Therapeutic application of human type 2 innate lymphoid cells via induction of granzyme B-mediated tumor cell death.

The therapeutic potential for human type 2 innate lymphoid cells (ILC2s) has been underexplored. Although not observed in mouse ILC2s, we found that human ILC2s secrete granzyme B (GZMB) and directly lyse tumor cells by inducing pyroptosis and/or apoptosis, which is governed by a DNAM-1-CD112/CD155 interaction that inactivates the negative regulator FOXO1. Over time, the high surface density expression of CD155 in acute myeloid leukemia cells impairs the expression of DNAM-1 and GZMB, thus allowing for immune evasion. We describe a reliable platform capable of up to 2,000-fold expansion of human ILC2s within 4 weeks, whose molecular and cellular ILC2 profiles were validated by single-cell RNA sequencing. In both leukemia and solid tumor models, exogenously administered expanded human ILC2s show significant antitumor effects in vivo. Collectively, we demonstrate previously unreported properties of human ILC2s and identify this innate immune cell subset as a member of the cytolytic immune effector cell family.

ILC1s control leukemia stem cell fate and limit development of AML.

Type I innate lymphoid cells (ILC1s) are critical regulators of inflammation and immunity in mammalian tissues. However, their function in cancer is mostly undefined. Here, we show that a high density of ILC1s induces leukemia stem cell (LSC) apoptosis in mice. At a lower density, ILC1s prevent LSCs from differentiating into leukemia progenitors and promote their differentiation into non-leukemic cells, thus blocking the production of terminal myeloid blasts. All of these effects, which require ILC1s to produce interferon-γ after cell-cell contact with LSCs, converge to suppress leukemogenesis in vivo. Conversely, the antileukemia potential of ILC1s wanes when JAK-STAT or PI3K-AKT signaling is inhibited. The relevant antileukemic properties of ILC1s are also functional in healthy individuals and impaired in individuals with acute myeloid leukemia (AML). Collectively, these findings identify ILC1s as anticancer immune cells that might be suitable for AML immunotherapy and provide a potential strategy to treat AML and prevent relapse of the disease.

Systematic pan-cancer analysis insights into ICAM1 as an immunological and prognostic biomarker.

Intercellular adhesion molecule 1 (ICAM1) is a cell surface adhesion glycoprotein in the immunoglobulin supergene family. It is associated with several epithelial tumorigenesis processes, as well as with inflammation. However, the function of ICAM1 in the prognosis of tumor immunity is still unclear. This study aimed to examine the immune function of ICAM1 in 33 tumor types and to investigate the prognostic value of tumors. Using datasets from the Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Cancer Cell Lines Encyclopedia (CCLE), Human Protein Atlas (HPA), and cBioPortal, we investigated the role of ICAM1 in tumors. We explored the potential correlation between ICAM1 expression and tumor prognosis, gene mutations, microsatellite instability, and tumor immune cell levels in various cancers. We observed that ICAM1 is highly expressed in multiple malignant tumors. Furthermore, ICAM1 is negatively or positively associated with different malignant tumor prognoses. The expression levels of ICAM1 were correlated with the tumor mutation burden (TMB) in 11 tumors and with MSI in eight tumors. ICAM1 is a gene associated with immune infiltrating cells, such as M1 macrophages and CD8+ T cells in gastric and colon cancer. Meanwhile, the expression of ICAM1 is associated with several immune-related functions and immune-regulation-related signaling pathways, such as the chemokine signaling pathway. Our study shows that ICAM1 can be used as a prognostic biomarker in many cancer types because of its function in tumorigenesis and malignant tumor immunity.

Multi-level Factors Associated with HIV Late Presentation with Advanced Disease and Delay Time of Diagnosis in South Carolina, 2005-2019.

