Stabilization of Cx43 mRNA via RNA-binding protein HuR regulated by polyamines enhances intestinal epithelial barrier function.

Gut barrier dysfunction occurs commonly in patients with critical disorders, leading to the translocation of luminal toxic substances and bacteria to the bloodstream. Connexin 43 (Cx43) acts as a gap junction protein and is crucial for intercellular communication and the diffusion of nutrients. The levels of cellular Cx43 are tightly regulated by multiple factors, including polyamines, but the exact mechanism underlying the control of Cx43 expression remains largely unknown. The RNA-binding protein HuR regulates the stability and translation of target mRNAs and is involved in many aspects of intestinal epithelial pathobiology. Here we show that HuR directly bound to Cx43 mRNA via its 3'-untranslated region in intestinal epithelial cells (IECs) and this interaction enhanced Cx43 expression by stabilizing Cx43 mRNA. Depletion of cellular polyamines inhibited the [HuR/Cx43 mRNA] complex and decreased the level of Cx43 protein by destabilizing its mRNA, but these changes were prevented by ectopic overexpression of HuR. Polyamine depletion caused intestinal epithelial barrier dysfunction, which was reversed by ectopic Cx43 overexpression. Moreover, overexpression of checkpoint kinase 2 in polyamine-deficient cells increased the [HuR/Cx43 mRNA] complex, elevated Cx43 levels, and promoted barrier function. These findings indicate that Cx43 mRNA is a novel target of HuR in IECs and that polyamines regulate Cx43 mRNA stability via HuR, thus playing a critical role in the maintenance of intestinal epithelial barrier function.NEW & NOTEWORTHY The current study shows that polyamines stabilize the Cx43 mRNA via HuR, thus enhancing the function of the Cx43-mediated gap junction. These findings suggest that induced Cx43 by HuR plays a critical role in the process by which polyamines regulate intestinal epithelial barrier.

Association of Reduced Hospitalizations and Mortality Rates Among COVID-19-Vaccinated Patients With Heart Failure.

BACKGROUND: Patients with heart failure (HF) are at high risk for adverse outcomes when they have COVID-19. Reports of COVID-19 vaccine-related cardiac complications may contribute to vaccine hesitancy in patients with HF. METHODS: To analyze the impact of COVID-19 vaccine status on clinical outcomes in patients with HF, we conducted a retrospective cohort study of the association of COVID-19 vaccination status with hospitalizations, intensive care unit admission and mortality after adjustment for covariates. Inverse probability treatment-weighted models were used to adjust for potential confounding. RESULTS: Of 7094 patients with HF, 645 (9.1%) were partially vaccinated, 2200 (31.0%) were fully vaccinated, 1053 were vaccine-boosted (14.8%), and 3196 remained unvaccinated (45.1%) by January 2022. The mean age was 73.3 ± 14.5 years, and 48% were female. Lower mortality rates were observed in patients who were vaccine-boosted, followed by those who were fully vaccinated; they experienced lower mortality rates (HR 0.33; CI 0.23, 0.48) and 0.36 (CI 0.30, 0.43), respectively, compared to unvaccinated individuals (P< 0.001) over the mean follow-up time of 276.5 ± 104.9 days, whereas no difference was observed between those who were unvaccinated or only partially vaccinated. CONCLUSION: COVID-19 vaccination was associated with significant reduction in all-cause hospitalization rates and mortality rates, lending further evidence to support the importance of vaccination implementation in the high-risk population of patients living with HF.

Non-invasive ventilation versus mechanical ventilation in hypoxemic patients with COVID-19.

PURPOSE: Limited mechanical ventilators (MV) during the Coronavirus disease (COVID-19) pandemic have led to the use of non-invasive ventilation (NIV) in hypoxemic patients, which has not been studied well. We aimed to assess the association of NIV versus MV with mortality and morbidity during respiratory intervention among hypoxemic patients admitted with COVID-19. METHODS: We performed a retrospective multi-center cohort study across 5 hospitals during March-April 2020. Outcomes included mortality, severe COVID-19-related symptoms, time to discharge, and final oxygen saturation (SpO2) at the conclusion of the respiratory intervention. Multivariable regression of outcomes was conducted in all hypoxemic participants, 4 subgroups, and propensity-matched analysis. RESULTS: Of 2381 participants with laboratory-confirmed SARS-CoV-2, 688 were included in the study who were hypoxemic upon initiation of respiratory intervention. During the study period, 299 participants died (43%), 163 were admitted to the ICU (24%), and 121 experienced severe COVID-19-related symptoms (18%). Participants on MV had increased mortality than those on NIV (128/154 [83%] versus 171/534 [32%], OR = 30, 95% CI 16-60) with a mean survival of 6 versus 15 days, respectively. The MV group experienced more severe COVID-19-related symptoms [55/154 (36%) versus 66/534 (12%), OR = 4.3, 95% CI 2.7-6.8], longer time to discharge (mean 17 versus 7.1 days), and lower final SpO2 (92 versus 94%). Across all subgroups and propensity-matched analysis, MV was associated with a greater OR of death than NIV. CONCLUSIONS: NIV was associated with lower respiratory intervention mortality and morbidity than MV. However, findings may be liable to unmeasured confounding and further study from randomized controlled trials is needed to definitively determine the role of NIV in hypoxemic patients with COVID-19.

Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury.

BACKGROUND/AIMS: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. METHODS: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. RESULTS: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52-84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67-0.73), followed by MLP 0.59 (0.54-0.64), LR 0.57 (0.52-0.62), SVM 0.51 (0.46-0.56), AdaBoost 0.51 (0.46-0.55), RF 0.44 (0.39-0.48), and XGBoost 0.43 (CI 0.38-0.47). CONCLUSIONS: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

Genetic dependency of Alzheimer's disease-associated genes across cells and tissue types.

