Suillus hypogaeus: First record of a truffle Suillus.

Suillus (order Boletales) is a diverse genus of epigeous, mushroom-forming fungi native to temperate forests across the Northern Hemisphere; however, some species are also present in areas where Pinaceae has been introduced in the Southern Hemisphere. Unlike the closely related genus Rhizopogon, there are no described hypogeous, sequestrate species of Suillus. Here, we describe Suillus hypogaeus, the first known species of the genus with hypogeous, sequestrate sporocarps. Collections were made on Marys Peak in Benton County, Oregon, USA, at an elevation of 800 m in forests dominated by Pseudotsuga menziesii var. menziesii. The peridium is white, quickly staining pink to purple-reddish where bruised or cut. The gleba is pale yellow when young, becoming purple with maturity, and the basidiospores are obovoid, light yellow in KOH, and amyloid in Melzer's reagent. Multilocus molecular phylogenetic analyses support the placement of S. hypogaeus among the Larix specialists in the spectabilis group of Suillus. Although Larix and Pseudotsuga are sister genera, Larix does not occur on Marys Peak or elsewhere in western Oregon. Suillus hypogaeus, therefore, represents both an independent origin of the hypogeous, sequestrate sporocarp within the Boletales and an independent host shift between Larix and Pseudotsuga within the genus Suillus.

Volumetric Additive Manufacturing of Dicyclopentadiene by Solid-State Photopolymerization.

Polymerization in the solid state is generally infeasible due to restrictions on mobility. However, in this work, the solid-state photopolymerization of crystalline dicyclopentadiene is demonstrated via photoinitiated ring-opening metathesis polymerization. The source of mobility in the solid state is attributed to the plastic crystal nature of dicyclopentadiene, which yields local short-range mobility due to orientational degrees of freedom. Polymerization in the solid state enables photopatterning, volumetric additive manufacturing of free-standing structures, and fabrication with embedded components. Solid-state photopolymerization of dicyclopentadiene offers a new paradigm for advanced and freeform fabrication of high-performance thermosets.

Medicare Eligibility and Changes in Coverage, Access to Care, and Health by Sexual Orientation and Gender Identity.

Importance: Medicare provides nearly universal insurance coverage at age 65 years. However, how Medicare eligibility affects disparities in health insurance coverage, access to care, and health status among individuals by sexual orientation and gender identity is poorly understood. Objective: To assess the association of Medicare eligibility with disparities in health insurance coverage, access to care, and self-reported health status among individuals by sexual orientation and by gender identity. Design, Setting, and Participants: This cross-sectional study used the age discontinuity for Medicare eligibility at age 65 years to isolate the association of Medicare with health insurance coverage, access to care, and self-reported health status, by their sexual orientation and by their gender identity. Data were collected from the Behavioral Risk Factor Surveillance System for respondents from 51 to 79 years old from 2014 to 2021. Data analysis was performed from September 2022 to April 2023. Exposures: Medicare eligibility at age 65 years. Main Outcomes and Measures: Proportions of respondents with health insurance coverage, usual source of care, cost barriers to care, influenza vaccination, and self-reported health status. Results: The study population included 927 952 individuals (mean [SD] age, 64.4 [7.7] years; 524 972 [56.6%] females and 402 670 [43.4%] males), of whom 28 077 (3.03%) identified as a sexual minority-lesbian, gay, bisexual, or another sexual minority identity (LGB+) and 3286 (0.35%) as transgender or gender diverse. Respondents who identified as heterosexual had greater improvements at age 65 years in insurance coverage (4.2 percentage points [pp]; 95% CI, 4.0-4.4 pp) than those who identified as LGB+ (3.6 pp; 95% CI, 2.3-4.8 pp), except when the analysis was limited to a subsample of married respondents. For access to care, improvements in usual source of care, cost barriers to care, and influenza vaccination were larger at age 65 years for heterosexual respondents compared with LGB+ respondents, although confidence intervals were overlapping and less precise for LGB+ individuals. For self-reported health status, the analyses found larger improvements at age 65 years for LGB+ respondents compared with heterosexual respondents. There was considerable heterogeneity by state in disparities by sexual orientation among individuals who were nearly eligible for Medicare (close to 65 years old), with the US South and Central states demonstrating the highest disparities. Among the top-10 highest-disparities states, Medicare eligibility was associated with greater increases in coverage (6.7 pp vs 5.0 pp) and access to a usual source of care (1.4 pp vs 0.6 pp) for LGB+ respondents compared with heterosexual respondents. Conclusions and Relevance: The findings of this cross-sectional study indicate that Medicare eligibility was not associated with consistently greater improvements in health insurance coverage and access to care among LGBTQI+ individuals compared with heterosexual and/or cisgender individuals. However, among sexual minority individuals, Medicare may be associated with closing gaps in self-reported health status, and among states with the highest disparities, it may improve health insurance coverage, access to care, and self-reported health status.

