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Increasing naloxone access in communities has been a priority to mitigate the increasing rate of opioid-related overdose deaths.The aims of this telephone survey were to estimate the availability of naloxone furnishing (provided without a prescription) by community pharmacists in California and examine the changes that occurred between 2018 and 2020.A telephone audit of a random representative sample of 1271 California licensed community pharmacies was conducted from January 22, 2020, to February 24, 2020. The results were compared with those of a survey of 1147 California licensed community pharmacies that was conducted from January 23, 2018, to February 28, 2018. The primary outcomes measured were naloxone availability without a prescription, information on formulations, cost, insurance billing, and stocking status.There was a statistically significant increase in the furnishing of naloxone, as well as stocking and billing, in California from 2018 to 2020. Although fewer than half of the pharmacies were willing to provide naloxone without a prescription in 2020 (n = 487, 42.4%), this was an 80% increase from 2018 (P0.001). Of the pharmacies furnishing naloxone, many (n = 399, 81.9%) had nasal naloxone in stock, a large and statistically significant increase from 2018 when only 50.6% reported having it in stock (P0.001). In 2020, 90% of the pharmacies reported correctly that pharmacist-furnished naloxone could be billed to insurance compared with 56.9% in 2018 (P0.001). The median cash price of nasal naloxone (pack of 2) at chain pharmacies in 2020 was $131 (interquartile range [IQR] $129-$138) compared with $153 (IQR, $141-$163; P = 0.001) at independent pharmacies.Community pharmacy-based access to naloxone increased in a statistically significant manner in California, although more than half of the pharmacies still do not provide such access. This study demonstrates the need for further efforts to expand community pharmacy-based access to naloxone.

Background: To examine the contribution of B-cell activation molecules to B-cell follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), a prospective study was conducted using pre-diagnosis serial serum samples from the US Department of Defense Serum Repository. Methods: Each case (n = 142 FL, n = 211 DLBCL) was matched to two controls on age, gender, race, military branch, and blood collection dates. Immune activation molecules (IL1β, IL2, IL4, IL5, IL6, IL10, IL12, CXCL13, IL8, TNFα, IFNγ, GM-CSF, VEGF, sCD30, IgE) were quantified using ELISA or multiplex immunometric (Luminex) assay. Longitudinal data were analyzed using linear mixed modeling. As serial specimens were collected over several years before diagnosis, we evaluated the temporal dynamics of these markers. Results: Increased serum levels of sCD30, CXCL13, and to a lesser extent IL10, were associated with both FL and DLBCL in cases compared with controls, with a median follow-up of 5.5 years from the earliest specimen collection to diagnosis date. Significant increasing sCD30 and CXCL13 trajectories for FL and DLBCL subtypes were noted starting at the earliest time points and with IL10 levels increasing significantly at time points closer to diagnosis. Conclusions: These results suggest that sCD30, CXCL13, and IL10 may contribute to the etiology of FL and DLBCL and are potential biomarkers for these non–Hodgkin lymphoma subtypes. Impact: The increasing trajectories of the B-cell activation molecules, sCD30, CXCL13, and to a lesser extent IL10, may indicate early disease-induced effects or reflect the chronic stimulation of B-cells that promotes the development of FL and DLBCL subtypes.

Purpose Long-acting formulations of the potent antiretroviral prodrug tenofovir alafenamide (TAF) hold potential as biomedical HIV prevention modalities. Here, we present a rigorous comparison of three animal models, C57BL/6 J mice, beagle dogs, and merino sheep for evaluating TAF implant pharmacokinetics (PKs). Methods Implants delivering TAF over a wide range of controlled release rates were tested in vitro and in mice and dogs. Our existing PK model, supported by an intravenous (IV) dosing dog study, was adapted to analyze mechanistic aspects underlying implant TAF delivery. Results TAF in vitro release in the 0.13 to 9.8 mg d−1 range with zero order kinetics were attained. Implants with equivalent fabrication parameters released TAF in mice and sheep at rates that were not statistically different, but were 3 times higher in dogs. When two implants were placed in the same subcutaneous pocket, a two-week creep to Cmax was observed in dogs for systemic drug and metabolite concentrations, but not in mice. Co-modeling IV and TAF implant PK data in dogs led to an apparent TAF bioavailability of 9.6 in the single implant groups (compared to the IV group), but only 1.5 when two implants were placed in the same subcutaneous pocket. Conclusions Based on the current results, we recommend using mice and sheep, with macaques as a complementary species, for preclinical TAF implant evaluation with the caveat that our observations may be specific to the implant technology used here. Our report provides fundamental, translatable insights into multispecies TAF delivery via long-acting implants.