This study explored individual- and county-level risk factors of late presentation with advanced disease (LPAD) among people with HIV (PWH) and their longer delay time from infection to diagnosis in South Carolina (SC), using SC statewide Enhanced HIV/AIDS Reporting System (eHARS). LPAD was defined as having an AIDS diagnosis within three months of initial HIV diagnosis, and delay time from HIV infection to diagnosis was estimated using CD4 depletion model. 3,733 (41.88%) out of 8,913 adult PWH diagnosed from 2005 to 2019 in SC were LPAD, and the median delay time was 13.04 years. Based on the generalized estimating equations models, PWH who were male (adjusted prevalence ratio [aPR]: 1.22, 95% CI: 1.12 ∼ 1.33), aged 55+ (aPR: 1.76, 95% CI: 1.62 ∼ 1.92), were Black (aPR: 1.09, 95% CI: 1.03 ∼ 1.15) or Hispanic (aPR: 1.42, 95% CI: 1.26 ∼ 1.61), and living in counties with a larger proportion of unemployment individuals (aPR: 1.02, 95% CI: 1.01 ∼ 1.03) were more likely to be LPAD. Among PWH who were LPAD, Hispanic (adjusted beta: 1.17, 95% CI: 0.49 ∼ 1.85) instead of Black (adjusted beta: 0.11, 95% CI: -0.30 ∼ 0.52) individuals had significant longer delay time compared to White individuals. Targeted and sustained interventions are needed for older, male, Hispanic or Black individuals and those living in counties with a higher percentage of unemployment because of their higher risk of LPAD. Additionally, specific attention should be paid to Hispanic individuals due to their longer delay time to diagnosis.

BTRX-246040 Acts Through the Ventrolateral Periaqueductal Gray to Exert Antidepressant-Relevant Actions in Mice.

BACKGROUND: BTRX-246040, a nociceptin/orphanin FQ peptide receptor antagonist, is being developed for the treatment of depressive patients. However, the underlying mechanism of this potential antidepressant is still largely unclear. Here, we studied the antidepressant-related actions of BTRX-246040 in the ventrolateral periaqueductal gray (vlPAG). METHODS: The tail suspension test, forced swim test, female urine sniffing test, sucrose preference test, and learned helplessness (LH) combined with pharmacological approaches were employed to examine the antidepressant-like effects and drug effects on LH-induced depressive-like behavior in C57BL/6J mice. Electrophysiological recordings in vlPAG neurons were used to study synaptic activity. RESULTS: Intraperitoneal administration of BTRX-246040 produced antidepressant-like behavioral effects in a dose-dependent manner. Systemic BTRX-246040 (10 mg/kg) resulted in an increased frequency and amplitude of miniature excitatory postsynaptic currents (EPSCs) in the vlPAG. Moreover, slice perfusion of BTRX-246040 directly elevated the frequency and amplitude of miniature EPSCs and enhanced the evoked EPSCs in the vlPAG, which were blocked by pretreatment with the nociceptin/orphanin FQ peptide receptor agonist Ro 64-6198. In addition, intra-vlPAG application of BTRX-246040 produced antidepressant-like behavioral effects in a dose-dependent manner. Moreover, intra-vlPAG pretreatment with 6-cyano-7-nitroquinoxaline-2,3-dione reversed both systemic and local BTRX-246040-mediated antidepressant-like behavioral effects. Furthermore, both systemic and local BTRX-246040 decreased the LH phenotype and reduced LH-induced depressive-like behavior. CONCLUSIONS: The results suggested that BTRX-246040 may act through the vlPAG to exert antidepressant-relevant actions. The present study provides new insight into a vlPAG-dependent mechanism underlying the antidepressant-like actions of BTRX-246040.

The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.