Classification machine learning to detect de facto reuse and cyanobacteria at a drinking water intake.

Harmful algal blooms (HABs) or higher levels of de facto water reuse (DFR) can increase the levels of certain contaminants at drinking water intakes. Therefore, the goal of this study was to use multi-class supervised machine learning (SML) classification with data collected from six online instruments measuring fourteen total water quality parameters to detect cyanobacteria (corresponding to approximately 950 cells/mL, 2900 cells/mL, and 8600 cells/mL) or DFR (0.5, 1 and 2 % for wastewater effluent) events in the raw water entering an intake. Among 56 screened models from the caret package in R, four (mda, LogitBoost, bagFDAGCV, and xgbTree) were selected for optimization. mda had the greatest testing set accuracy, 98.09 %, after optimization with 7 false alerts. Some of the most important water parameters for the different models were phycocyanin-like fluorescence, UVA254, and pH. SML could detect algae blending events (estimated <9000 cells/mL) due in part to the phycocyanin-like fluorescence sensor. UVA254 helped identify higher concentrations of DFR. These results show that multi-class SML classification could be used at drinking water intakes in conjunction with online instrumentation to detect and differentiate HABs and DFR events. This could be used to create alert systems for the water utilities at the intake, rather than the finished water, so any adjustment to the treatment process could be implemented.

Improving Interpreter Access in the Pediatric Emergency Department: A Quality Improvement Initiative.

Background: An increasing proportion of the population in the United States have limited English proficiency (LEP). Hospitals that receive federal funding must offer interpreter services. However, access is often lacking for patients. Patients with LEP are at higher risk for adverse events, and the Emergency Department is a particularly high-risk environment for these events. Methods: This quality improvement initiative took place from April 2021 to August 2022 in an urban, tertiary care Pediatric Emergency Department. A driver diagram informed four Plan-Do-Study-Act cycles, and data were collected through medical record review, patient surveys, and staff surveys. We tracked outcomes using run and control chart data. Results: During the study period, the proportion of patients with LEP reporting "always" having an interpreter was unchanged (no centerline shift-control chart rules). Documentation of interpreter use for encounters with patients with LEP improved. Preferred language documentation and documentation of the need for an interpreter in the electronic medical record showed no change. Process measure data for staff-reported use of professional interpreters significantly increased, and the use of ad hoc interpreters decreased significantly. Length of stay did not change for English or LEP patients. Conclusions: This quality improvement initiative improved appropriate documentation of LEP and decreased use of nonqualified interpreters, although no change occurred in the proportion of patients who reported always having an interpreter. Patient satisfaction was unaffected.

Acute Inpatient Rehabilitation Functional Outcomes and Disposition After Liver Transplant.

Objective: To describe the outcomes (change in functional independence and discharge disposition) of patients who after liver transplantation received acute inpatient rehabilitation in a freestanding rehabilitation hospital. Design: A retrospective chart review was conducted of patients admitted to an acute inpatient rehabilitation hospital within 6 months of undergoing liver transplantation between January 2014 and December 2018. Change in function from rehabilitation admission to discharge was measured using FIM Change and FIM Efficiency. Setting: A freestanding rehabilitation hospital. Participants: 107 patients who underwent acute inpatient rehabilitation at a freestanding rehabilitation hospital within 6 months after liver transplantation who met inclusion criteria (N=107). Most were men (71.96%), and the mean age of the patient population was 62.15 years. Interventions: Acute inpatient rehabilitation consisting of at least 3 hours of therapy 5 days a week split between physical therapy, occupational therapy, and speech language pathology services. Main Outcome Measure: FIM Change, FIM Efficiency, Discharge Disposition. Results: Participants were found to have statistically significant positive FIM Change (P<.00001) and FIM Efficiency (P<.00001). The mean FIM Change and Efficiency were 35.7±11.8 and 2.4±1.0, respectively. 83.2% (n = 89) were ultimately discharged to the community. Conclusion: Acute inpatient rehabilitation provides patients who have received a liver transplant with the opportunity to measurably improve their function and independence, with most patients being able to return home.