Bone morphogenetic protein-9 (BMP-9) is a circulating cytokine that is known to play an essential role in the endothelial homeostasis and the binding of BMP-9 to the receptor activin-like kinase 1 (ALK-1) promotes endothelial cell quiescence. Previously, using an unbiased screen, we identified ALK-1 as a high-capacity receptor for low-density lipoprotein (LDL) in endothelial cells that mediates its transcytosis in a nondegradative manner. Here we examine the crosstalk between BMP-9 and LDL and how it influences their interactions with ALK-1. Treatment of endothelial cells with BMP-9 triggers the extensive endocytosis of ALK-1, and it is mediated by caveolin-1 (CAV-1) and dynamin-2 (DNM2) but not clathrin heavy chain. Knockdown of CAV-1 reduces BMP-9-mediated internalization of ALK-1, BMP-9-dependent signaling and gene expression. Similarly, treatment of endothelial cells with LDL reduces BMP-9-induced SMAD1/5 phosphorylation and gene expression and silencing of CAV-1 and DNM2 diminishes LDL-mediated ALK-1 internalization. Interestingly, BMP-9-mediated ALK-1 internalization strongly re-duces LDL transcytosis to levels seen with ALK-1 deficiency. Thus, BMP-9 levels can control cell surface levels of ALK-1, via CAV-1, to regulate both BMP-9 signaling and LDL transcytosis.

Transporters are specialized integral membrane proteins, which mediate the passage of virtually all molecules through cell membranes. They are expressed in a broad range of human and animal tissues and play important roles in both normal and disease states. For these reasons, they are evaluated when developing and testing drugs. Two major families of drug transporters, the adenosine 5'-triphosphate-binding cassette and solute carrier transporters (SLC), have critical roles in the absorption, distribution, metabolism, and elimination of drugs. The SLC family contains known nucleoside transporters and therefore are important when nucleoside analogs are used as drugs to prevent or treat viral infections. In this study, we wanted to determine if it was possible to locate one member of the SLC family, the human concentrative nucleoside transporter 3 (CNT3) in human vaginal epithelial cells. The CNT3 protein has important roles in drug delivery, subsequent drug tissue distribution, and, hence, efficacy. Vaginal epithelial cells, taken from two human volunteers (one Caucasian and one African American), were labeled for light and electron microscopy, with a commercial antibody to a cytoplasmic domain of CNT3, the protein product of the SLC28A3 gene. Fluorescent secondary antibodies or protein A-gold were used to detect antibody binding. By electron microscopy, gold particle binding was quantified to determine labeling specificity. By light microscopy, positive labeling with anti-CNT3 antibodies was detected on human vaginal epithelial cells, but specificity to any intracellular structure was not easily determined, most likely a result of specimen preparation. Electron microscopy revealed that the CNT3 transporter protein was present predominantly on microvilli located on one side of some human vaginal epithelial cells. Quantification confirmed specific anti-CNT3 labeling over human vaginal epithelial cell microvilli. The CNT3 protein, present in the microvilli of human vaginal epithelial cells, may have a role in redistributing nucleoside homologues delivered to the vaginal tract. Transporter proteins such as CNT3 could shuttle nucleosides and their analogs through the vaginal epithelium to immune cells located in lower cell layers. Outer layers of cells, which are eventually shed from the epithelium, may remove accumulated nucleoside drug analogs from the vaginal tract.

Background: Epigenetic aging is accelerated in tissues of persons living with HIV (PLWH) and may underlie the early onset of age-related illnesses. This study examines the rate-of-change in epigenetic age in PLWH following HIV infection but before HAART, using archived longitudinal samples from the Multicenter AIDS Cohort Study.Methods: DNA was isolated from cryopreserved peripheral blood mononuclear cells from 101 men living with HIV, with baseline visit Results: Epigenetic age increased approximately twice as fast in PLWH as uninfected controls (Pan-tissue, Extrinsic, and Phenotypic clocks). Shortening of DNAmTL was nearly 3-fold faster in PLWH than controls. Faster rate-of-aging was associated with HIV status (Pan-Tissue, Extrinsic, Phenotypic, and DNAmTL), white race (Extrinsic, DNAmTL), higher cumulative HIV viral load (Grim), and lower baseline DNAm age (Phenotypic, Skin & Blood).Conclusion: Epigenetic rates-of-aging were significantly faster for untreated PLWH. Our findings expand on the important impact of HIV infection on biologic aging, both in elevating epigenetic age and increasing the rate-of-aging in the years following infection.

Males and certain racial/ethnic minority groups have borne a disproportionate burden of COVID-19 mortality in the United States, and substantial scientific research has sought to quantify and characterize population-level disparities in COVID-19 mortality outcomes by sex and across categories of race/ethnicity. However, there has not yet been a national population-level study to quantify disparities in COVID-19 mortality outcomes across the intersection of these demographic dimensions. Here, we analyze a publicly available dataset from the National Center for Health Statistics comprising COVID-19 death counts stratified by race/ethnicity, sex, and age for the year 2020, calculating mortality rates for each race/ethnicity-sex-age stratum and age-adjusted mortality rates for each race/ethnicity-sex stratum, quantifying disparities in terms of mortality rate ratios and rate differences. Our results reveal persistently higher COVID-19 age-adjusted mortality rates for males compared to females within every racial/ethnic group, with notable variation in the magnitudes of the sex disparity by race/ethnicity. However, non-Hispanic Black, Hispanic, and non-Hispanic American Indian or Alaska Native females have higher age-adjusted mortality rates than non-Hispanic White and non-Hispanic Asian/Pacific Islander males. Moreover, persistent racial/ethnic disparities are observed among both males and females, with higher COVID-19 age-adjusted mortality rates observed for non-Hispanic Blacks, Hispanics, and non-Hispanic American Indian or Alaska Natives relative to non-Hispanic Whites.

