Association between deep neural network-derived electrocardiographic-age and incident stroke.

Background: Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke. Methods: In this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke. Results: The study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00-1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12-1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11-1.49)]. Conclusions: DNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.

Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study.

Background: Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods: The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results: The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions: The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.

Rationale and Design of Healthy at Home for COPD: an Integrated Remote Patient Monitoring and Virtual Pulmonary Rehabilitation Pilot Study.

Chronic Obstructive Pulmonary Disease (COPD) is a common, costly, and morbid condition. Pulmonary rehabilitation, close monitoring, and early intervention during acute exacerbations of symptoms represent a comprehensive approach to improve outcomes, but the optimal means of delivering these services is uncertain. Logistical, financial, and social barriers to providing healthcare through face-to-face encounters, paired with recent developments in technology, have stimulated interest in exploring alternative models of care. The Healthy at Home study seeks to determine the feasibility of a multimodal, digitally enhanced intervention provided to participants with COPD longitudinally over six months. This paper details the recruitment, methods, and analysis plan for the study, which is recruiting 100 participants in its pilot phase. Participants were provided with several integrated services including a smartwatch to track physiological data, a study app to track symptoms and study instruments, access to a mobile integrated health program for acute clinical needs, and a virtual comprehensive pulmonary support service. Participants shared physiologic, demographic, and symptom reports, electronic health records, and claims data with the study team, facilitating a better understanding of their symptoms and potential care needs longitudinally. The Healthy at Home study seeks to develop a comprehensive digital phenotype of COPD by tracking and responding to multiple indices of disease behavior and facilitating early and nuanced responses to changes in participants' health status. This study is registered at Clinicaltrials.gov (NCT06000696).

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns.

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Lipid levels and risk of acute pancreatitis using bidirectional Mendelian randomization.

Previous studies found lipid levels, especially triglycerides (TG), are associated with acute pancreatitis, but their causalities and bi-directions were not fully examined. We determined whether abnormal levels of TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) are precursors and/or consequences of acute pancreatitis using bidirectional two-sample Mendelian randomization (MR) with two non-overlapping genome-wide association study (GWAS) summary statistics for lipid levels and acute pancreatitis. We found phenotypic associations that both higher TG levels and lower HDL-C levels contributed to increased risk of acute pancreatitis. Our GWAS meta-analysis of acute pancreatitis identified seven independent signals. Genetically predicted TG was positively associated with acute pancreatitis when using the variants specifically associated with TG using univariable MR [Odds ratio (OR), 95% CI 2.02, 1.22-3.31], but the reversed direction from acute pancreatitis to TG was not observed (mean difference = 0.003, SE = 0.002, P-value = 0.138). However, a bidirectional relationship of HDL-C and acute pancreatitis was observed: A 1-SD increment of genetically predicted HDL-C was associated with lower risk of acute pancreatitis (OR, 95% CI 0.84, 0.76-0.92) and genetically predisposed individuals with acute pancreatitis have, on average, 0.005 SD lower HDL-C (mean difference = - 0.005, SE = 0.002, P-value = 0.004). Our MR analysis confirms the evidence of TG as a risk factor of acute pancreatitis but not a consequence. A potential bidirectional relationship of HDL-C and acute pancreatitis occurs and raises the prospect of HDL-C modulation in the acute pancreatitis prevention and treatment.

Exploring cognitive progression subtypes in the Framingham Heart Study.

INTRODUCTION: Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. METHODS: Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. RESULTS: Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. DISCUSSION: This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights: We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.

Differential Viral Dynamics by Sex and Body Mass Index During Acute SARS-CoV-2 Infection: Results from a Longitudinal Cohort Study.