The global COVID-19 pandemic has imposed unprecedented pressure on health systems and has interrupted public health efforts for other major health conditions, including HIV. It is critical to comprehensively understand how the pandemic has affected the delivery and utilization of HIV-related services and what are the effective strategies that may mitigate the negative impacts of COVID-19 and resultant interruptions. The current study thus aims to comprehensively investigate HIV service interruptions during the pandemic following a socioecological model, to assess their impacts on various outcomes of the HIV prevention and treatment cascade and to identify resilience resources for buffering impacts of interruptions on HIV treatment cascade outcomes. We will assess HIV service interruptions in South Carolina (SC) since 2020 using operational report data from Ryan White HIV clinics and HIV service utilization data (including telehealth use) based on statewide electronic health records (EHR) and cellphone-based place visitation data. We will further explore how HIV service interruptions affect HIV prevention and treatment cascade outcomes at appropriate geospatial units based on the integration of multi-type, multi-source datasets (e.g., EHR, geospatial data). Finally, we will identify institutional-, community-, and structural-level factors (e.g., resilience resources) that may mitigate the adverse impacts of HIV service interruptions based on the triangulation of quantitative (i.e., EHR data, geospatial data, online survey data) and qualitative (i.e., in-depth interviews with clinic leaders, healthcare providers, people living with HIV, and HIV clinic operational reports) data regarding health system infrastructure, social capital, and organizational preparedness. Our proposed research can lead to a better understanding of complicated HIV service interruptions in SC and resilience factors that can mitigate the negative effects of such interruptions on various HIV treatment cascade outcomes. The multilevel resilience resources identified through data triangulation will assist SC health departments and communities in developing strategic plans in response to this evolving pandemic and other future public health emergencies (e.g., monkeypox, disasters caused by climate change). The research findings can also inform public health policymaking and the practices of other Deep South states with similar sociocultural contexts in developing resilient healthcare systems and communities and advancing epidemic preparedness.

Place Visitation Data Reveals the Geographic and Racial Disparities of COVID-19 Impact on HIV Service Utilization in the Deep South.

BACKGROUND: The COVID-19 pandemic has posed unprecedented pressure to health care systems, and interrupted health care delivery and access including HIV care in the United States' Deep South, which endures a double epidemic of HIV and COVID-19. Ryan White programs cover HIV care services for over half of PLWH in the Deep South. Given the important role of Ryan White programs, examining the visitation changes to Ryan White facilities during the pandemic offers insights into the impact of the pandemic on HIV healthcare utilization. OBJECTIVES: Analyze the geographic distribution of HIV facility visitors at the county level before and during the pandemic in the nine US states of Deep South (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Texas) to reveal the geographic and racial disparity in visitation disruption caused by the pandemic. METHODS: We first extracted mobile device-based visitation data for Ryan White HIV facilities in the Deep South during 2019 and 2020. To quantify the disruption in visitations during 2020, we calculated the visitation reduction rate (VRR) for each county, using 2019 data as the baseline. Next, we conducted a spatial analysis of the VRR values to uncover geographical disparities in visitation interruptions. To investigate racial disparities, we performed spatial regression analyses with VRR as the dependent variable, and the percentages of Black, Hispanic, and Asian populations as the independent variables. In this analysis, we controlled for potential confounders. RESULTS: Geographic disparities in visitation reduction were observed, with all nine Deep South states experiencing significant drops. Georgia experienced the highest visitation loss (VRR = -0.58), followed by Texas (-0.47), Alabama (0.47), and Tennessee (-0.46), while South Carolina had the smallest reductions (-0.11). All the regression models consistently revealed racial disparities in visitation interruption. That is, counties with a higher proportion of Black population tended to have higher RW facility visitation reductions. CONCLUSIONS: Our analysis revealed distinct geographic disparities in visitation interruptions at Ryan White HIV facilities in the Deep South during the COVID-19 pandemic in 2020. Furthermore, we found that the Black/African American population experienced a greater disruption at the county level in the Deep South during this period.

Purinergic Receptor P2Y6 Is a Negative Regulator of NK Cell Maturation and Function.

NK cells are critical innate immune cells that target the tumor cells and cancer-initiating cells and clear viruses by producing cytokines and cytotoxic granules. However, the role of the purinergic receptor P2Y6 in the NK cells remains largely unknown. In this study, we discovered that the expression of P2Y6 was decreased upon the activation of the NK cells. Moreover, in the P2Y6-deficient mice, we found that the deficiency of P2Y6 promoted the development of the NK precursor cells into immature NK and mature NK cells. We also found that the P2Y6 deficiency increased, but the P2Y6 receptor agonist UDP or UDP analog 5-OMe-UDP decreased the production of IFN-γ in the activated NK cells. Furthermore, we demonstrated that the P2Y6-deficient NK cells exhibited stronger cytotoxicity in vitro and antimetastatic effects in vivo. Mechanistically, P2Y6 deletion promoted the expression of T-bet (encoded by Tbx21), with or without the stimulation of IL-15. In the absence of P2Y6, the levels of phospho-serine/threonine kinase and pS6 in the NK cells were significantly increased upon the stimulation of IL-15. Collectively, we demonstrated that the P2Y6 receptor acted as a negative regulator of the NK cell function and inhibited the maturation and antitumor activities of the NK cells. Therefore, inhibition of the P2Y6 receptor increases the antitumor activities of the NK cells, which may aid in the design of innovative strategies to improve NK cell-based cancer therapy.