Arabidopsis PROTODERMAL FACTOR2 binds lysophosphatidylcholines and transcriptionally regulates phospholipid metabolism.

Plant homeodomain leucine zipper IV (HD-Zip IV) transcription factors (TFs) contain an evolutionarily conserved steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domain. While the START domain is required for TF activity, its presumed role as a lipid sensor is not clear. Here we used tandem affinity purification from Arabidopsis cell cultures to demonstrate that PROTODERMAL FACTOR2 (PDF2), a representative member that controls epidermal differentiation, recruits lysophosphatidylcholines (LysoPCs) in a START-dependent manner. Microscale thermophoresis assays confirmed that a missense mutation in a predicted ligand contact site reduces lysophospholipid binding. We additionally found that PDF2 acts as a transcriptional regulator of phospholipid- and phosphate (Pi) starvation-related genes and binds to a palindromic octamer with consensus to a Pi response element. Phospholipid homeostasis and elongation growth were altered in pdf2 mutants according to Pi availability. Cycloheximide chase experiments revealed a role for START in maintaining protein levels, and Pi starvation resulted in enhanced protein destabilization, suggesting a mechanism by which lipid binding controls TF activity. We propose that the START domain serves as a molecular sensor for membrane phospholipid status in the epidermis. Our data provide insights toward understanding how the lipid metabolome integrates Pi availability with gene expression.

Evaluating the feasibility and acceptability of a co-designed physical activity intervention for rural middle schoolers: a pilot study.

BACKGROUND: Lack of physical activity is a concern for children across diverse backgrounds, particularly affecting those in rural areas who face distinct challenges compared to their urban counterparts. Community-derived interventions are needed that consider the unique context and additional physical activity barriers in under-resourced rural settings. Therefore, a prospective pre-post pilot/feasibility study of Hoosier Sport was conducted over 8-weeks with 6th and 7th grade children in a low-socioeconomic rural middle school setting. The primary objective of the present study was to assess trial- and intervention-related feasibility indicators; and the secondary objective was to collect preliminary assessment data for physical activity levels, fitness, psychological needs satisfaction, and knowledge of physical activity and nutrition among participating youth. METHODS: This prospective 8-week pilot/feasibility study took place in the rural Midwestern United States where twenty-four middle school students participated in a mixed-methods pre-post intervention during physical education classes. The intervention included elements like sport-based youth development, individualized goal setting, physical activity monitoring, pedometer usage, and health education. Data were collected at baseline (T1) and post-intervention (T3), with intermediate measures during the intervention (T2). Qualitative data were integrated through semi-structured interviews. Analytical methods encompassed descriptive statistics, correlations, repeated measures ANOVA, and thematic analysis. RESULTS: Key findings indicate robust feasibility, with intervention-related scores (FIM, AIM, and IAM) consistently surpassing the "good" threshold and 100% retention and recruitment success. Additionally, participants showed significant physical performance improvement, shifting from the 25th to the 50th percentile in the 6-minute walk test (p < 0.05). Autonomy and competence remained high, reflecting positive perceptions of program practicality. Nutrition knowledge, initially low, significantly improved at post-intervention (p < 0.01), highlighting the efficacy of targeted nutritional education in Hoosier Sport. CONCLUSIONS: This study pioneers a community-engaged model for physical activity intervention in under-resourced rural settings. Positive participant feedback, coupled with improvements in physical fitness and psychosocial factors, highlights the potential of the co-design approach. The findings offer valuable insights and a practical template for future community-based research, signaling the promising impact of such interventions on holistic well-being. This research lays the foundation for subsequent phases of the ORBIT model, emphasizing collaborative, community-driven approaches to address the complex issue of declining physical activity levels among adolescents.