Effective treatments targeting disease etiology are urgently needed for Alzheimer's disease (AD). Although candidate AD genes have been identified and altering their levels may serve as therapeutic strategies, the consequence of such alterations remain largely unknown. Herein, we analyzed CRISPR knockout/RNAi knockdown screen data for over 700 cell lines and evaluated cellular dependencies of 104 AD-associated genes previously identified by genome-wide association studies (GWAS) and gene expression network studies. Multiple genes showed widespread cell dependencies across tissue lineages, suggesting their inhibition may yield off-target effects. Meanwhile, several genes including SPI1, MEF2C, GAB2, ABCC11, ATCG1 were identified as genes of interest since their genetic knockouts specifically affected high-expressing cells whose tissue lineages are relevant to cell types found in AD. Overall, analyses of genetic screen data identified AD-associated genes whose knockout or knockdown selectively affected cell lines of relevant tissue lineages, prioritizing targets for potential AD treatments.

Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City.

Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.

Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics: predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.

Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure.

Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.

Contrastive learning improves critical event prediction in COVID-19 patients.

Deep learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus disease 2019 (COVID-19) pandemic, where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL), which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL, especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR dataset to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full dataset and a restricted dataset. CL models consistently outperform CEL models, with differences ranging from 0.04 to 0.15 for area under the precision and recall curve (AUPRC) and 0.05 to 0.1 for area under the receiver-operating characteristic curve (AUROC).

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

BACKGROUND AND OBJECTIVES: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. RESULTS: A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction. CONCLUSIONS: An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.

Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. METHODS AND FINDINGS: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. CONCLUSIONS: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Long Noncoding RNA H19 Impairs the Intestinal Barrier by Suppressing Autophagy and Lowering Paneth and Goblet Cell Function.

BACKGROUND & AIMS: The protective intestinal mucosal barrier consists of multiple elements including mucus and epithelial layers and immune defense; nonetheless, barrier dysfunction is common in various disorders. The imprinted and developmentally regulated long noncoding RNA H19 is involved in many cell processes and diseases. Here, we investigated the role of H19 in regulating Paneth and goblet cells and autophagy, and its impact on intestinal barrier dysfunction induced by septic stress. METHODS: Studies were conducted in H19-deficient (H19-/-) mice, mucosal tissues from patients with sepsis, primary enterocytes, and Caco-2 cells. Septic stress was induced by cecal ligation and puncture (CLP), and gut permeability was detected by tracer fluorescein isothiocyanate-dextran assays. The function of Paneth and goblet cells was examined by immunostaining for lysozyme and mucin 2, respectively, and autophagy was examined by microtubule-associated proteins 1A/1B light chain 3 II immunostaining and Western blot analysis. Intestinal organoids were isolated from H19-/- and control littermate mice and treated with lipopolysaccharide (LPS). RESULTS: Intestinal mucosal tissues in mice 24 hours after exposure to CLP and in patients with sepsis showed high H19 levels, associated with intestinal barrier dysfunction. Targeted deletion of the H19 gene in mice enhanced the function of Paneth and goblet cells and promoted autophagy in the small intestinal mucosa. Knockout of H19 protected Paneth and goblet cells against septic stress, preserved autophagy activation, and promoted gut barrier function after exposure to CLP. Compared with organoids from control littermate mice, intestinal organoids isolated from H19-/- mice had increased numbers of lysozyme- and mucin 2-positive cells and showed increased tolerance to LPS. Conversely, ectopic overexpression of H19 in cultured intestinal epithelial cells prevented rapamycin-induced autophagy and abolished the rapamycin-induced protection of the epithelial barrier against LPS. CONCLUSIONS: In investigations of mice, human tissues, primary organoids, and intestinal epithelial cells, we found that increased H19 inhibited the function of Paneth and goblet cells and suppressed autophagy, thus potentially contributing to barrier dysfunction in intestinal pathologies.

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

OBJECTIVE: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS: Participants over the age of 18 years were included. PRIMARY OUTCOMES: We investigated in-hospital mortality during the study period. RESULTS: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 µg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 µg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

H19 Long Noncoding RNA Regulates Intestinal Epithelial Barrier Function via MicroRNA 675 by Interacting with RNA-Binding Protein HuR.

The disruption of the intestinal epithelial barrier function occurs commonly in various pathologies, but the exact mechanisms responsible are unclear. The H19 long noncoding RNA (lncRNA) regulates the expression of different genes and has been implicated in human genetic disorders and cancer. Here, we report that H19 plays an important role in controlling the intestinal epithelial barrier function by serving as a precursor for microRNA 675 (miR-675). H19 overexpression increased the cellular abundance of miR-675, which in turn destabilized and repressed the translation of mRNAs encoding tight junction protein ZO-1 and adherens junction E-cadherin, resulting in the dysfunction of the epithelial barrier. Increasing the level of the RNA-binding protein HuR in cells overexpressing H19 prevented the stimulation of miR-675 processing from H19, promoted ZO-1 and E-cadherin expression, and restored the epithelial barrier function to a nearly normal level. In contrast, the targeted deletion of HuR in intestinal epithelial cells enhanced miR-675 production in the mucosa and delayed the recovery of the gut barrier function after exposure to mesenteric ischemia/reperfusion. These results indicate that H19 interacts with HuR and regulates the intestinal epithelial barrier function via the H19-encoded miR-675 by altering ZO-1 and E-cadherin expression posttranscriptionally.