Cbl-b Is Upregulated and Plays a Negative Role in Activated Human NK Cells.

The E3 ubiquitin ligase Cbl-b has been characterized as an intracellular checkpoint in T cells; however, the function of Cbl-b in primary human NK cells, an innate immune anti-tumor effector cell, is not well defined. In this study, we show that the expression of Cbl-b is significantly upregulated in primary human NK cells activated by IL-15, IL-2, and the human NK cell-sensitive tumor cell line K562 that lacks MHC class I expression. Pretreatment with JAK or AKT inhibitors prior to IL-15 stimulation reversed Cbl-b upregulation. Downregulation of Cbl-b resulted in significant increases in granzyme B and perforin expression, IFN-γ production, and cytotoxic activity against tumor cells. Collectively, we demonstrate upregulation of Cbl-b and its inhibitory effects in IL-15/IL-2/K562-activated human NK cells, suggesting that Cbl-b plays a negative feedback role in human NK cells.

Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data.

BACKGROUND: COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic. METHODS: Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC. RESULTS: As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (ß = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (ß = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (ß: -1.19 and -0.42; p-value is < 0.05 for both). CONCLUSIONS: County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

Rural-Urban Disparities in Hospital Admissions and Mortality Among Patients with COVID-19: Evidence from South Carolina from 2021 to 2022.

Although rural communities have been hard-hit by the COVID-19 pandemic, there is limited evidence on COVID-19 outcomes in rural America using up-to-date data. This study aimed to estimate the associations between hospital admissions and mortality and rurality among COVID-19 positive patients who sought hospital care in South Carolina. We used all-payer hospital claims, COVID-19 testing, and vaccination history data from January 2021 to January 2022 in South Carolina. We included 75,545 hospital encounters within 14 days after positive and confirmatory COVID-19 testing. Associations between hospital admissions and mortality and rurality were estimated using multivariable logistic regressions. About 42% of all encounters resulted in an inpatient hospital admission, while hospital-level mortality was 6.3%. Rural residents accounted for 31.0% of all encounters for COVID-19. After controlling for patient-level, hospital, and regional characteristics, rural residents had higher odds of overall hospital mortality (Adjusted Odds Ratio - AOR = 1.19, 95% Confidence Intervals - CI = 1.04-1.37), both as inpatients (AOR = 1.18, 95% CI = 1.05-1.34) and as outpatients (AOR = 1.63, 95% CI = 1.03-2.59). Sensitivity analyses using encounters with COVID-like illness as the primary diagnosis only and encounters from September 2021 and beyond - a period when the Delta variant was dominant and booster vaccination was available - yielded similar estimates. No significant differences were observed in inpatient hospitalizations (AOR = 1.00, 95% CI = 0.75-1.33) between rural and urban residents. Policymakers should consider community-based public health approaches to mitigate geographic disparities in health outcomes among disadvantaged population subgroups.

Intracellular Ca2+ regulation of H+/Ca2+ antiporter YfkE mediated by a Ca2+ mini-sensor.

The H+/Ca2+ (calcium ion) antiporter (CAX) plays an important role in maintaining cellular Ca2+ homeostasis in bacteria, yeast, and plants by promoting Ca2+ efflux across the cell membranes. However, how CAX facilitates Ca2+ balance in response to dynamic cytosolic Ca2+ perturbations is unknown. Here, we identified a type of Ca2+ "mini-sensor" in YfkE, a bacterial CAX homolog from Bacillus subtilis. The mini-sensor is formed by six tandem carboxylate residues within the transmembrane (TM)5-6 loop on the intracellular membrane surface. Ca2+ binding to the mini-sensor triggers the transition of the transport mode of YfkE from a high-affinity to a low-affinity state. Molecular dynamics simulation and fluorescence resonance energy transfer analysis suggest that Ca2+ binding to the mini-sensor causes an adjacent segment, namely, the exchanger inhibitory peptide (XIP), to move toward the Ca2+ translocation pathway to interact with TM2a in an inward-open cavity. The specific interaction was demonstrated with a synthetic peptide of the XIP, which inhibits YfkE transport and interrupts conformational changes mediated by the mini-sensor. By comparing the apo and Ca2+-bound CAX structures, we propose the following Ca2+ transport regulatory mechanism of YfkE: Ca2+ binding to the mini-sensor induces allosteric conformational changes in the Ca2+ translocation pathway via the XIP, resulting in a rearrangement of the Ca2+-binding transport site in the midmembrane. Since the Ca2+ mini-sensor and XIP sequences are also identified in other CAX homologs and/or Ca2+ transporters, including the mammalian Na+/Ca2+ exchanger (NCX), our study provides a regulatory mechanism for the Ca2+/cation transporter superfamily.

Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022.

BACKGROUND: Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people's expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. OBJECTIVE: This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. METHODS: This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. RESULTS: Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. CONCLUSIONS: Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.

Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis.

Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID-19 over time. Our current understandings of risk perceptions regarding COVID-19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID-19-related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID-19-related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county-level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID-19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID-19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID-19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID-19 pandemic can help policymakers frame in-time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.

Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina.

Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has emerged as a valuable data source for revealing fine-grained human mobility, which can be used to estimate the intensity of physical contact surrounding different locations. Our study applied smartphone mobility data to simulate the second wave spreading of COVID-19 in January 2021 in three major metropolitan statistical areas (Columbia, Greenville, and Charleston) in South Carolina, United States. Based on the simulation, the number of historical county-level COVID-19 cases was allocated to neighborhoods (Census block groups) and points of interest (POIs), and the transmission rate of each allocated place was estimated. The result reveals that the COVID-19 infections during the study period mainly occurred in neighborhoods (86%), and the number is approximately proportional to the neighborhood's population. Restaurants and elementary and secondary schools contributed more COVID-19 infections than other POI categories. The simulation results for the coastal tourism Charleston area show high transmission rates in POIs related to travel and leisure activities. The results suggest that neighborhood-level infectious controlling measures are critical in reducing COVID-19 infections. We also found that households of lower socioeconomic status may be an umbrella against infection due to fewer visits to places such as malls and restaurants associated with their low financial status. Control measures should be tailored to different geographic locations since transmission rates and infection counts of POI categories vary among metropolitan areas.

Population Mobility and Aging Accelerate the Transmission of Coronavirus Disease 2019 in the Deep South: A County-Level Longitudinal Analysis.

Population mobility and aging at local areas contributed to the geospatial disparities in the coronavirus disease 2019 (COVID-19) transmission among 418 counties in the Deep South. In predicting the incidence of COVID-19, a significant interaction was found between mobility and the proportion of older adults. Effective disease control measures should be tailored to vulnerable communities.

Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.

BACKGROUND: Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support. METHODS: The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: "Pattern Analysis" to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: "Model Development" to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: "Translational Research" to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits. DISCUSSION: With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.

P2Y6 Deficiency Enhances Dendritic Cell-Mediated Th1/Th17 Differentiation and Aggravates Experimental Autoimmune Encephalomyelitis.

Dendritic cells (DCs) are essential APCs and play a crucial role in initiating and regulating the adaptive immune response. In this study, we have reported that P2Y6, a member of G protein-coupled receptors, inhibits the maturation and activation of DCs via suppressing the activation of the transcription factor NF-κB. Furthermore, loss of P2Y6 does not impact T cells homeostasis in the steady-state. However, in vitro studies show that P2Y6 signaling inhibits the production of IL-12 and IL-23 and the polarization of Th1 and Th17 subsets mediated by DCs. In addition, we find that mice lacking P2Y6 develop more severe experimental autoimmune encephalomyelitis compared with wild-type mice. Our results indicate that P2Y6 functions as a pivotal regulator on DC maturation, and the loss of P2Y6 results in the aggravated experimental autoimmune encephalomyelitis, which suggests that P2Y6 may play a pivotal role in the pathogenesis of autoimmune diseases.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.

BACKGROUND: Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time. METHODS: Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality. RESULTS: Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year. CONCLUSIONS: Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.