Inadequate Engagement in HIV Care Among People With HIV Newly Diagnosed With a Sexually Transmitted Disease: A Multijurisdictional Analysis.

BACKGROUND: A key challenge of HIV surveillance-based HIV care reengagement is locating people living with HIV (PLWH) who seem to be out of care to reengage them in care. Providing reengagement services to PLWH diagnosed with a sexually transmitted disease (STD)-individuals who are in jurisdiction and connected to the health care system-could be an efficient means of promoting HIV treatment and reducing HIV transmission. METHODS: Early and late syphilis (ES/LS) and gonorrhea (GC) cases diagnosed in 2016 and 2017 in Louisiana, Michigan, Mississippi, Oregon, Rhode Island, and Texas were matched to each state's HIV surveillance data to determine the proportion of PLWH with these infections who (1) did not have evidence of a CD4 count or viral load in the prior ≥13 months (out of care) or (2) had a viral load ≥1500 copies/mL on their most recent HIV RNA test before STD diagnosis (viremic). RESULTS: Previously diagnosed HIV infection was common among persons diagnosed with ES (n = 6942; 39%), LS (n = 4329; 27%), and GC (n = 9509; 6%). Among these ES, LS, and GC cases, 26% (n = 1543), 33% (n = 1113), and 29% (n = 2391) were out of HIV medical care or viremic at the time of STD diagnosis. CONCLUSIONS: A large proportion of STD cases with prior HIV diagnosis are out of care or viremic. Integrating relinkage to care activities into STD partner services and/or the use of matching STD and HIV data systems to prioritize data to care activities could be an efficient means for relinking patients to care and promoting viral suppression.

Single-Dose Compared With Multidose Metronidazole for the Treatment of Trichomoniasis in Women: A Meta-Analysis.

BACKGROUND: Trichomonas vaginalis is the most common curable sexually transmitted infection worldwide. Although the Centers for Disease Control and Prevention and the World Health Organization recommend a single 2-g dose of metronidazole for the first line of treatment for T. vaginalis among human immunodeficiency virus (HIV) negative women, high rates of repeat infections are found. The purpose of this meta-analysis was to compare treatment failure between single versus multidose metronidazole for the treatment of T. vaginalis. METHODS: A systematic literature search was performed using search terms including metronidazole AND trichomoniasis AND women. Embase, MEDLINE, and Clinicaltrials.gov were used to search for relevant studies as well as hand searching relevant articles. These databases were last searched on January 25, 2016. To be included in this meta-analysis, the study had to be a clinical trial, evaluate T. vaginalis, use oral metronidazole, and compare single dose metronidazole to multidose metronidazole. RESULTS: There were 487 articles that were assessed for relevance and quality. Of these articles, 6 met the eligibility criteria and were included in the final results. The pooled risk ratio indicated higher treatment failure for single dose compared to multidose 1.87 (95% confidence interval, 1.23-2.82; P < 0.01). When the one study that included HIV+ women was excluded from analysis, the findings were similar with a pooled risk ratio of 1.80 (95% confidence interval, 1.07-3.02; P < 0.03). CONCLUSIONS: Centers for Disease Control and Prevention recently changed treatment recommendations for HIV+ women to multidose rather than single-dose. These data suggest that those recommendations should be considered for all women.

Not all clusters are equal: dynamics of molecular HIV-1 clusters in a statewide Rhode Island epidemic.

OBJECTIVES: Molecular epidemiology is a powerful tool to characterize HIV epidemics and prioritize public health interventions. Typically, HIV clusters are assumed to have uniform patterns over time. We hypothesized that assessment of cluster evolution would reveal distinct cluster behavior, possibly improving molecular epidemic characterization, towards disrupting HIV transmission. DESIGN: Retrospective cohort. METHODS: Annual phylogenies were inferred by cumulative aggregation of all available HIV-1 pol sequences of individuals with HIV-1 in Rhode Island (RI) between 1990 and 2020, representing a statewide epidemic. Molecular clusters were detected in annual phylogenies by strict and relaxed cluster definition criteria, and the impact of annual newly-diagnosed HIV-1 cases to the structure of individual clusters was examined over time. RESULTS: Of 2153 individuals, 31% (strict criteria) - 47% (relaxed criteria) clustered. Longitudinal tracking of individual clusters identified three cluster types: normal, semi-normal and abnormal. Normal clusters (83-87% of all identified clusters) showed predicted growing/plateauing dynamics, with approximately three-fold higher growth rates in large (15-18%) vs. small (∼5%) clusters. Semi-normal clusters (1-2% of all clusters) temporarily fluctuated in size and composition. Abnormal clusters (11-16% of all clusters) demonstrated collapses and re-arrangements over time. Borderline values of cluster-defining parameters explained dynamics of non-normal clusters. CONCLUSIONS: Comprehensive tracing of molecular HIV clusters over time in a statewide epidemic identified distinct cluster types, likely missed in cross-sectional analyses, demonstrating that not all clusters are equal. This knowledge challenges current perceptions of consistent cluster behavior over time and could improve molecular surveillance of local HIV epidemics to better inform public health strategies.