Background Adverse mental and emotional health outcomes are increasingly recognized as a public health challenge associated with the COVID-19 pandemic. Objective The goal of this study was to examine the association of COVID-19 risk misperceptions with self-reported household isolation, a potential risk factor for social isolation and loneliness. Methods We analyzed data from the Franklin Templeton-Gallup Economics of Recovery Study (July to December 2020) of 24,649 US adults. We also analyzed data from the Gallup Panel (March 2020 to February 2021), which included 123,516 observations about loneliness. The primary outcome was self-reported household isolation, which we defined as a respondent having no contact or very little contact with people outside their household, analogous to quarantining. Results From July to December 2020, 53% to 57% of respondents reported living in household isolation. Most participants reported beliefs about COVID-19 health risks that were inaccurate, and overestimation of health risk was most common. For example, while deaths in persons younger than 55 years old accounted for 7% of total US deaths, respondents estimated that this population represented 43% of deaths. Overestimating COVID-19 health risks was associated with increased self-reported household isolation, with percentage differences ranging from 5.6 to 11.8 (P Conclusions Pandemic-related harms to emotional and mental well-being may be attenuated by reducing risk overestimation and household isolation preferences that exceed public health guidelines.

Males are at higher risk relative to females of severe outcomes following COVID-19 infection. Focusing on COVID-19-attributable mortality in the United States (U.S.), we quantified and contrasted years of potential life lost (YPLL) attributable to COVID-19 by sex based on data from the U.S. National Center for Health Statistics as of 31 March 2021, specifically by contrasting male and female percentages of total YPLL with their respective percent population shares and calculating age-adjusted male-to-female YPLL rate ratios, both nationally and for each of the 50 states and the District of Columbia. Using YPLL before age 75 to anchor comparisons between males and females and a novel Monte Carlo simulation procedure to perform estimation and uncertainty quantification, our results reveal a near-universal pattern across states of higher COVID-19-attributable YPLL among males compared to females. Furthermore, the disproportionately high COVID-19 mortality burden among males is generally more pronounced when measuring mortality burden in terms of YPLL compared to death counts, reflecting dual phenomena of males dying from COVID-19 at higher rates and at systematically younger ages relative to females. The U.S. COVID-19 epidemic also offers lessons underscoring the importance of cultivating a public health environment that recognizes sex-specific needs as well as different patterns in risk factors, health behaviors, and responses to interventions between men and women. Public health strategies incorporating focused efforts to increase COVID-19 vaccinations among men are particularly urged.

Objective:To estimate latent dietary profiles in a community-dwelling sample of older Americans and identify associations between dietary profile membership and individual demographic, socio-economic and health characteristics.Design:Secondary analysis of the 2012 Health and Retirement Study (HRS) and linked 2013 Health Care and Nutrition Study (HCNS). Latent profile analysis identified mutually exclusive subgroups of dietary intake and bivariate analyses examined associations between dietary profile membership, participant characteristics and nutrient intakes.Setting:USA.Participants:An analytic sample of 3558 adults aged 65 years or older.Results:Four dietary profiles were identified with 15·5 % of the sample having a ‘Healthy’ diet, 42·0 % consuming a ‘Western’ diet, 29·7 % having a diet consisting of high intake of all food groups and 12·7 % reporting relatively low intake of all food groups. Members of the ‘Healthy’ profile reported the greatest socio-economic resources and health, and members of the ‘Low Intake’ profile had the fewest resources and worst health outcomes. Macronutrient and micronutrient intakes varied across profile although inadequate and excessive intakes of selected nutrients were observed for all profiles.Conclusions:We identified dietary patterns among older Americans typified by either selective intake of foods or overall quantity of foods consumed, with those described as ‘Low Intake’ reporting the fewest socio-economic resources, greatest risk of food insecurity and the worst health outcomes. Limitations including the presence of measurement error in dietary questionnaires are discussed. The causes and consequences of limited dietary intake among older Americans require further study and can be facilitated by the HRS and HCNS.