BACKGROUND: There is evidence of an association of severe COVID-19 outcomes with increased body mass index (BMI) and male sex. However, few studies have examined the interaction between sex and BMI on SARS-CoV-2 viral dynamics. METHODS: Participants conducted RT-PCR testing every 24-48 hours over a 15-day period. Sex and BMI were self-reported, and Ct values from E-gene were used to quantify viral load. Three distinct outcomes were examined using mixed effects generalized linear models, linear models, and logistic models, respectively: all Ct values (Model 1); nadir Ct value (model 2); and strongly detectable infection (at least one Ct value ≤28 during their infection) (Model 3). An interaction term between BMI and sex was included, and inverse logit transformations were applied to quantify the differences by BMI and sex using marginal predictions. RESULTS: In total, 7,988 participants enrolled in this study, and 439 participants (Model 1) and 309 (Model 2 and 3) were eligible for these analyses. Among males, increasing BMI was associated with lower Ct values in a dose-response fashion. For participants with BMIs greater than 29, males had significantly lower Ct values and nadir Ct values than females. In total, 67.8% of males and 55.3% of females recorded a strongly detectable infection; increasing proportions of men had Ct values <28 with BMIs of 35 and 40. CONCLUSIONS: We observed sex-based dimorphism in relation to BMI and COVID-19 viral load. Further investigation is needed to determine the cause, clinical impact, and transmission implications of this sex-differential effect of BMI on viral load.

Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities.

BACKGROUND: Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS: This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS: In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS: Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.

Interplay between diet, circulating indolepropionate concentrations and cardiometabolic health in US populations.

OBJECTIVES: To identify indolepropionate (IPA)-predicting gut microbiota species, investigate potential diet-microbiota interactions, and examine the prospective associations of circulating IPA concentrations with type 2 diabetes (T2D) and coronary heart disease (CHD) risk in free-living individuals. DESIGN: We included 287 men from the Men's Lifestyle Validation Study, a substudy of the Health Professionals Follow-Up Study (HPFS), who provided up to two pairs of faecal samples and two blood samples. Diet was assessed using 7-day diet records. Associations between plasma concentrations of tryptophan metabolites and T2D CHD risk were examined in 13 032 participants from Nurses' Health Study (NHS), NHSII and HPFS. RESULTS: We identified 17 microbial species whose abundance was significantly associated with plasma IPA concentrations. A significant association between higher tryptophan intake and higher IPA concentrations was only observed among men who had higher fibre intake and a higher microbial species score consisting of the 17 species (p-interaction<0.01). Dietary and plasma concentrations of tryptophan and most kynurenine pathway metabolites were positively associated with T2D risk (HRQ5 vs Q1 ranged from 1.17 to 1.46) while a significant inverse association was found for IPA (HRQ5 vs Q1 (95% CI) 0.70 (0.56 to 0.88)). No associations were found in CHD for any plasma tryptophan metabolites. CONCLUSIONS: Specific microbial species and dietary fibre jointly predicted significantly higher circulating IPA concentrations at higher tryptophan intake. Dietary and plasma tryptophan, as well as its kynurenine pathway metabolites, demonstrated divergent associations from those for IPA, which was significantly predictive of lower risk of T2D.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

HPV18 E6 inhibits α-ketoglutarate-induced pyroptosis of esophageal squamous cell carcinoma cells via the P53/MDH1/ROS/GSDMC pathway.

Oncogene E6 plays a critical role in the development and progression of esophageal cancer caused by human papillomavirus (HPV) infection. Alpha-ketoglutarate (AKG) is a key metabolite in the tricarboxylic acid cycle and has been widely used as a dietary and anti-ageing supplement. In this study, we found that treating esophageal squamous carcinoma cells with a high dose of AKG can induce cell pyroptosis. Furthermore, our research confirms that HPV18 E6 inhibits AKG-induced pyroptosis of esophageal squamous carcinoma cells by lowering P53 expression. P53 downregulates malate dehydrogenase 1 (MDH1) expression; however, MDH1 downregulates L-2-hydroxyglutarate (L-2HG) expression, which inhibits a rise in reactive oxygen species (ROS) levels-as L-2HG is responsible for excessive ROS. This study reveals the actuating mechanism behind cell pyroptosis of esophageal squamous carcinoma cells induced by high concentrations of AKG, and we posit the molecular pathway via which the HPV E6 oncoprotein inhibits cell pyroptosis.

Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study.

BACKGROUND: The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. OBJECTIVE: To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. DESIGN: This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. SETTING: Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. PARTICIPANTS: Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. MEASUREMENTS: The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. RESULTS: Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. LIMITATION: Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. CONCLUSION: The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. PRIMARY FUNDING SOURCE: National Institutes of Health RADx Tech program.

Association of lifestyle with deep learning predicted electrocardiographic age.

Background: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods: This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age. Results: This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age. Conclusion: In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

Bioinformatics Analysis and Verification of Metabolic Abnormalities in Esophageal Squamous Carcinoma.

BACKGROUND: Although esophageal carcinoma (EC) is one of the most common cancers in the world, details of its pathogenesis remain unclear. Metabolic reprogramming is a main feature of EC. Mitochondrial dysfunction, especially the decrease in mitochondrial complex I (MTCI), plays an important role in the occurrence and development of EC. OBJECTIVE: The objective of the study was to analyze and validate the metabolic abnormalities and the role of MTCI in esophageal squamous cell carcinoma. METHODS: In this work, we collected transcriptomic data from 160 esophageal squamous carcinoma samples and 11 normal tissue samples from The Cancer Genome Atlas (TCGA). The OmicsBean and GEPIA2 were used to conduct an analysis of differential gene expression and survival in clinical samples. Rotenone was used to inhibit the MTCI activity. Subsequently, we detected lactate production, glucose uptake, and ATP production. RESULTS: A total of 1710 genes were identified as being significantly differentially expressed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis suggested that these differentially expressed genes (DEGs) were significantly enriched in various pathways related to carcinoma tumorigenesis and progression. Moreover, we further identified abnormalities in metabolic pathways, in particular, the significantly low expression of multiple subunits of MTCI genes (ND1, ND2, ND3, ND4, ND4L, ND5, and ND6). Rotenone was used to inhibit the MTCI activity of EC109 cells, and it was found that the decrease in MTCI activity promoted HIF1A expression, glucose consumption, lactate production, ATP production, and cell migration. CONCLUSION: Our results indicated the occurrence of abnormal metabolism involving decreased mitochondrial complex I activity and increased glycolysis in esophageal squamous cell carcinoma (ESCC), which might be related to its development and degree of malignancy.

Performance of Rapid Antigen Tests Based on Symptom Onset and Close Contact Exposure: A secondary analysis from the Test Us At Home prospective cohort study.

Background: The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective: To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on 'when to test'. Design Setting and Participants: The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure: Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures: Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results: A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance: Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.

Transcriptomic analysis of the effects of the HPV18 E6E7 gene on the cell death mode in esophageal squamous cell carcinoma.

Human papillomavirus (HPV) infection is one of the main causes of esophageal carcinoma (ESCA), and its carcinogenic mechanisms in ESCA require further investigation. E6 and E7 are HPV oncogenes, and their genomic integration is a crucial reason for the transformation of host cells into cancer cells. In order to reveal the role of oncogenes E6 and E7 in ESCA cells, the RNA-Seq raw data for HPV18-positive and -negative esophageal squamous cell carcinoma (ESCC) samples derived from the NCBI BioProject database were analyzed, and the differentially expressed genes were identified. Moreover, differentially expressed genes were enriched significantly in multiple cell death pathways, including apoptosis (cyclin-dependent kinase inhibitor 2A, plakophilin 1 and desmoglein 3), pyroptosis (gasdermin A, gasdermin C, NLR family pyrin domain containing 3, absent in melanoma 2, NLR family pyrin domain containing 1 and Toll like receptor 1) and autophagy (Unc-51 like autophagy activating kinase 1, adrenoceptor beta 2). Consequently, the effects of cisplatin-induced apoptosis and Hank's balanced salt solution-induced autophagy, and α-ketoglutarate-induced pyroptosis in the ESCC-expressing E6 and E7 cells were verified. Therefore, the expression of E6E7 may culminate in the inhibition of multiple cell death modes, which may also be one of the mechanisms of oncogene-induced carcinogenesis.