Reusable tutorials for using cloud-based computing environments for the analysis of bacterial gene expression data from bulk RNA sequencing.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on RNA sequencing (RNAseq) data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Biomedical research is increasingly data-driven, and dependent upon data management and analysis methods that facilitate rigorous, robust, and reproducible research. Cloud-based computing resources provide opportunities to broaden the application of bioinformatics and data science in research. Two obstacles for researchers, particularly those at small institutions, are: (i) access to bioinformatics analysis environments tailored to their research; and (ii) training in how to use Cloud-based computing resources. We developed five reusable tutorials for bulk RNAseq data analysis to address these obstacles. Using Jupyter notebooks run on the Google Cloud Platform, the tutorials guide the user through a workflow featuring an RNAseq dataset from a study of prophage altered drug resistance in Mycobacterium chelonae. The first tutorial uses a subset of the data so users can learn analysis steps rapidly, and the second uses the entire dataset. Next, a tutorial demonstrates how to analyze the read count data to generate lists of differentially expressed genes using R/DESeq2. Additional tutorials generate read counts using the Snakemake workflow manager and Nextflow with Google Batch. All tutorials are open-source and can be used as templates for other analysis.

Fecal incontinence is not associated with UTI: A contemporary case-control study.

INTRODUCTION: Urinary tract infections (UTIs) are a leading cause of infection in adults. The most common cause is gastrointestinal bacteria ascending the urethra into the bladder. Studies showing fecal incontinence (FI) is a risk factor for UTI have been limited to nursing home populations. Healthy patients with recurrent UTI, especially women, often receive counseling, suggesting improper personal hygiene contributes to UTIs. This advice can be stigmatizing. Given UTI prevalence, it is important to elucidate risk factors for improved diagnosis, treatment, and patient education. Our objective was to perform a hospital-centered, retrospective case-control analysis to assess the effect of FI on UTI development in ambulatory patients. METHODS: Patients (n = 3035) with a diagnosis of FI were identified from a single institution and propensity score-matched with screening colonoscopy patients (n = 3035) from 2018 to 2021. Patients were matched on age, sex, race, ethnicity, body mass index, and comorbidities, for example, diabetes, vesicoureteral reflux, and urinary incontinence. The association between FI and UTI was tested using Pearson's χ2 test. RESULTS: Median age was 64 years with more females than males (73.81% vs. 71.20% female for case/control, p = 0.02). Patients with FI were more often to have concurrent urinary incontinence (18.62% vs. 10.25% for case/control, p < 0.001), as well as specifically urgency incontinence (13.28% vs. 11.57% for case/control, p = 0.04). There was no significant difference in the incidence of UTI between patients with FI and those presenting for screening colonoscopy (p = 0.44). CONCLUSION: FI was not associated with an increased number of UTIs. Based on our results, current stigmatizing beliefs regarding the association between FI and UTI should be reevaluated.

Formative peer evaluation instrument for a team-based learning course: Content and construct validity.

PURPOSE: Team-based learning (TBL) is an evidence-based approach to promote teamwork. Peer evaluation (PE) is an essential component to shape future team engagement and promote reflection. As PEs vary in use, implementation, and assessment, this study establishes the content and construct validity of a formative PE tool for a TBL course. METHODS: A ten-item instrument was developed based on a comprehensive review of PE literature and was critically edited by a team of experienced educators. Each student in a graduate histology course rated peers at two timepoints on a scale from Never to Always (0-3). The instrument's factor structure was analyzed by dividing the response set (D1 and D2); with D1 utilized for exploratory factor analysis (EFA) and D2 for confirmatory factor analysis (CFA). Cronbach's alpha assessed internal consistency. RESULTS: Data from 158 students across four cohorts were included in the analyses (D1, D2 = 972). A three-factor solution had good overall internal consistency (alpha = 0.82), and within the subscales ranged from 0.67 to 0.81. The factor structures were resonant with existing literature on (1) preparation, (2) providing feedback, and (3) feedback receptivity and attitude. CONCLUSION: This study establishes evidence of content and construct validity of a formative PE instrument for a TBL course.

Oscillation steers differentiation.

The differentiation trajectories defining enteroendocrine (EE) cell heterogeneity remain obscure. In this issue of Cell Stem Cell, Singh et al.1 map the differentiation landscape of EE cells, identifying early oscillating cell progenitor states, which play a critical role in generating terminal EE cell diversity.