BackgroundThe COVID-19 pandemic has challenged even the most robust public health systems world-wide, leaving state and local health departments, hospitals, and physicians with little guidance on planning and resource allocation. Efforts at predicting the virus’ spread have largely failed to capture the nuances presented by national and local geographic, environmental, and sociological variables.ObjectiveUsing county-level data from the United States, we sought to measure the extent to which these demographic, geographic, and environmental variables correlate with the spread of COVID-19.MethodsUsing demographic data from the US Census Bureau’s American Community Survey, weather station data from the National Oceanic and Atmospheric Administration (NOAA), and COVID-19 case data from the Center for Systems Science and Engineering at Johns Hopkins University and the New York State Department of Health, we employed Bayesian hierarchical modeling with zero-inflated Negative Binomial regression to calculate correlations between these variables, COVID-19 case count, and rate of viral spread. Key predictors were identified and measured during two periods of two weeks each: March and June of 2020. The resultant model was then employed to predict case counts and spread rate for early July 2020.ResultsWhile demographic and environmental factors explain viral spread well, our findings challenge earlier conclusions about how these factors related to viral progress. Using these factors alone, we were able to predict spread to within 1% in all but 8 counties (99.9%), and within 0.1% in all but 51 counties (98.4%). The model was subsequently able to predict early July viral spread to within 0.5% in 98% of counties. Contrary to earlier findings, temperature had variable effect; as Spring temperatures warmed, cases decreased, but Summer heat increased cases, likely reflecting movement of populations from indoors to outdoors and back in. States varied little in their case rate relative to the model, and much of the variation could be linked to known “superspreader” events.ConclusionWhile environmental and demographic variables can help predict COVID-19 spread rates, some relationships are variable in ways earlier research failed to identify.Role of Funding SourceThere was no funding supporting this work.

Scientists and the public were alarmed at the first large viral variant of SARS‐CoV‐2 reported in December 2020. We have followed the time course of emerging viral mutants and variants during the SARS‐CoV‐2 pandemic in ten countries on four continents. We examined > 383,500 complete SARS‐CoV‐2 nucleotide sequences in GISAID (Global Initiative of Sharing All Influenza Data) with sampling dates extending until April 05, 2021. These sequences originated from ten different countries: United Kingdom, South Africa, Brazil, United States, India, Russia, France, Spain, Germany, and China. Among the 77 to 100 novel mutations, some previously reported mutations waned and some of them increased in prevalence over time. VUI2012/01 (B.1.1.7) and 501Y.V2 (B.1.351), the so‐called UK and South Africa variants, respectively, and two variants from Brazil, 484K.V2, now called P.1 and P.2, increased in prevalence. Despite lockdowns, worldwide active replication in genetically and socio‐economically diverse populations facilitated selection of new mutations. The data on mutant and variant SARS‐CoV‐2 strains provided here comprise a global resource for easy access to the myriad mutations and variants detected to date globally. Rapidly evolving new variant and mutant strains might give rise to escape variants, capable of limiting the efficacy of vaccines, therapies, and diagnostic tests., This 2020/21 time course study shows the rapid rise of new SARS‐CoV‐2 mutants and variants across the entire genome during worldwide viral replication. In 10 countries, 40 to 65% of mutants were C to T transitions. Viral mutations will affect vaccination programs.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course., Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian velocity model and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios—anchoring comparisons to non-Hispanic Whites—in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of 30 December 2020. Using a novel Monte Carlo simulation procedure to perform estimation, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, estimated disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.

The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios – anchoring comparisons to non-Hispanic Whites – in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of December 30, 2020. Using a novel Monte Carlo simulation procedure to quantify estimation uncertainty, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, observed disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

The HIV-1 epidemic remains an urgent global health concern. Young women are disproportionately at risk of acquiring the virus. A range of highly effective, female-controlled, discrete vaginal products therefore is needed to help curb the epidemic. Oral tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) are effective in HIV-1 pre-exposure prophylaxis (PrEP) and form a promising basis for a vaginal product. Here, we evaluate TDF and FTC in combination with the broadly neutralizing antibody VRC01-N using a highly reproducible humanized mouse model. The agents were vaginally dosed individually and in combination, and the efficacy of HIV-1 prevention was analyzed using the established, rigorous median-effect model. Surprisingly, the triple combination showed a high degree of synergism, unprecedented for in vivo HIV-1 PrEP, leading to a possible fivefold dose reduction for some of the agents. Vaginal administration of the TDF-FTC-VRC01-N combination holds significant promise for HIV-1 PrEP.

Highlights • Study of SARS-CoV-2 genomes from world-wide isolates. • Sequence comparisons of 570 isolates to original Wuhan 2019 SARS-CoV-2 clade. • Identification of several hotspot mutants after world-wide spreading of virus. • Several hotspot mutations affect sequences of replication-relevant viral proteins. • How do hotspot mutations relate to viral pathogenicity?, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was first identified in Wuhan, China late in 2019. Nine months later (Sept. 23, 2020), the virus has infected > 31.6 million people around the world and caused > 971.000 (3.07 %) fatalities in 220 countries and territories. Research on the genetics of the SARS-CoV-2 genome, its mutants and their penetrance can aid future defense strategies. By analyzing sequence data deposited between December 2019 and end of May 2020, we have compared nucleotide sequences of 570 SARS-CoV-2 genomes from China, Europe, the US, and India to the sequence of the Wuhan isolate. During worldwide spreading among human populations, at least 10 distinct hotspot mutations had been selected and found in up to > 80 % of viral genomes. Many of these mutations led to amino acid exchanges in replication-relevant viral proteins. Mutations in the SARS-CoV-2 genome would also impinge upon the secondary structure of the viral RNA molecule and its repertoire of interactions with essential cellular and viral proteins. The increasing frequency of SARS-CoV-2 mutation hotspots might select for dangerous viral pathogens. Alternatively, in a 29.900 nucleotide-genome, there might be a limit to the number of mutable and selectable sites which, when exhausted, could prove disadvantageous to viral survival. The speed, at which novel SARS-CoV-2 mutants are selected and dispersed around the world, could pose problems for the development of vaccines and therapeutics.