Low-Carbohydrate Diet Scores and Mortality Among Adults With Incident Type 2 Diabetes.

OBJECTIVE: The current study aims to prospectively examine the association between postdiagnosis low-carbohydrate diet (LCD) patterns and mortality among individuals with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: Among participants with incident diabetes identified in the Nurses' Health Study and Health Professionals Follow-up Study, an overall total LCD score (TLCDS) was calculated based on the percentage of energy as total carbohydrates. In addition, vegetable (VLCDS), animal (ALCDS), healthy (HLCDS), and unhealthy (ULCDS) LCDS were further derived that emphasized different sources and quality of macronutrients. Multivariable-adjusted Cox models were used to assess the association between the LCDS and mortality. RESULTS: Among 10,101 incident T2D cases contributing 139,407 person-years during follow-up, we documented 4,595 deaths of which 1,389 cases were attributed to cardiovascular disease (CVD) and 881 to cancer. The pooled multivariable-adjusted hazard ratios (HRs, 95% CIs) of total mortality per 10-point increment of postdiagnosis LCDS were 0.87 (0.82, 0.92) for TLCDS, 0.76 (0.71, 0.82) for VLCDS, and 0.78 (0.73, 0.84) for HLCDS. Both VLCDS and HLCDS were also associated with significantly lower CVD and cancer mortality. Each 10-point increase of TLCDS, VLCDS, and HLCDS from prediagnosis to postdiagnosis period was associated with 12% (7%, 17%), 25% (19%, 30%), and 25% (19%, 30%) lower total mortality, respectively. No significant associations were observed for ALCDS and ULCDS. CONCLUSIONS: Among people with T2D, greater adherence to LCD patterns that emphasize high-quality sources of macronutrients was significantly associated with lower total, cardiovascular, and cancer mortality.

Impacts of the choice of distance measurement method on estimates of access to point-based resources.

BACKGROUND/OBJECTIVE: Lack of access to resources such as medical facilities and grocery stores is related to poor health outcomes and inequities, particularly in an environmental justice framework. There can be substantial differences in quantifying "access" to such resources, depending on the geospatial method used to generate distance estimates. METHODS: We compared three methods for calculating distance to the nearest grocery store to illustrate differential access at the census block-group level in the Atlanta metropolitan area, including: Euclidean distance estimation, service areas incorporating roadways and other factors, and cost distance for every point on the map. RESULTS: We found notable differences in access across the three estimation techniques, implying a high potential for exposure misclassification by estimation method. There was a lack of nuanced exposure in the highest- and lowest-access areas using the Euclidean distance method. We found an Intraclass Correlation Coefficient (ICC) of 0.69 (0.65, 0.73), indicating moderate agreement between estimation methods. SIGNIFICANCE: As compared with Euclidean distance, service areas and cost distance may represent a more meaningful characterization of "access" to resources. Each method has tradeoffs in computational resources required versus potential improvement in exposure classification. Careful consideration of the method used for determining "access" will reduce subsequent misclassifications.

Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection.

Background: Performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance in screening for SARS-CoV-2 is not well established. We aimed to evaluate performance of Ag-RDT for detection of SARS-CoV-2 for symptomatic and asymptomatic participants. Methods: Participants >2 years old across the United States enrolled in the study between October 2021 and February 2022. Participants completed Ag-RDT and molecular testing (RT-PCR) for SARS-CoV-2 every 48 hours for 15 days. This analysis was limited to participants who were asymptomatic and tested negative on their first day of study participation. Onset of infection was defined as the day of first positive RT-PCR result. Sensitivity of Ag-RDT was measured based on testing once, twice (after 48-hours), and thrice (after 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status. Results: In total, 5,609 of 7,361 participants were eligible for this analysis. Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDT twice 48-hours apart resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Excluding singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower at 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with testing three times at 48-hour intervals. Discussion: Performance of Ag-RDT was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours.