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Objective The impact of comorbidities and biomarkers on COVID-19 severity vary by sex but have not yet been verified in population-based studies. We examined the association of comorbidities, inflammatory biomarkers, and severe outcomes in men and women hospitalized for COVID-19. Design This is a retrospective cohort analysis based on the National COVID Cohort Collaborative (N3C). We included 574,391 adult patients admitted for COVID-19 at hospitals or emergency rooms between 01/01/2020 and 12/31/2021. Methods We defined comorbidities at or before the first admission for COVID-19 by Charlson Comorbidity Index (CCI) and CCI components. We used the averaged lab values taken within 15 days before or after the admission date to measure biomarkers including c-reactive protein (CRP), ferritin, procalcitonin, N-terminal pro b-type natriuretic peptide (NT proBNP), d-dimer, absolute lymphocyte counts, absolute neutrophil counts, and platelets. Our primary outcome was all-cause mortality; secondary outcomes were invasive mechanical ventilation (IMV) and hospital length of stay (LOS). We used logistic regression adjusted for age, race, ethnicity, visit type, and medications to assess the association of comorbidities, biomarkers, and mortality disaggregating by sex. Results Moderate to severe liver disease, renal disease, metastatic solid tumor, and myocardial infarction were the top four fatal comorbidities among patients who were hospitalized for COVID-19 (adjusted odds ratio [aOR] > 2). These four comorbid conditions remained the most lethal in both sexes, with a higher magnitude of risk in women than in men (p-interaction < 0.05). Abnormal elevations of CRP, ferritin, procalcitonin, NT proBNP, neutrophil, and platelet counts, and lymphocytopenia were significantly associated with the risk of death, with procalcitonin and NT proBNP as the strongest predictors (aOR > 2). The association between the abnormal biomarkers and death was stronger in women than in men (p-interaction < 0.05). Conclusion There are sex differences in inpatient mortality associated with comorbidities and biomarkers. The significant impact of these clinical determinants in women with COVID-19 may be underappreciated as previous studies stressed the increased death rate in male patients that is related to comorbidities or inflammation. Our study highlights the importance and the need for sex-disaggregated research to understand the risk factors of poor outcomes and health disparities in COVID-19.

Previous studies demonstrated higher short-term mortality among rural compared with urban residents infected with SARS-CoV-2. However, whether this difference persists remains uncertain. This retrospective cohort study analyzed two-year post-COVID-19 mortality by rurality using the National Clinical Cohort Collaborative COVID-19 Enclave, a United States-based longitudinal electronic health record repository. We analyzed mortality among patients infected with SARS-CoV-2 between April 2020 and December 2022, with follow-up until December 2024. Patients were categorized into urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR) groups based on residential ZIP Code. Mortality differences were assessed using Kaplan-Meier analysis and weighted multivariable Cox regression, with weights derived from demographic factors and models adjusted for background clinical risk and social vulnerability. Among 3,082,978 SARS-CoV-2-infected patients, we found a significant association between rurality and increased two-year all-cause mortality post-infection. Adjusted hazards for two-year mortality for UAR and NAR were 1.19 (95% CI 1.18-1.21) and 1.26 (1.22-1.29). A reference cohort of 4,153,216 COVID-19-negative patients showed a modest yet consistent rural mortality penalty, with a similar relative hazard across cohorts, an observed rurality-COVID-19 interaction, and a greater absolute number of deaths following SARS-CoV-2 infection. Our findings emphasize ongoing rural mortality disparities and the importance of public health efforts in rural communities. People living in rural areas of the United States have poorer outcomes from acute COVID-19. Here, the authors show that higher mortality rates among rural dwellers persist for up to two years after the initial infection, even after accounting for baseline risk factors.

Background/objectivesPragmatic trials are increasingly used to study the implementation of weight loss interventions in real-world settings. This study compared researcher-measured body weights versus electronic medical record (EMR)-derived body weights from a pragmatic trial conducted in an underserved patient population.Subjects/methodsThe PROPEL trial randomly allocated 18 clinics to usual care (UC) or to an intensive lifestyle intervention (ILI) designed to promote weight loss. Weight was measured by trained technicians at baseline and at 6, 12, 18, and 24 months. A total of 11 clinics (6 UC/5 ILI) with 577 enrolled patients also provided EMR data (n = 561), which included available body weights over the period of the trial.ResultsThe total number of assessments were 2638 and 2048 for the researcher-measured and EMR-derived body weight values, respectively. The correlation between researcher-measured and EMR-derived body weights was 0.988 (n = 1 939; p < 0.0001). The mean difference between the EMR and researcher weights (EMR-researcher) was 0.63 (2.65 SD) kg, and a Bland-Altman graph showed good agreement between the two data collection methods; the upper and lower boundaries of the 95% limits of agreement are −4.65 kg and +5.91 kg, and 71 (3.7%) of the values were outside the limits of agreement. However, at 6 months, percent weight loss in the ILI compared to the UC group was 7.3% using researcher-measured data versus 5.5% using EMR-derived data. At 24 months, the weight loss maintenance was 4.6% using the technician-measured data versus 3.5% using EMR-derived data.ConclusionAt the group level, body weight data derived from researcher assessments and an EMR showed good agreement; however, the weight loss difference between ILI and UC was blunted when using EMR data. This suggests that weight loss studies that rely on EMR data may require larger sample sizes to detect significant effects.Clinical trial registrationClinicalTrials.gov number NCT02561221.

Objectives/Goals: The primary objective of this study is to investigate the relationship between human leukocyte antigen (HLA) alleles to COVID-19 clinical severity, specifically: hospitalization, mortality, pneumonia by COVID-19, post-acute sequelae of SARS-CoV-2 infection (PASC), and clinical lab values. Methods/Study Population: We are conducting a retrospective cohort study utilizing the All of Us controlled tier dataset. The base population was defined as any patients with a COVID-19 diagnosis code (ICD-10: U07.1 or SNOMED: 840539006) and genomic sequencing data. PASC definitions were developed by the N3C consortium and refined in house. A total of 15,252 patients (64.5% female; 50.4% self-reported European ancestry; 18.8% self-reported African ancestry; 34.5% > 65 years old) are included in this study. HLA Class I and Class II alleles will be imputed from a global diversity reference panel utilizing the HIBAG “R” package. Results/Anticipated Results: Controlling for age, sex, race, and COVID-19 vaccination status, we anticipate determining the HLA alleles associated with severe clinical outcomes, such as Pneumonia by COVID (n = 1,436) and PASC (ICD-10:U09.9 or SNOMED:119303003 or OMOP:OMOP5160861 [n = 498]). We will assess which HLA alleles are associated with markedly different IgM and IgG COVID-19 serum antibody levels (n = 1,024). Coexisting conditions, i.e., type 2 diabetes, chronic obstructive pulmonary disease, and hypertension, will be controlled for with the Charlson comorbidity index. The accuracy of HLA allelic imputation will be validated in patients with long-read whole genome sequences. Discussion/Significance of Impact: Our findings can help identify patients who may be at risk of severe COVID-19 infection, particularly those undergoing bone marrow or organ transplantation. We hope this study will accelerate personalized care of COVID-19 in vulnerable populations.

Rationale Short‐term weight loss is possible in a variety of settings. However, long‐term, free‐living weight loss maintenance following structured weight loss interventions remains elusive. Objective The purpose was to study body weight trajectories over 2 years of intensive lifestyle intervention (ILI) and up to 4 years of follow‐up versus usual care (UC). Methods Data were obtained from electronic medical records (EMRs) from participating clinics. Baseline (Day 0) was established as the EMR data point closest but prior to the baseline date of the trial. The sample included 111 ILI and 196 UC patients. The primary statistical analysis focused on differentiating weight loss trajectories between ILI and UC. Results The ILI group experienced significantly greater weight loss compared with the UC group from Day 100 to Day 700, beyond which there were no significant differences. Intensive lifestyle intervention patients who maintained ≥5% and ≥10% weight loss at 24 months demonstrated significantly greater weight loss (p < 0.001) across the active intervention and follow‐up. Conclusions Following 24 months of active intervention, patients with ILI regained weight toward their baseline to the point where ILI versus UC differences were no longer statistically or clinically significant. However, patients in the ILI who experienced ≥5% or ≥10% weight loss at the cessation of the active intervention maintained greater weight loss at the end of the follow‐up phase. Clinical Trial Registration ClinicalTrials.gov: NCT02561221. Following 24 months of active intervention, patients in an intensive lifestyle intervention regained weight toward their baseline to the point where differences between the intervention and usual care groups were no longer statistically or clinically significant during the 4‐year follow‐up phase. Patients in the intervention who experienced ≥5% or ≥10% weight loss at the end of the active intervention maintained greater weight loss at the end of the 4‐year follow‐up phase.

Long COVID remains poorly characterized at the genomic level. The primary aim of this study was to examine the relationship between viral sequences and the incidence of Long COVID at a tertiary care center in Louisiana between April 2020 and December 2022. A secondary aim was analysis of the Spike protein to identify conserved regions for multivalent vaccine targets. To estimate Long COVID incidence across variants, we linked 4789 SARS-CoV-2 sequences to 3090 de-identified patient electronic health record information. The base population was defined as any patient with an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) based definitions of Long COVID presentation developed by the N3C consortium. 1,554 patients (1,536 Long COVID-negative) met Long COVID definitions, with 56.3% being female, 36.1% self-reported as African American, 5.5% self-reported as Hispanic/Latino, and 54.5% had received at least one vaccine dose 14 days prior to SARS-CoV-2 collection. Long COVID-positive patients were older (mean age 43.1 years) than negative patients (35.9 years; = 0.0054) and were more likely to be female ( = 0.0001). Among unvaccinated patients, those with Long COVID were significantly younger than their vaccinated counterparts ( < 0.00001). Long COVID incidence varied by PANGO lineage, ranging between 14% in AY.13 to 67.8% in B.1.1.7. Analysis of spike protein diversity revealed eight conserved amino acid regions (Shannon entropy < 0.43), representing potential targets for vaccine design. Long COVID rates across thousands of annotated SARS-CoV-2 sequences revealed lineage-specific risk and conserved epitopes for future interventions.

IntroductionThe objective of this study was to determine the burden of influenza disease in patients with or without diabetes in a population of American adults to understand the benefits of seasonal vaccination.Research design and methodsWe performed a retrospective cohort study using electronic medical records totaling 1,117,263 from two Louisiana healthcare providers spanning January 2012 through December 2017. Adults 18 years or older with two or more records within the study period were included. The primary outcome quantified was influenza-related diagnosis during inpatient (IP) or emergency room (ER) visits and risk reduction with the timing of immunization.ResultsInfluenza-related IP or ER visits totaled 0.0122–0.0169 events per person within the 2013–2016 influenza seasons. Subjects with diabetes had a 5.6-fold more frequent influenza diagnosis for IP or ER visits than in subjects without diabetes or 3.7-fold more frequent when adjusted for demographics. Early immunization reduced the risk of influenza healthcare utilization by 66% for subjects with diabetes or 67% for subjects without diabetes when compared with later vaccination for the 2013–2016 influenza seasons. Older age and female sex were associated with a higher incidence of influenza, but not a significant change in risk reduction from vaccination.ConclusionsThe risk for influenza-related healthcare utilization was 3.7-fold higher if patients had diabetes during 2013–2016 influenza seasons. Early immunization provides a significant benefit to adults irrespective of a diabetes diagnosis. All adults, but particularly patients with diabetes, should be encouraged to get the influenza vaccine at the start of the influenza season.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

The urgency and scale of the COVID-19 pandemic demanded a coordinated response from public health agencies and the biomedical research community. The National COVID Cohort Collaborative (N3C) was established as a centralized enclave in 2020 to support the study of COVID-19 across the U.S. The Institutional Development Award for Clinical and Translational Research (IDeA-CTR) centers enhanced N3C's national response by bringing representation from rural and medically underserved communities. This improved the representation of our diverse populations in the N3C Enclave and its use for research by IDeA-state investigators. We developed an organizational structure across the IDeA-CTRs to improve research productivity in resource-challenged areas of the U.S. This socio-technical ecosystem, informed by community input, included a governance committee and two workstreams. The operations workstream focused on data management and regulatory compliance, while the navigation, education, analysis, and training (NEAT) workstream supported educational and analytical activities for the N3C Enclave. Our collaborative approach led to participation by 12 IDeA-CTRs, representing over 400 investigators from 23 sites. The shared governance, investigator engagement, and resource pooling enhanced research productivity and engagement with researchers across IDeA states. Participation in this IDeA-CTR N3C consortium enhanced informatics research capacity and collaboration across the IDeA-CTRs for participating networks. This collaborative model provides a roadmap and framework for future efforts among IDeA-CTRs and other academic partnerships. The socio-technical ecosystem fostered collectivism and team science, enabling the consortium to achieve far more than isolated efforts could, offering valuable insights for interdisciplinary research across geographically dispersed communities.

The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycles are substituted with DNNs. We revisit R2D2 on the grounds of series convergence, training methodology, and DNN architecture, improving its robustness in terms of generalizability beyond training conditions, capability to deliver high data fidelity, and epistemic uncertainty. First, while still focusing on telescope-specific training, we enhance the learning process by randomizing Fourier sampling integration times, incorporating multiscan multinoise configurations, and varying imaging settings, including pixel resolution and visibility-weighting scheme. Second, we introduce a convergence criterion whereby the reconstruction process stops when the data residual is compatible with noise, rather than simply using all available DNNs. This not only increases the reconstruction efficiency by reducing its computational cost, but also refines training by pruning out the data/image pairs for which optimal data fidelity is reached before training the next DNN. Third, we substitute R2D2's early U-Net DNN with a novel architecture (U-WDSR) combining U-Net and WDSR, which leverages wide activation, dense skip connections, weight normalization, and low-rank convolution to improve feature reuse and reconstruction precision. As previously, R2D2 was trained for monochromatic intensity imaging with the Very Large Array at fixed \\(512 \\times 512\\) image size. Simulations on a wide range of inverse problems and a case study on real data reveal that the new R2D2 model consistently outperforms its earlier version in image reconstruction quality, data fidelity, and epistemic uncertainty.