BackgroundThe rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated coronavirus disease 2019 (COVID-19) have precipitated a global pandemic heavily challenging our social behavior, economy, and healthcare infrastructure. Public health practices currently represent the primary interventions for managing the spread of the pandemic. We hypothesized that frequent, longitudinal workplace disease surveillance would represent an effective approach to controlling SARS-CoV-2 transmission among employees and their household members, reducing potential economic consequences and loss of productivity of standard isolation methods, while providing new insights into viral-host dynamics.Methodology and FindingsOn March 23, 2020 a clinical study (OCIS-05) was initiated at a small Southern California organization. Results from the first 3 months of the ongoing study are presented here. Study participants (27 employees and 27 household members) consented to provide frequent nasal or oral swab samples that were analyzed by RT-qPCR for SARS-CoV-2 RNA using CDC protocols. Only participants testing negative were allowed to enter the “safe zone” workplace facility. Optional blood samples were collected at baseline and throughout the 3-month study. Serum virus-specific antibody concentrations (IgG, IgM, and IgA) were measured using a selective, sensitive, and quantitative ELISA assay developed in house. A COVID-19 infection model, based on traditional SEIR compartmental models combined with Bayesian non-linear mixed models and modern machine learning, was used to predict the number of employees and household members who would have become infected in the absence of workplace surveillance.Two study participants were found to be infected by SARS-CoV-2 during the study. One subject, a household member, tested positive clinically by RT-qPCR prior to enrollment and experienced typical COVID-19 symptoms that did not require hospitalization. While on study, the participant was SARS-CoV-2 RNA positive for at least 71 days and had elevated virus-specific antibody concentrations (medians: IgM, 9.83 µg mL-1; IgG, 11.5 µg mL-1; IgA, 1.29 µg mL-1) in serum samples collected at three timepoints. A single, unrelated employee became positive for SARS-CoV-2 RNA over the course of the study, but remained asymptomatic with low associated viral RNA copy numbers. The participant did not have detectable serum IgM and IgG concentrations, and IgA concentrations decayed rapidly (half-life: 1.3 d). The employee was not allowed entry to the safe zone workplace until testing negative three consecutive times over 7 d. No other employees or household members contracted COVID-19 over the course of the study. Our model predicted that under the current prevalence in Los Angeles County without surveillance intervention, up to 7 employees (95% CI = 3-10) would have become infected with at most 1 of them requiring hospitalizations and 0 deaths.ConclusionsOur clinical study met its primary objectives by using intense longitudinal testing to provide a safe work environment during the COVID-19 pandemic, and elucidating SARS-CoV-2 dynamics in recovering and asymptomatic participants. The surveillance plan outlined here is scalable and transferrable. The study represents a powerful example on how an innovative public health initiative can be dovetailed with scientific discovery.

The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. In this narrative review, we develop an analytical framework to examine mask usage, considering and synthesizing the relevant literature to inform multiple areas: population impact; transmission characteristics; source control; PPE; sociological considerations; and implementation considerations. A primary route of transmission of COVID-19 is via respiratory droplets, and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and other measures, and second, reduce the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low. Given the current shortages of medical masks we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory droplets become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask-wearing by infectious people ("source control") with benefits at the population-level, rather than mask-wearing by susceptible people, such as health-care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high risk population subgroups aids policymakers and health officials in combatting the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic, because governmental agencies typically release aggregate COVID-19 data as marginal summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics.We introduce a method that overcomes the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates — key quantities for guiding public policy related to the control and prevention of COVID-19 — for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics.We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race and ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are African American male, Multi-race male, Asian male, African American female, and American Indian or Alaska Native male, indicating that African Americans are an especially vulnerable California subpopulation.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Background: HIV-1 infection is associated with acceleration of age-related methylation patterns in peripheral blood and brain of infected individuals although the relative contributions of HIV-1 infection versus its treatment to the observed accelerations in biological aging have not yet been investigated.Methods: In this longitudinal study of the effects of antiretroviral therapy (ART) on epigenetic aging patterns, we extracted DNA from peripheral blood mononuclear cells from 15 HIV-1-infected individuals infected at three time points: 6 months-1year pre-ART, 6-12 months post-initiation of ART, and 18-24 months after initiating ART. We compared these trajectories with those of 15 age-matched uninfected control participants at three time points with similar intervals. Methylation studies were performed using the Infinium methylation 450 arrays. We examined four epigenetic clock measurements: Age acceleration residual (AAR), Extrinsic (EEAA), Phenotypic (PEAA), and Grim (GEAA) epigenetic age acceleration. Weighted correlation network (WGCNA) analysis was used to identify clusters of highly co-methylated CpGs.Results: We found that prior to the initiation of ART all four epigenetic measures were significantly higher in HIV-1-infected individuals compared with uninfected individuals (P

Objective: What can machine learning tell us about who voted in 2016? There are numerous competing voter turnout theories, and a large number of covariates are required to assess which theory best explains turnout. This article is a proof of concept that machine learning can help overcome this curse of dimensionality and reveal important insights in studies of political phenomena. Methods: We use fuzzy forests, an extension of random forests, to screen variables for a parsimonious but accurate prediction. Fuzzy forests achieve accurate variable importance measures in the face of high‐dimensional and highly correlated data. The data that we use are from the 2016 Cooperative Congressional Election Study. Results: Fuzzy forests chose only a small number of covariates as major correlates of 2016 turnout and still boasted high predictive performance. Conclusion: Our analysis provides three important conclusions about turnout in 2016: registration and voting procedures were important, political issues were important (especially Obamacare, climate change, and fiscal policy), but few demographic variables other than age were strongly associated with turnout. We conclude that fuzzy forests is an important methodology for studying overdetermined questions in social sciences.

Chronic pelvic pain and dysmenorrhea are common conditions affecting reproductive-age women. Surgical pelvic denervation procedures may be a treatment option for women with midline dysmenorrhea, in which medical management is declined by the patient, ineffective at managing symptoms, or medically contraindicated. This review describes the surgical techniques and complications associated with pelvic denervation procedures as well as the current evidence for these procedures in women with primary dysmenorrhea and dysmenorrhea secondary to endometriosis.Presacral neurectomy is the preferred pelvic denervation procedure in patients with primary dysmenorrhea and midline chronic pelvic pain associated with endometriosis. In patients with endometriosis presacral neurectomy is a useful adjunct to excision or ablation of all endometrial lesions to improve postoperative pain relief. There is no additional patient benefit of performing combined presacral neurectomy and uterine nerve ablation procedures.Pelvic denervation procedures can be performed safely and quickly with a low risk of complication if the surgeon is knowledgeable and skilled in operating in the presacral space. Patients should be adequately counseled on expected success rates and potential complications associated with pelvic denervation procedures.

Survey responses in public health surveys are heterogeneous. The quality of a respondent’s answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys.

In this paper we introduce fuzzy forests, a novel machine learning algorithm for ranking the importance of features in high-dimensional classification and regression problems. Fuzzy forests is specifically designed to provide relatively unbiased rankings of variable importance in the presence of highly correlated features, especially when the number of features, p, is much larger than the sample size, n (p n). We introduce our implementation of fuzzy forests in the R package, fuzzyforest. Fuzzy forests works by taking advantage of the network structure between features. First, the features are partitioned into separate modules such that the correlation within modules is high and the correlation between modules is low. The package fuzzyforest allows for easy use of the package WGCNA (weighted gene coexpression network analysis, alternatively known as weighted correlation network analysis) to form modules of features such that the modules are roughly uncorrelated. Then recursive feature elimination random forests (RFE-RFs) are used on each module, separately. From the surviving features, a final group is selected and ranked using one last round of RFE-RFs. This procedure results in a ranked variable importance list whose size is pre-specified by the user. The selected features can then be used to construct a predictive model.

We and others previously reported that the direct-acting agents (DAA) NS5A inhibitors (NS5Ai) and the host-targeting agents cyclophilin inhibitors (CypIs) inhibit HCV replication in vitro. In this study, we investigated whether the combination of NS5Ai and CypI offers a potent anti-HCV effect in vivo. A single administration of NS5Ai or CypI alone to HCV-infected humanized-mice inhibits HCV replication. The combination of NS5Ai with CypI suppresses HCV (GT1a, GT2a, GT3a and GT4a) replication in an additive manner. NS5Ai/CypI combinations provide a statistically more profound anti-HCV inhibition for GT2a and GT3a than GT1a and GT4a, leading to a fastest and deepest inhibition of GT2a and GT3a replications. Combining CypI with NS5Ai prevents the viral rebound normally observed in mice treated with NS5Ai alone. Results were confirmed in mice implanted with human hepatocytes from different donors. Therefore, the combination of NS5Ai with CypI may serve as a regimen for the treatment of HCV patients with specific genotypes and disorder conditions, which diminish sustain viral response levels to DAA, such as GT3a infection, cirrhosis, and DAA resistance associated with the selection of resistance-associated substitutions present at baseline or are acquired during treatment.

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic because governmental agencies typically release aggregate COVID-19 data as summary statistics of patient demographics. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that helps to overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates - key quantities for guiding public policy related to the control and prevention of COVID-19 - for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race/ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. The workforce population, especially, has a higher test-based infection rate than children, adolescents, and other elderly people in their 60-80. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are all-male groups with race as African American, Asian, Multi-race, LatinX, and White, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.

