrogens and metabolic ratios, adjusted for age and BMI. RESULTS: In this sample of 164 healthy postmenopausal women, the mean age was 62.9 years (range 47.0-86.0). We found significant inverse associations of observed species with 4-pathway:total estrogens (p = 0.04) and 4-pathway:2-pathway (p = 0.01). Shannon index was positively associated with 2-catechols: methylated 2-catechols (p = 0.04). Chao1 was inversely associated with E1:total estrogens (p = 0.04), and 4-pathway:2-pathway (p = 0.02) and positively associated with 2-pathway:parent estrogens (p = 0.01). Phylogenetic diversity was inversely associated with 4-pathway:total estrogens (p = 0.02), 4-pathway:parent estrogens (p = 0.03), 4-pathway:2-pathway (p = 0.01), and 4-pathway:16-pathway (p = 0.03) and positively associated with 2-pathway:parent estrogens (p = 0.01). F/B ratio was not associated with any of the estrogen measures. CONCLUSION: Microbial diversity was associated with several estrogen metabolism ratios implicated in breast cancer risk. Further studies are warranted to confirm these findings in a larger and more representative sample of postmenopausal women, particularly with enrichment of minority participants.

Network typologies predict future molecular linkages in the network of HIV transmission.

OBJECTIVE: HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health. DESIGN: This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida. METHODS: HIV-1 molecular transmission clusters were reconstructed for PWH diagnosed in Florida from 2006 to 2017 using the HIV-TRAnsmission Cluster Engine (HIV-TRACE). A suite of machine-learning models designed to predict linkage to a new diagnosis were internally and temporally externally validated using a variety of demographic, clinical, and network-derived parameters. RESULTS: Of the 9897 individuals who received a genotype within 12 months of diagnosis during 2012-2017, 2611 (26.4%) were molecularly linked to another case within 1 year at 1.5% genetic distance. The best performing model, trained on two years of data, was high performing (area under the receiving operating curve = 0.96, sensitivity = 0.91, and specificity = 0.90) and included the following variables: age group, exposure group, node degree, betweenness, transitivity, and neighborhood. CONCLUSIONS: In the molecular network of HIV transmission in Florida, individuals' network position and connectivity predicted future molecular linkages. Machine-learned models using network typologies performed superior to models using individual data alone. These models can be used to more precisely identify subpopulations for intervention.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions.

BACKGROUND: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored. OBJECTIVE: The aim of this study is to convene experts in public health, infectious diseases, virology, and bioinformatics-many of whom were actively engaged in the COVID-19 response-to discuss and report on the application of phylodynamic tools to inform pandemic responses. METHODS: In total, 4 focus groups (FGs) occurred between June 2020 and June 2021, covering both the pre- and postvariant strain emergence and vaccination eras of the ongoing COVID-19 crisis. Participants included national and international academic and government researchers, clinicians, public health practitioners, and other stakeholders recruited through purposive and convenience sampling by the study team. Open-ended questions were developed to prompt discussion. FGs I and II concentrated on phylodynamics for the public health practitioner, while FGs III and IV discussed the methodological nuances of phylodynamic inference. Two FGs per topic area to increase data saturation. An iterative, thematic qualitative framework was used for data analysis. RESULTS: We invited 41 experts to the FGs, and 23 (56%) agreed to participate. Across all the FG sessions, 15 (65%) of the participants were female, 17 (74%) were White, and 5 (22%) were Black. Participants were described as molecular epidemiologists (MEs; n=9, 39%), clinician-researchers (n=3, 13%), infectious disease experts (IDs; n=4, 17%), and public health professionals at the local (PHs; n=4, 17%), state (n=2, 9%), and federal (n=1, 4%) levels. They represented multiple countries in Europe, the United States, and the Caribbean. Nine major themes arose from the discussions: (1) translational/implementation science, (2) precision public health, (3) fundamental unknowns, (4) proper scientific communication, (5) methods of epidemiological investigation, (6) sampling bias, (7) interoperability standards, (8) academic/public health partnerships, and (9) resources. Collectively, participants felt that successful uptake of phylodynamic tools to inform the public health response relies on the strength of academic and public health partnerships. They called for interoperability standards in sequence data sharing, urged careful reporting to prevent misinterpretations, imagined that public health responses could be tailored to specific variants, and cited resource issues that would need to be addressed by policy makers in future outbreaks. CONCLUSIONS: This study is the first to detail the viewpoints of public health practitioners and molecular epidemiology experts on the use of viral genomic data to inform the response to the COVID-19 pandemic. The data gathered during this study provide important information from experts to help streamline the functionality and use of phylodynamic tools for pandemic responses.

Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin.

Florida is considered an epicenter of HIV in the United States. The U.S. federal plan for Ending the HIV Epidemic (EHE) within 10 years prioritizes seven of Florida's 67 counties for intervention. We applied molecular epidemiology methods to characterize the HIV infection networks in the state and infer whether the results support the EHE. HIV sequences (N = 34,446) and associated clinical/demographic metadata of diagnosed people with HIV (PWH), during 2007 to 2017, were retrieved from the Florida Department of Health. HIV genetic networks were investigated using MicrobeTrace. Associates of clustering were identified through boosted logistic regression. Assortative trait mixing was also assessed. Bayesian phylogeographic methods were applied to evaluate evidence of imported HIV-1 lineages and illustrate spatiotemporal flows within Florida. We identified nine large clusters spanning all seven EHE counties but little evidence of external introductions, suggesting-in the absence of undersampling-an epidemic that evolved independently from the rest of the country or other external influences. Clusters were highly assortative by geography. Most of the sampled infections (82%) did not cluster with others in the state using standard molecular surveillance methods despite satisfactory sequence sampling in the state. The odds of being unclustered were higher among PWH in rural regions, and depending on demographics. A significant number of unclustered sequences were observed in counties omitted from EHE. The large number of missing sequence links may impact timely detection of emerging transmission clusters and ultimately hinder the success of EHE in Florida. Molecular epidemiology may help better understand infection dynamics at the population level and underlying disparities in disease transmission among subpopulations; however, there is also a continuous need to conduct ethical discussions to avoid possible harm of advanced methodologies to vulnerable groups, especially in the context of HIV stigmatization. IMPORTANCE The large number of missing phylogenetic linkages in rural Florida counties and among women and Black persons with HIV may impact timely detection of ongoing and emerging transmission clusters and ultimately hinder the success of epidemic elimination goals in Florida.

Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida.

INTRODUCTION: Urinary tract infections (UTIs) are common infections for which initial antibiotic treatment decisions are empirically based, often without antibiotic susceptibility testing to evaluate resistance, increasing the risk of inappropriate therapy. We hypothesized that models based on electronic health records (EHR) could assist in the identification of patients at higher risk for antibiotic-resistant UTIs and help guide the selection of antimicrobials in hospital and clinic settings. METHODS: EHR from multiple centers in North-Central Florida, including patient demographics, previous diagnoses, prescriptions, and antibiotic susceptibility tests, were obtained for 9990 patients diagnosed with a UTI during 2011-2019. Decision trees, boosted logistic regression (BLR), and random forest models were developed to predict resistance to common antibiotics used for UTI management [sulfamethoxazole-trimethoprim (SXT), nitrofurantoin (NIT), ciprofloxacin (CIP)] and multidrug resistance (MDR). RESULTS: There were 6307 (63.1%) individuals with a UTI caused by a resistant microorganism. Overall, the population was majority female, white, non-Hispanic, and older aged (mean = 60.7 years). The BLR models yielded the highest discriminative ability, as measured by the out-of-bag area under the receiver-operating curve (AUROC), for the resistance outcomes [AUROC = 0.58 (SXT), 0.62 (NIT), 0.64 (CIP), and 0.66 (MDR)]. Variables in the best performing model were sex, history of UTIs, catheterization, renal disease, dementia, hemiplegia/paraplegia, and hypertension. CONCLUSIONS: The discriminative ability of the prediction models was moderate. Nonetheless, these models based solely on EHR demonstrate utility for the identification of patients at higher risk for resistant infections. These models, in turn, may help guide clinical decision-making on the ordering of urine cultures and decisions regarding empiric therapy for these patients.