An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic.

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.

Beyond HIV outbreaks: protocol, rationale and implementation of a prospective study quantifying the benefit of incorporating viral sequence clustering analysis into routine public health interventions.

INTRODUCTION: HIV continues to have great impact on millions of lives. Novel methods are needed to disrupt HIV transmission networks. In the USA, public health departments routinely conduct contact tracing and partner services and interview newly HIV-diagnosed index cases to obtain information on social networks and guide prevention interventions. Sequence clustering methods able to infer HIV networks have been used to investigate and halt outbreaks. Incorporation of such methods into routine, not only outbreak-driven, contact tracing and partner services holds promise for further disruption of HIV transmissions. METHODS AND ANALYSIS: Building on a strong academic-public health collaboration in Rhode Island, we designed and have implemented a state-wide prospective study to evaluate an intervention that incorporates real-time HIV molecular clustering information with routine contact tracing and partner services. We present the rationale and study design of our approach to integrate sequence clustering methods into routine public health interventions as well as related important ethical considerations. This prospective study addresses key questions about the benefit of incorporating a clustering analysis triggered intervention into the routine workflow of public health departments, going beyond outbreak-only circumstances. By developing an intervention triggered by, and incorporating information from, viral sequence clustering analysis, and evaluating it with a novel design that avoids randomisation while allowing for methods comparison, we are confident that this study will inform how viral sequence clustering analysis can be routinely integrated into public health to support the ending of the HIV pandemic in the USA and beyond. ETHICS AND DISSEMINATION: The study was approved by both the Lifespan and Rhode Island Department of Health Human Subjects Research Institutional Review Boards and study results will be published in peer-reviewed journals.

Statewide Longitudinal Trends in Transmitted HIV-1 Drug Resistance in Rhode Island, USA.

BACKGROUND: HIV-1 transmitted drug resistance (TDR) remains a global challenge that can impact care, yet its comprehensive assessment is limited and heterogenous. We longitudinally characterized statewide TDR in Rhode Island. METHODS: Demographic and clinical data from treatment-naïve individuals were linked to protease, reverse transcriptase, and integrase sequences routinely obtained over 2004-2020. TDR extent, trends, impact on first-line regimens, and association with transmission networks were assessed using the Stanford Database, Mann-Kendall statistic, and phylogenetic tools. RESULTS: In 1123 individuals, TDR to any antiretroviral increased from 8% (2004) to 26% (2020), driven by non-nucleotide reverse transcriptase inhibitor (NNRTI; 5%-18%) and, to a lesser extent, nucleotide reverse transcriptase inhibitor (NRTI; 2%-8%) TDR. Dual- and triple-class TDR rates were low, and major integrase strand transfer inhibitor resistance was absent. Predicted intermediate to high resistance was in 77% of those with TDR, with differential suppression patterns. Among all individuals, 34% were in molecular clusters, some only with members with TDR who shared mutations. Among clustered individuals, people with TDR were more likely in small clusters. CONCLUSIONS: In a unique (statewide) assessment over 2004-2020, TDR increased; this was primarily, but not solely, driven by NNRTIs, impacting antiretroviral regimens. Limited TDR to multiclass regimens and pre-exposure prophylaxis are encouraging; however, surveillance and its integration with molecular epidemiology should continue in order to potentially improve care and prevention interventions.

The statin class of HMG-CoA reductase inhibitors demonstrate differential activation of the nuclear receptors PXR, CAR and FXR, as well as their downstream target genes.

The therapeutic class of HMG-CoA reductase inhibitors, the statins are central agents in the treatment of hypercholesterolaemia and the associated conditions of cardiovascular disease, obesity and metabolic syndrome. Although statin therapy is generally considered safe, a number of known adverse effects do occur, most commonly treatment-associated muscular pain. In vitro evidence also supports the potential for drug-drug interactions involving this class of agents, and to examine this a ligand-binding assay was used to determine the ability of six clinically used statins for their ability to directly activate the nuclear receptors pregnane X-receptor (PXR), farnesoid X-receptor (FXR) and constitutive androstane receptor (CAR), demonstrating a relative activation of PXR>FXR>CAR. Using reporter gene constructs, we demonstrated that this order of activation is mirrored at the transcriptional activation level, with PXR-mediated gene activation being pre-eminent. Finally, we described a novel regulatory loop, whereby activation of FXR by statins increases PXR reporter gene expression, potentially enhancing PXR-mediated responses. Delineating the molecular interactions of statins with nuclear receptors is an important step in understanding the full biological consequences of statin exposure. This demonstration of their ability to directly activate nuclear receptors, leading to nuclear receptor cross-talk, has important potential implications for their use within a polypharmacy paradigm.

Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.

BACKGROUND: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.

Public Health Approaches Toward Eliminating Hepatitis C Virus in Rhode Island.

Hepatitis C Virus (HCV) continues to be a cause of significant morbidity and mortality around the world surpassing HIV, Tuberculosis and Malaria as the leading cause of death by an infectious disease. In the United States, advances in screening, testing and treatment have put the goal set by the World Health Organization (WHO) to HCV elimination within reach. Rhode Island has taken an innovative public health approach to eliminating HCV by improving disease surveillance activities, supporting disease reduction strategies and removing barriers across the continuum of care, particularly for populations that are disproportionately impacted by the disease. Through the coordination of the Rhode Island Hepatitis C Action Coalition, the Rhode Island Department of Health (RIDOH), the Executive Office of Health and Human Services (EOHHS), community organizations, and clinical leaders, important steps have been taken to reduce transmission of the disease and work toward HCV elimination.

In silico and in vitro modeling of hepatocyte drug transport processes: importance of ABCC2 expression levels in the disposition of carboxydichlorofluroscein.

The impact of transport proteins in the disposition of chemicals is becoming increasingly evident. Alteration in disposition can cause altered pharmacokinetic and pharmacodynamic parameters, potentially leading to reduced efficacy or overt toxicity. We have developed a quantitative in silico model, based upon literature and experimentally derived data, to model the disposition of carboxydichlorofluroscein (CDF), a substrate for the SLCO1A/B and ABCC subfamilies of transporters. Kinetic parameters generated by the in silico model closely match both literature and experimentally derived kinetic values, allowing this model to be used for the examination of transporter action in primary rat hepatocytes. In particular, we show that the in silico model is suited to the rapid, accurate determination of K(i) values, using 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) as a prototypical pan-ABCC inhibitor. In vitro-derived data are often used to predict in vivo response, and we have examined how differences in protein expression levels between these systems may affect chemical disposition. We show that ABCC2 and ABCC3 are overexpressed in sandwich culture hepatocytes by 3.5- and 2.3-fold, respectively, at the protein level. Correction for this in markedly different disposition of CDF, with the area under the concentration versus time curve and C(max) of intracellular CDF increasing by 365 and 160%, respectively. Finally, using kinetic simulations we show that ABCC2 represents a fragile node within this pathway, with alterations in ABCC2 having the most prominent effects on both the K(m) and V(max) through the pathway. This is the first demonstration of the utility of modeling approaches to estimate the impact of drug transport processes on chemical disposition.

Health Equity, Social Justice, and HIV in Rhode Island: A Contemporary Challenge.

From its beginning, HIV has primarily affected marginalized populations, such as injection drug users, gay, bisexual and other men who have sex with men (GBMSM), and minority racial and ethnic groups. HIV is a disease that, from the start, has been strongly influenced by issues related to social justice and health equity due to its intersection with behaviors among at-risk populations. While some of the risks associated with HIV have been successfully mitigated through social justice initiatives related to needle exchange programs and routine HIV testing of pregnant women, Rhode Island remains confronted with the health equity challenges of preventing HIV transmission and ensuring access to HIV care/treatment, especially for Black/African Americans, Hispanics, and GBMSM. [Full article available at http://rimed.org/rimedicaljournal-2016-11.asp].

Histologic examination of the eye of acid-sensing ion channel 1a knockout mice.

Various subtypes of the acid sensing ion channel have been detected in the retina of rodents and other mammalian species, but the functional importance of this finding is not clearly understood. The purpose of the study was to determine if retinal degeneration was present in ASIC1a-/- mice. The eyes of ASIC1a-/- mice, heterozygote ASIC1a+/- mice, and wild type ASIC1a+/+ mice that were 5 or 22-27 weeks old were processed by routine histotech-nological methods and examined for histologic changes in the retina and other portions of the eye. Additional sections of eyes from ASIC1a-/- and ASIC1a+/+ mice were labeled with peanut agglutinin (PNA) to evaluate cone pho-toreceptors. The retinas of ASIC1a-/-, ASIC1a+/-, and ASIC1a+/+ mice at 5 or 22-27 weeks of age were unremarkable and no morphologic changes in cone photo receptors were detected. Additional findings detected in the eye of ASIC1a+/+ mice included swelling of lens fibers or cataract that were also detected in some of the ASIC1a-/- or ASIC1a+/- mice. Lenticular findings were not considered to be associated with an absence of ASIC1a.