Efferocytosis, the phagocytic clearance of apoptotic cells, can provide host protection against certain types of viruses by mediating phagocytic clearance of infected cells undergoing apoptosis. It is known that HIV-1 induces apoptosis and HIV-1-infected cells are efferocytosed by macrophages, although its molecular mechanisms are unknown. To elucidate the roles that efferocytosis of HIV-1-infected cells play in clearance of infected cells, we sought to identify molecules that mediate these processes. We found that protein S, present in human serum, and its homologue, Gas6, can mediate phagocytosis of HIV-1-infected cells by bridging receptor tyrosine kinase Mer, expressed on macrophages, to phosphatidylserine exposed on infected cells. Efferocytosis of live infected cells was less efficient than dead infected cells; however, a significant fraction of live infected cells were phagocytosed over 12 h. Our results suggest that efferocytosis not only removes dead cells, but may also contribute to macrophage removal of live virus producing cells.

A hallmark of human immunosenescence is the accumulation of late-differentiated memory CD8+ T cells with features of replicative senescence, such as inability to proliferate, absence of CD28 expression, shortened telomeres, loss of telomerase activity, enhanced activation, and increased secretion of inflammatory cytokines. Importantly, oligoclonal expansions of these cells are associated with increased morbidity and mortality risk in elderly humans. Currently, most information on the adaptive immune system is derived from studies using peripheral blood, which contains approximately only 2% of total body lymphocytes. However, most lymphocytes reside in tissues. It is not clear how representative blood changes are of the total immune status. This is especially relevant with regard to the human gastrointestinal tract (GALT), a major reservoir of total body lymphocytes (approximately 60%) and an anatomical region of high antigenic exposure. To assess how peripheral blood T cells relate to those in other locations, we compare CD8+ T cells from peripheral blood and the GALT, specifically rectosigmoid colon, in young/middle age, healthy donors, focusing on phenotypic and functional alterations previously linked to senescence in peripheral blood. Overall, our results indicate that gut CD8+ T cells show profiles suggestive of greater differentiation and activation than those in peripheral blood. Specifically, compared to blood from the same individual, the gut contains significantly greater proportions of CD8+ T cells that are CD45RA- (memory), CD28-, CD45RA-CD28+ (early memory), CD45RA-CD28- (late memory), CD25-, HLA-DR+CD38+ (activated) and Ki-67+ (proliferating); ex vivo CD3+ telomerase activity levels are greater in the gut as well. However, gut CD8+ T cells may not necessarily be more senescent, since they expressed significantly lower levels of CD57 and PD-1 on CD45RO+ memory cells, and had in vitro proliferative dynamics similar to that of blood cells. Compartment-specific age-effects in this cohort were evident as well. Blood cells showed a significant increase with age in proportion of HLA-DR+38+, Ki-67+ and CD25+ CD8+ T cells; and an increase in total CD3+ ex-vivo telomerase activity that approached significance. By contrast, the only age-effect seen in the gut was a significant increase in CD45RA- (memory) and concurrent decrease in CD45RA+CD28+ (naïve) CD8+ T cells. Overall, these results indicate dynamics of peripheral blood immune senescence may not hold true in the gut mucosa, underscoring the importance for further study of this immunologically important tissue in evaluating the human immune system, especially in the context of chronic disease and aging.

OBJECTIVES: Improving cognitive health in young adulthood may improve academic and career success and prevent early age-related cognitive decline. A growing body of evidence suggests that the dietary carotenoid lutein may promote cognitive function; however, these studies have primarily been conducted in Non-Hispanic White populations. The objective of this study was to examine the relationship between macular pigment optical density (MPOD), a biomarker of lutein status, and cognitive function in a diverse, young adult population. Further, we examined the association between fruit and vegetable (F&V) intake and MPOD scores. METHODS: In this cross-sectional study, seventy young adults (Mean = 20.2, SD = 2.3 years of age), were recruited from a university in Central Texas. Daily F&V intake were assessed by the NCI fruit and vegetable intake screener. MPOD was measured with heterochromatic flicker photometry. Cognitive performance was assessed with the NIH Toolbox Cognition Battery, and fully corrected T-scores that correct for age, gender, race/ethnicity, and education were used for analyses. Participants were split into Low and High MPOD groups based on the MPOD median of the sample. The association between F&V intake and MPOD was assessed by multiple linear regression, including body fat percentage as a covariate. F&V intake as a function of MPOD group was analyzed by ANOVA, and cognitive scores were compared using ANCOVA with BMI as a covariate. RESULTS: The sample was primarily female (68.5%) and Caucasian (72.7%), and 40% of the population was Hispanic. The MPOD mean (Mean = 0.39, SD = 0.17) of the sample was above the reported national average of 0.33. Participants consumed approximately 1 cup of fruits and 1 cup of vegetables a day. F&V intake was not correlated with MPOD scores (R(2 )= −0.03, P = 0.81), and F&V intake did not significantly differ between High and Low MPOD groups. Episodic memory performance was significantly higher in the high MPOD group compared to those with low MPOD (P = 0.041). No other differences were found between the MPOD groups for the other fluid cognitive tests. CONCLUSIONS: MPOD was not a biomarker for F&V intake which may be due to the overall low intakes of these lutein-containing foods in the young adult sample. Lutein's cognitive benefits in young adults may be domain specific. FUNDING SOURCES: none.