Moving from predicting hospital deaths by antibiotic-resistant bloodstream bacteremia toward actionable risk reduction using machine learning on electronic health records.

Drug-resistant bacterial infections are a global health concern with high mortality and limited treatment options. Several clinical risk-severity scores are available, e.g. qPitt, but their predictive performance is moderate. Here, we leveraged machine learning and electronic health records (EHRs) to improve prediction of mortality due to bloodstream infection with Klebsiella pneumoniae. We tested the qPitt score and new EHR variables (either expert-chosen or the full set of diagnostic codes), fitting LASSO, boosted logistic regression (BLR), support vector machines, decision trees, and random forests. The qPitt score showed moderate discriminative ability (AUROC=0.63), whilst machine learning models significantly improved its performance (best AUROC by BLR 0.80 for expert-chosen and 0.88 for full code set). Similar results were obtained in critically ill patients, and when excluding potential non-causal variables to evaluate an actionable model. In conclusion, current risk scores for bacteremia mortality can be improved and, with opportune causal modelling, considered for deployment in clinical decision-making.

Evaluation of screen, test, & protect: A public health program to control and prevent COVID-19 at a large academic university.

Background: We evaluate the public health surveillance program, Screen, Test, and Protect (STP) designed to control and prevent COVID-19 at a large academic university in the United States. Methods: STP was established at the University of Florida in May 2020. This report details STP's full-time workforce, centralized database, and testing and vaccination programs. We evaluate the program's success in controlling COVID-19 during the 2020-2021 academic school year. Results: COVID-19 cases rose among the campus community in the first few weeks of campus reopening in Fall 2020. Test positivity levels returned to prefall semester levels within one month, however. A few additional, yet smaller, waves occurred during the 2020-2021 school year and were successfully controlled without any campus-wide closures. Conclusions: This program may serve as a framework for other institutions managing the ongoing COVID-19 crisis, in addition to setting the standard for programmatic management of future emerging infectious diseases at universities.

Multi-Faceted Approach to COVID-19 Surveillance at a Large University Campus in the Southeastern United States.

The coronavirus disease 2019 (COVID-19) pandemic continues to present unique public health challenges both within the United States and across the globe. Institutions of higher learning are tasked with preventing and responding to COVID-19 on campus while also considering implications for the surrounding communities. The process of re-opening campus, whether at full or partial capacity, has tasked these institutions with overcoming complex challenges associated with balancing the resumption of campus operations while simultaneously protecting university affiliates and surrounding community members from COVID-19 through robust surveillance, contact tracing, and testing efforts. Here, we provide a concise outline related to the development and implementation of the comprehensive and sustainable COVID-19 surveillance program at the University of Florida. We also critically discuss the successes and pitfalls of this program while also providing recommendations for the development of similar programs in the future.

Longitudinal trajectories of HIV care engagement since diagnosis among persons with HIV in the Florida Ryan White program.

HIV care engagement is a dynamic process. We employed group-based trajectory modeling to examine longitudinal patterns in care engagement among people who were newly diagnosed with HIV and enrolled in the Ryan White program in Florida (n = 9,755) between 2010 and 2015. Five trajectories were identified (47.9% "in care" with 1-2 care visit(s) per 6 months, 18.0% "frequent care" with 3 or more care visits per 6 months, 11.0% "re-engage", 11.0% "gradual drop out", 12.6% "early dropout") based on the number of care attendances (including outpatient/case management visits, viral load or CD4 test) for each six-month during the first five years since diagnosis. Relative to "in care", people in the "frequent care" trajectory were more likely to be Hispanic/Latino and older at HIV diagnosis, whereas people in the three suboptimal care retention trajectories were more likely to be younger. Area deprivation index, rurality, and county health rankings were also strongly associated with care trajectories. Individual- and community-level factors associated to the three suboptimal care retention trajectories, if confirmed to be causative and actionable, could be prioritized to improve HIV care engagement.

Low-frequency variants in mildly symptomatic vaccine breakthrough infections presents a doubled-edged sword.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) has raised questions regarding vaccine protection against SARS-CoV-2 infection, transmission, and ongoing virus evolution. Twenty-three mildly symptomatic "vaccination breakthrough" infections were identified as early as January 2021 in Alachua County, Florida, among individuals fully vaccinated with either the BNT162b2 (Pfizer) or the Ad26 (Janssen/J&J) vaccines. SARS-CoV-2 genomes were successfully generated for 11 of the vaccine breakthroughs, and 878 individuals in the surrounding area and were included for reference-based phylogenetic investigation. These 11 individuals were characterized by infection with VOCs, but also low-frequency variants present within the surrounding population. Low-frequency mutations were observed, which have been more recently identified as mutations of interest owing to their location within targeted immune epitopes (P812L) and association with increased replicative capacity (L18F). We present these results to posit the nature of the efficacy of vaccines in reducing symptoms as both a blessing and a curse-as vaccination becomes more widespread and self-motivated testing reduced owing to the absence of severe symptoms, we face the challenge of early recognition of novel mutations of potential concern. This case study highlights the critical need for continued testing and monitoring of infection and transmission among individuals regardless of vaccination status.

Evaluating the Diagnostic Paradigm for Group A and Non-Group A Streptococcal Pharyngitis in the College Student Population.

BACKGROUND: Acute pharyngitis is a frequent illness presenting in outpatient settings. Antibiotics are only recommended for bacterial pharyngitis caused by group A ß-hemolytic streptococci (GAS); however, infections with non-group A ß-hemolytic streptococci (NGAS) have similar clinical presentations and are common in young adult populations. The objective of this study was to analyze the performance of a current (expert) diagnostic algorithm for GAS pharyngitis, the Centor score, and compare it to alternative models developed to predict GAS and NGAS in a college student population. METHODS: Electronic health records were obtained for all patients who received a streptococcal rapid antigen detection test (RADT) and/or a bacterial throat culture (n = 3963) at a southeastern US university in 2014. Bivariate and multivariable regression models (least absolute shrinkage and selection operator [LASSO] and stepwise-selected) were fitted to assess and compare their diagnostic performances for GAS-positive and NGAS-positive infections. RESULTS: Prevalence of GAS was 18.8%. In the subset of RADT-negative patients who received bacterial throat cultures (n = 313), growth of NGAS occurred in 34.8%, with group C streptococci the most frequent isolate. Mean Centor score was higher for NGAS (3.2) vs GAS (2.9) infections (P = .0111). The area under the curve (AUC) for GAS prediction was 0.64 using the Centor score and 0.70 using the LASSO model. For NGAS, the most important features were cough, pharyngeal erythema, tonsillar exudate, and gastrointestinal symptoms (AUC = 0.63). CONCLUSIONS: GAS and NGAS pharyngitis were indistinguishable among college students in this study utilizing a commonly applied decision score. Alternative models using additional clinical criteria may be useful for supporting diagnosis of this common illness.

Challenges in replicating secondary analysis of electronic health records data with multiple computable phenotypes: A case study on methicillin-resistant Staphylococcus aureus bacteremia infections.

BACKGROUND: Replication of prediction modeling using electronic health records (EHR) is challenging because of the necessity to compute phenotypes including study cohort, outcomes, and covariates. However, some phenotypes may not be easily replicated across EHR data sources due to a variety of reasons such as the lack of gold standard definitions and documentation variations across systems, which may lead to measurement error and potential bias. Methicillin-resistant Staphylococcus aureus (MRSA) infections are responsible for high mortality worldwide. With limited treatment options for the infection, the ability to predict MRSA outcome is of interest. However, replicating these MRSA outcome prediction models using EHR data is problematic due to the lack of well-defined computable phenotypes for many of the predictors as well as study inclusion and outcome criteria. OBJECTIVE: In this study, we aimed to evaluate a prediction model for 30-day mortality after MRSA bacteremia infection diagnosis with reduced vancomycin susceptibility (MRSA-RVS) considering multiple computable phenotypes using EHR data. METHODS: We used EHR data from a large academic health center in the United States to replicate the original study conducted in Taiwan. We derived multiple computable phenotypes of risk factors and predictors used in the original study, reported stratified descriptive statistics, and assessed the performance of the prediction model. RESULTS: In our replication study, it was possible to (re)compute most of the original variables. Nevertheless, for certain variables, their computable phenotypes can only be approximated by proxy with structured EHR data items, especially the composite clinical indices such as the Pitt bacteremia score. Even computable phenotype for the outcome variable was subject to variation on the basis of the admission/discharge windows. The replicated prediction model exhibited only a mild discriminatory ability. CONCLUSION: Despite the rich information in EHR data, replication of prediction models involving complex predictors is still challenging, often due to the limited availability of validated computable phenotypes. On the other hand, it is often possible to derive proxy computable phenotypes that can be further validated and calibrated.