The Potential Clinical Benefits of Direct Surgical Transgastric Pancreatic Necrosectomy for Patients With Infected Necrotizing Pancreatitis.

OBJECTIVE: Surgical transgastric pancreatic necrosectomy (STGN) has the potential to overcome the shortcomings (ie, repeat interventions, prolonged hospitalization) of the step-up approach for infected necrotizing pancreatitis. We aimed to determine the outcomes of STGN for infected necrotizing pancreatitis. MATERIALS AND METHODS: This observational cohort study included adult patients who underwent STGN for infected necrosis at two centers from 2008 to 2022. Patients with a procedure for pancreatic necrosis before STGN were excluded. Primary outcomes included mortality, length of hospital and intensive care unit (ICU) stay, new-onset organ failure, repeat interventions, pancreatic fistulas, readmissions, and time to episode closure. RESULTS: Forty-three patients underwent STGN at a median of 48 days (interquartile range [IQR] 32-70) after disease onset. Mortality rate was 7% (n = 3). After STGN, the median length of hospital was 8 days (IQR 6-17), 23 patients (53.5%) required ICU admission (2 days [IQR 1-7]), and new-onset organ failure occurred in 8 patients (18.6%). Three patients (7%) required a reintervention, 1 (2.3%) developed a pancreatic fistula, and 11 (25.6%) were readmitted. The median time to episode closure was 11 days (IQR 6-22). CONCLUSIONS: STGN allows for treatment of retrogastric infected necrosis in one procedure and with rapid episode resolution. With these advantages and few pancreatic fistulas, direct STGN challenges the step-up approach.

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed.

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.

Identification of State Markers in Anorexia Nervosa: Replication and Extension of Inflammation-Associated Biomarkers Using Multiplex Profiling.

Background: Proteomics offers potential for detecting and monitoring anorexia nervosa (AN) and its variant, atypical AN (atyp-AN). However, research has been limited by small protein panels, a focus on adult AN, and lack of replication. Methods: In this study, we performed Olink multiplex profiling of 92 inflammation-related proteins in females with AN/atyp-AN (n = 64), all of whom were ≤90% of expected body weight, and age-matched healthy control individuals (n = 44). Results: Five proteins differed significantly between the primary AN/atyp-AN group and the healthy control group (lower levels: HGF, IL-18R1, TRANCE; higher levels: CCL23, LIF-R). The expression levels of 3 proteins (lower IL-18R1, TRANCE; higher LIF-R) were uniquely disrupted in participants with AN in our primary model. No unique expression levels emerged for atyp-AN. In the total sample, 12 proteins (ADA, CD5, CD6, CXCL1, FGF-21, HGF, IL-12B, IL18, IL-18R1, SIRT2, TNFSF14, TRANCE) were positively correlated with body mass index and 5 proteins (CCL11, FGF-19, IL8, LIF-R, OPG) were negatively correlated with body mass index in our primary models. Conclusions: Our results replicate the results of a previous study that demonstrated a dysregulated inflammatory status in AN and extend those results to atyp-AN. Of the 17 proteins correlated with body mass index, 11 were replicated from a previous study that used similar methods, highlighting the promise of inflammatory protein expression levels as biomarkers of AN disease monitoring. Our findings underscore the complexity of AN and atyp-AN by highlighting the inability of the identified proteins to differentiate between these 2 subtypes, thereby emphasizing the heterogeneous nature of these disorders.

We examined 73 inflammation proteins in adolescent girls with anorexia nervosa (AN) and atypical AN and compared them with age-matched healthy control girls. Significant differences were found, driven by 5 key proteins (lower: HGF, IL-18R1, TRANCE; higher: CCL23, LIF-R). Three proteins (TRANCE, LIF-R, IL-18R1) uniquely distinguished low-weight participants with AN from control participants. Our study reveals distinct inflammation patterns in AN and atypical AN and sheds light on potential state-specific factors that underlie these disorders.

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.