HIV-1 infection is characterized by genetic diversity, with multiple subtypes and recombinant variants circulating, particularly in sub-Saharan Africa. During the Rwandan genocide, many women experienced multiple rapes and some became HIV-1 infected. We studied plasma and peripheral blood mononuclear cells (PBMCs) from 30 infected women comprising two exposure groups: those with numerous contacts, raped multiple times, and women with one lifetime sexual partner and no history of rape. Population-based sequences from gag, pol, and env genes were analyzed to determine HIV-1 subtypes and intersubtype recombination. Individual plasma-derived variants from 12 women were also analyzed. Subtype A was found in 24/30 (80%), intersubtype recombination (AC and AD) in 4/30 (13%), and subtypes C and D in 1/30 each. In two subjects, the pattern of HIV-1 recombination differed between plasma and PBMC-derived sequences. Intersubtype recombination was common, although there were no significant differences in subtype or recombination rates between exposure groups.

Multidrug-resistant (MDR) HIV-1 presents a challenge to the efficacy of antiretroviral therapy (ART). To examine mechanisms leading to MDR variants in infected individuals, we studied recombination between single viral genomes from the genital tract and plasma of a woman initiating ART. We determined HIV-1 RNA sequences and drug resistance profiles of 159 unique viral variants obtained before ART and semiannually for 4 years thereafter. Soon after initiating zidovudine, lamivudine, and nevirapine, resistant variants and intrapatient HIV-1 recombinants were detected in both compartments; the recombinants had inherited genetic material from both genital and plasma-derived viruses. Twenty-three unique recombinants were documented during 4 years of therapy, comprising ∼22% of variants. Most recombinant genomes displayed similar breakpoints and clustered phylogenetically, suggesting evolution from common ancestors. Longitudinal analysis demonstrated that MDR recombinants were common and persistent, demonstrating that recombination, in addition to point mutation, can contribute to the evolution of MDR HIV-1 in viremic individuals.

Pluripotent stem cells (PSCs) may provide a potential source of haematopoietic stem/progenitor cells (HSPCs) for transplantation; however, unknown molecular barriers prevent the self-renewal of PSC-HSPCs. Using two-step differentiation, human embryonic stem cells (hESCs) differentiated in vitro into multipotent haematopoietic cells that had the CD34(+)CD38(-/lo)CD90(+)CD45(+)GPI-80(+) fetal liver (FL) HSPC immunophenotype, but exhibited poor expansion potential and engraftment ability. Transcriptome analysis of immunophenotypic hESC-HSPCs revealed that, despite their molecular resemblance to FL-HSPCs, medial HOXA genes remained suppressed. Knockdown of HOXA7 disrupted FL-HSPC function and caused transcriptome dysregulation that resembled hESC-derived progenitors. Overexpression of medial HOXA genes prolonged FL-HSPC maintenance but was insufficient to confer self-renewal to hESC-HSPCs. Stimulation of retinoic acid signalling during endothelial-to-haematopoietic transition induced the HOXA cluster and other HSC/definitive haemogenic endothelium genes, and prolonged HSPC maintenance in culture. Thus, medial HOXA gene expression induced by retinoic acid signalling marks the establishment of the definitive HSPC fate and controls HSPC identity and function.

ObjectivePrior hypothesis-driven studies identified immunophenotypic characteristics associated with the control of HIV replication without antiretroviral therapy (HIV controllers) as well as with the degree of CD4 T-cell recovery during ART. We hypothesized that an unbiased 'discovery-based' approach might identify novel immunologic characteristics of these phenotypes.DesignWe performed immunophenotyping on four 'aviremic' patient groups: HIV controllers (n = 98), antiretroviral-treated immunologic nonresponders (CD4 350, n = 142), and as a control group HIV-negative adults (n = 43). We measured levels of T-cell maturation, activation, dysfunction, senescence, functionality, and proliferation.MethodsSupervised learning assessed the relative importance of immune parameters in predicting clinical phenotypes (controller, immunologic responder, or immunologic nonresponder). Unsupervised learning clustered immune parameters and examined if these clusters corresponded to clinical phenotypes.ResultsHIV controllers were characterized by high percentages of HIV-specific T-cell responses and decreased percentages of cells expressing human leukocytic antigen-antigen D related in naive, central memory, and effector T-cell subsets. Immunologic nonresponders were characterized by higher percentages of CD4 T cells that were TNFα+ or INFγ+, higher percentages of activated naive and central memory T cells, and higher percentages of cells expressing programmed cell death protein 1. Unsupervised learning found two distinct clusters of controllers and two distinct clusters of immunologic nonresponders, perhaps suggesting different mechanisms for the clinical outcomes.ConclusionOur discovery-based approach confirmed previously reported characteristics that distinguish aviremic individuals, but also identified novel immunologic phenotypes and distinct clinical subpopulations that should lead to more focused pathogenesis studies that might identify targets for novel therapeutic interventions.