Gut microbiome, body weight, and mammographic breast density in healthy postmenopausal women.

PURPOSE: We examined gut microbiome (GM) profiles in relation to mammographic breast density (BD) and body mass index (BMI) in healthy postmenopausal women. METHODS: Eligible women were postmenopausal, had a BMI ≤ 35 kg/m2, and had not recently taken oral/IV antibiotics. All women provided a fecal sample and information on breast cancer risk factors. Mammographic BD was classified with the American College of Radiology's BI-RADS BD classification system. Bacterial DNA was isolated from fecal samples and the V1-V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of GM with indices of within-sample (alpha) diversity and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with BD and BMI. RESULTS: Among 69 women with BD data, 39 had low BD (BI-RADS I/II) and 30 had high BD (BI-RADS III/IV). BMI was inversely associated with BD (mean BMI = 23.8 and 28.0 in women with high and low BD, respectively, p = 1.07 × 10-5). Similar levels of GM diversity were found across weight groups according to Shannon (p = 0.83); Inverse Simpson (p = 0.97); and Chao1 (p = 0.31) indices. F/B ratio and microbiota diversity were suggestively greater in women with high vs. low BD (p = 0.35, 0.14, 0.15, and 0.17 for F/B ratio, Shannon, Inverse Simpson and Chao1, respectively). CONCLUSION: Suggestive differences observed in women with high and low BD with respect to GM alpha diversity and prevalence of specific GM taxa need to be confirmed in larger studies.

Brain tissue transcriptomic analysis of SIV-infected macaques identifies several altered metabolic pathways linked to neuropathogenesis and poly (ADP-ribose) polymerases (PARPs) as potential therapeutic targets.

Despite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals' quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. We investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n = 11), with or without CD8+ lymphocyte depletion, based on detectable (n = 6) or non-detectable (n = 5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. Our results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.

Sociodemographic, Ecological, and Spatiotemporal Factors Associated with Human Immunodeficiency Virus Drug Resistance in Florida: A Retrospective Analysis.

BACKGROUND: Persons living with human immunodeficiency virus (HIV) with resistance to antiretroviral therapy are vulnerable to adverse HIV-related health outcomes and can contribute to transmission of HIV drug resistance (HIVDR) when nonvirally suppressed. The degree to which HIVDR contributes to disease burden in Florida-the US state with the highest HIV incidence- is unknown. METHODS: We explored sociodemographic, ecological, and spatiotemporal associations of HIVDR. HIV-1 sequences (n = 34 447) collected during 2012-2017 were obtained from the Florida Department of Health. HIVDR was categorized by resistance class, including resistance to nucleoside reverse-transcriptase , nonnucleoside reverse-transcriptase , protease , and integrase inhibitors. Multidrug resistance and transmitted drug resistance were also evaluated. Multivariable fixed-effects logistic regression models were fitted to associate individual- and county-level sociodemographic and ecological health indicators with HIVDR. RESULTS: The HIVDR prevalence was 19.2% (nucleoside reverse-transcriptase inhibitor resistance), 29.7% (nonnucleoside reverse-transcriptase inhibitor resistance), 6.6% (protease inhibitor resistance), 23.5% (transmitted drug resistance), 13.2% (multidrug resistance), and 8.2% (integrase strand transfer inhibitor resistance), with significant variation by Florida county. Individuals who were older, black, or acquired HIV through mother-to-child transmission had significantly higher odds of HIVDR. HIVDR was linked to counties with lower socioeconomic status, higher rates of unemployment, and poor mental health. CONCLUSIONS: Our findings indicate that HIVDR prevalence is higher in Florida than aggregate North American estimates with significant geographic and socioecological heterogeneity.