BACKGROUND AND OBJECTIVE: To develop the COVid Veteran (COVet) score for clinical deterioration in Veterans hospitalized with COVID-19 and further validate this model in both Veteran and non-Veteran samples. No such score has been derived and validated while incorporating a Veteran sample. DERIVATION COHORT: Adults (age ≥ 18 yr) hospitalized outside the ICU with a diagnosis of COVID-19 for model development to the Veterans Health Administration (VHA) (n = 80 hospitals). VALIDATION COHORT: External validation occurred in a VHA cohort of 34 hospitals, as well as six non-Veteran health systems for further external validation (n = 21 hospitals) between 2020 and 2023. PREDICTION MODEL: eXtreme Gradient Boosting machine learning methods were used, and performance was assessed using the area under the receiver operating characteristic curve and compared with the National Early Warning Score (NEWS). The primary outcome was transfer to the ICU or death within 24 hours of each new variable observation. Model predictor variables included demographics, vital signs, structured flowsheet data, and laboratory values. RESULTS: A total of 96,908 admissions occurred during the study period, of which 59,897 were in the Veteran sample and 37,011 were in the non-Veteran sample. During external validation in the Veteran sample, the model demonstrated excellent discrimination, with an area under the receiver operating characteristic curve of 0.88. This was significantly higher than NEWS (0.79; p < 0.01). In the non-Veteran sample, the model also demonstrated excellent discrimination (0.86 vs. 0.79 for NEWS; p < 0.01). The top three variables of importance were eosinophil percentage, mean oxygen saturation in the prior 24-hour period, and worst mental status in the prior 24-hour period. CONCLUSIONS: We used machine learning methods to develop and validate a highly accurate early warning score in both Veterans and non-Veterans hospitalized with COVID-19. The model could lead to earlier identification and therapy, which may improve outcomes.

Relationship between alcohol consumption and dementia with Mendelian randomization approaches among older adults in the United States.

INTRODUCTION: In observational studies, the association between alcohol consumption and dementia is mixed. METHODS: We performed two-sample Mendelian randomization (MR) using summary statistics from genome-wide association studies of weekly alcohol consumption and late-onset Alzheimer's disease and one-sample MR in the Health and Retirement Study (HRS), wave 2012. Inverse variance weighted two-stage regression provided odds ratios of association between alcohol exposure and dementia or cognitively impaired, non-dementia relative to cognitively normal. RESULTS: Alcohol consumption was not associated with late-onset Alzheimer's disease using two-sample MR (odds ratio [OR] = 1.15, 95% confidence interval [CI]: [0.78, 1.72]). In HRS, doubling weekly alcohol consumption was not associated with dementia (African ancestries, n = 1,322, OR = 1.00, 95% CI [0.45, 2.25]; European ancestries, n = 7,160, OR = 1.37, 95% CI [0.53, 3.51]) or cognitively impaired, non-dementia (African ancestries, n = 1,322, OR = 1.17, 95% CI [0.69, 1.98]; European ancestries, n = 7,160, OR = 0.75, 95% CI [0.47, 1.22]). DISCUSSION: Alcohol consumption was not associated with cognitively impaired, non-dementia or dementia status. Highlights: Cross-sectionally in a large, diverse sample, alcohol appears protective for dementia.We apply two- and one-sample Mendelian randomization to test inferred causality.Mendelian randomization approaches show no association with alcohol and dementia.We conclude that alcohol consumption should not be considered protective.

Obesity-associated microbiomes instigate visceral adipose tissue inflammation by recruitment of distinct neutrophils.

Neutrophils are increasingly implicated in chronic inflammation and metabolic disorders. Here, we show that visceral adipose tissue (VAT) from individuals with obesity contains more neutrophils than in those without obesity and is associated with a distinct bacterial community. Exploring the mechanism, we gavaged microbiome-depleted mice with stool from patients with and without obesity during high-fat or normal diet administration. Only mice receiving high-fat diet and stool from subjects with obesity show enrichment of VAT neutrophils, suggesting donor microbiome and recipient diet determine VAT neutrophilia. A rise in pro-inflammatory CD4+ Th1 cells and a drop in immunoregulatory T cells in VAT only follows if there is a transient spike in neutrophils. Human VAT neutrophils exhibit a distinct gene expression pattern that is found in different human tissues, including tumors. VAT neutrophils and bacteria may be a novel therapeutic target for treating inflammatory-driven complications of obesity, including insulin resistance and colon cancer.