Background Use of machine learning (ML) to identify clusters within heart failure with preserved ejection fraction (HFpEF) may inform development of specific therapies. Previous studies used comorbidity, echocardiographic, hemodynamic, biomarker, and myocardial transcriptomic data as ML input variables. We hypothesized that adding myocardial histopathologic findings (semiquantitative fibrosis and hypertrophy grade) as input variables may enhance ML generated HFpEF clustering and identify phenogroups with differing clinical features and outcomes. Methods We analyzed consecutive patients with HFpEF who underwent right ventricular septal endomyocardial biopsy (EMB) at our institution (2001-2022) for diagnostic evaluation (infiltrative, inflammatory, or hypertrophic cardiomyopathies excluded). A consensus clustering approach incorporating 23 different methods indicated the optimal number of clusters was 2. Model-based clustering was then performed (specifying 2 clusters) and variables providing highest discriminatory value (power > 1.0) were assessed. Results Among the 123 patients (median age 67.5 years, 50% female), cardiovascular conditions were common (Table 1). Cluster 1 was characterized by a higher prevalence of obesity, lower hemoglobin, and higher creatinine, E/e’, right and left filling pressures, pulmonary vascular resistance, H₂FPEF scores, and hypertrophy grade (Table 1) and higher all-cause mortality (log-rank p = 0.007, Figure 1B). Serum creatinine was the most discriminatory variable, followed by E/e’ ratio, H₂FPEF score, and pulmonary artery systolic pressure (Figure 1A), but histopathologic features had minimal discriminatory value (power <1.0 for both). Conclusions ML based clustering using routine clinical, echocardiographic and hemodynamic data as input variables identified two distinct HFpEF subtypes with differing clinical characteristics and outcomes. Histopathologic characteristics did not contribute meaningfully to cluster differentiation. These data reinforce the concept of HF, including HFpEF, as a systemic syndrome with renal function having a major contribution to pathophysiology.

Objective:Older patients with type 2 diabetes mellitus (T2DM) often face severe health challenges. This study aims to develop and validate a predictive model for estimating in-hospital death risk in this population. Methods:Clinical data of 17,421 patients with T2DM aged ≥ 65 years admitted to six hospitals in southwest China were collected retrospectively. Model performance was assessed through area under the receiver operating characteristic curve (AUROC) analysis and calibration plots. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC). Results:The overall in-hospital death rate was 3.19% (556 cases). Eleven independent predictors were identified: age, gender, history of surgery, Charlson Comorbidity Index score, coronary heart disease, chronic obstructive pulmonary disease, serum levels of creatinine, albumin, glycated hemoglobin, nutritional support drug use, and antibiotic drug use. The multivariable model demonstrated robust predictive accuracy with AUROC values of 0.873 (95% CI: 0.857-0.889) in training set, 0.830 (0.797-0.864) in internal validation set, and 0.834 (0.757-0.911) in external validation set. Bootstrap validation (n=1,000 resamples) confirmed adequate calibration. DCA and CIC analyses revealed substantial clinical net benefit across threshold probabilities. An interactive web-based calculator was implemented for clinical application (https://cqykdxtjt.shinyapps.io/in_hospital_death/). Conclusion:The prediction model developed in this study demonstrated robust discrimination, calibration, and clinical utility. It can assist healthcare professionals in identifying high-risk older patients with T2DM, facilitating early prevention, detection, and intervention, thereby reducing the risk of in-hospital death in this vulnerable population.

Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

BACKGROUND:Secondary tricuspid regurgitation (STR) in heart failure with preserved ejection fraction (HFpEF) is linked to more advanced stages with pulmonary vascular disease (PVD), but it may also develop at earlier stages of HFpEF, such as those with isolated exercise-induced congestion. OBJECTIVES:This study sought to evaluate the prevalence, distribution, and prognostic significance of STR mechanisms across the spectrum of HFpEF. METHODS:Cardiac structure, function, hemodynamics, and clinical outcomes were compared among patients with HFpEF phenotypes categorized according to the presence of PVD (pulmonary vascular resistance >2 WU), and elevation in filling pressure at rest vs provocation (leg-elevation or exercise), as well as according to the presence of atrial or ventricular STR (A-STR, V-STR). RESULTS:Of 1,091 patients (median age 65 years, 60.3% women), 669 (61.3%) had HFpEF (20.2% exercise HFpEF - PVD, 38.6% rest HFpEF - PVD, 15.5% exercise HFpEF + PVD, 25.7% rest HFpEF + PVD). Moderate or severe STR was present in 17.4% overall, but increased with PVD (6.7%, 11.6%, 18.3%, and 33.7%, respectively), although moderate or severe STR was still more common in exercise-only HFpEF vs noncardiac dyspnea patients (11.7% vs 6.5%; P = 0.047). Most STR (69%) fulfilled criteria for V-STR, but those individuals had similar atrial fibrillation prevalence and greater atrial remodeling compared with A-STR. V-STR was independently associated with composite of death or heart failure (HF) hospitalization (multivariable HR: 1.70; 95% CI: 1.10-2.65) as well as death and HF hospitalizations each alone, whereas A-STR was associated with increased risk of HF hospitalizations (multivariable Fine-Gray HR: 2.21; 95% CI: 1.12-4.37). CONCLUSIONS:STR in HFpEF is associated with PVD and less strongly with higher resting filling pressure. Although V-STR is the more common phenotype of STR in HFpEF, these patients also have substantial atrial myopathy, suggesting a mixed mechanism. TR was more prevalent in exercise HFpEF and exercise pulmonary hypertension than with noncardiac dyspnea; with none in the latter group having moderate-severe or severe TR, highlighting the importance of exercise hemodynamics. V-STR conferred excess mortality and HF hospitalizations, but A-STR conferred only excess HF hospitalizations.

BACKGROUND:The purpose of this study was to employ the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to mine and analyze adverse events related to iodinated contrast media (ICM), explore the characteristics of adverse events (AEs) including their occurrence and correlation strength between AEs and drugs, and to provide valuable insights for clinical use. METHODS:The FAERS database was queried, data from Q1 of 2004 to Q2 of 2023 were extracted, and AE reports targeting 5 ICMs as the primary suspects were collected. Data mining and analysis were carried out on relevant reports using the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), while the standardized medical dictionary for regulatory activities (MedDRA) queries (SMQ) was used for systematic classification. RESULTS:A total of 11,155,106 AE reports were retrieved from FAERS, with 2,412 for ioversol, 2,001 for iohexol, 987 for iodixanol, 1,154 for iopamidol, and 3,835 for iopromide. ICM-induced AE occurrence targeted 21 system organ classes (SOCs). A total of 329 significant disproportionality Preferred terms (PTs) conforming to the 4 algorithms were simultaneously retained. The results revealed that the medium and strong adverse drug reaction (ADR) signals of the 5 ICMs largely focused on "respiratory, thoracic and mediastinal disorders," "general disorders and administration site conditions," "immune system disorders," and "skin and subcutaneous tissue disorders." Ioversol (log2ROR = 1.21, Padj = 0.034) and iopromide (log2ROR = 1.32, Padj = 0.004) were both correlated with a higher incidence of a significant ADR signal, namely throat irritation, particularly in females. In addition, ioversol and iopromide also suggested that toxic nephropathy (log2ROR = -2.47, Padj < 0.001) and hyperhidrosis (log2ROR = -1.22, Padj = 0.001) were significant ADR signals, especially in males, respectively. CONCLUSIONS:While the AE distribution of the 5 ICMs was consistent, there were variations in specific ADR signal characteristics, warranting further consideration and exploration.

Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive functionality with ease of use, immunopipe empowers users to unlock valuable insights from the data, which notably advances our understanding of immune response and fosters the development of innovative immunotherapies. Results: Immunopipe is a comprehensive analytical pipeline for scRNA-seq and scTCR-seq data, offering various modules leveraging existing tools and novel methods to ensure data quality and enable downstream analytics. It performs quality control for scRNA-seq data, clusters cells based on gene expression to facilitate the identification of cell populations and provides insights into T-cell heterogeneity and functional diversity through T-cell selection and clustering. Enrichment analysis identifies markers and pathways for T-cell characterization. Our automated T-cell annotation tool annotates cell types accurately and hierarchically. For scTCR-seq data, immunopipe analyzes TCR clones, including clonality, diversity, gene usage, and repertoire overlap. Clone residency analysis examines the presence of clones across samples. Integrative analyses combine scRNA-seq and scTCR-seq data, providing a comprehensive understanding of the cellular landscape. Immunopipe incorporates diverse cell group information to analyze T-cell populations, which enables granular perspectives of the data for comprehensive insights. Immunopipe offers a flexible platform, allowing users to customize the analyses as needed. It supports both core and optional analyses through configuration, where users have control over the adjustable parameters. The pipeline relies on cell grouping information that can be referenced from metadata or modified to generate new variables for analysis and data filtering. Immunopipe can be executed locally or on scheduler platforms, and a docker image is provided for compatibility and simplified installation. It also offers a web interface that facilitates configuration, execution monitoring, and access to results. Conclusion: Immunopipe is a flexible and user-friendly pipeline for analyzing scRNA-seq and scTCR-seq data. It empowers researchers to gain valuable insights that could enhance the understanding of immune responses and facilitate the development of innovative immunotherapies. Citation Format: Panwen Wang, Haidong Dong, Yue Yu, Shuwen Zhang, Zhifu Sun, Jean-Pierre A. Kocher, Junwen Wang, Lin Yi, Ying Li. Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4959.

In this work, a mild green electrochemical method was used to synthesize two-dimensional MOFs. Electrochemical testing revealed that NiCo-CAT/CC exhibited a low overpotential accompanied by a small Tafel slope in a 1.0 M KOH solution.

This work studied message communications on patient portals and examined both the longitudinal trends and the correlations with characteristics of message senders. We analyzed over 5.6 million secure messages sent on the Mayo Clinic patient portal between February 18, 2010, and December 31, 2017. We studied the longitudinal changes in the number of portal messages, patient senders’ demographics and medical conditions (PheCodes), and provider senders’ care settings (e.g., primary or specialty) and practice roles (e.g., physician, nurse practitioner, and registered nurses). When compared to non-message-senders, patient message senders had a significantly higher proportion of the demographics: age 41-60, female, married, white, and English-speaking. From 2010-2017, an individual patient sent an average of 9.8 messages per person while a provider sent 418.4. The average number of PheCodes for all patients regardless of portal usage increased from 7.5 +/-6.9 in 2010 to 10.7 +/- 10.1 in 2017. The Pearson correlation coefficient between average PheCodes per patient and average messages per patient was 0.273 (p < 0.0001). Physicians were the largest proportion of message composers in both primary and specialty care (36.20% of primary, 37.54% of specialty). Starting 2013 onwards, specialty providers comprised the majority of portal providers while primary care providers remained stable around 20-22%. Our results show that patient portals are playing an increasingly significant role in supporting patient-provider communications. The longitudinal growth also sheds light on the possible challenge of communication overload for providers and the healthcare system.

BACKGROUND:Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS:A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS:The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users. CONCLUSION:In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.

Objectives To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. Methods Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). Conclusions The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

Background & Aims The prevalence of non-alcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is rising rapidly, yet its underlying mechanisms remain unclear. Herein, we aim to determine the role of hypoxia-inducible lipid droplet associated protein (HILPDA)/hypoxia-inducible gene 2 (HIG2), a selective inhibitor of intracellular lipolysis, in NASH-driven HCC. Methods The clinical significance of HILPDA was assessed in human NASH-driven HCC specimens by immunohistochemistry and transcriptomics analyses. The oncogenic effect of HILPDA was assessed in human HCC cells and in 3D epithelial spheroids upon exposure to free fatty acids and either normoxia or hypoxia. Lipidomics profiling of wild-type and HILPDA knockout HCC cells was assessed via shotgun and targeted approaches. Wild-type (Hilpdafl/fl) and hepatocyte-specific Hilpda knockout (HilpdaΔHep) mice were fed a Western diet and high sugar in drinking water while receiving carbon tetrachloride to induce NASH-driven HCC. Results In patients with NASH-driven HCC, upregulated HILPDA expression is strongly associated with poor survival. In oxygen-deprived and lipid-loaded culture conditions, HILPDA promotes viability of human hepatoma cells and growth of 3D epithelial spheroids. Lack of HILPDA triggered flux of polyunsaturated fatty acids to membrane phospholipids and of saturated fatty acids to ceramide synthesis, exacerbating lipid peroxidation and apoptosis in hypoxia. The apoptosis induced by HILPDA deficiency was reversed by pharmacological inhibition of ceramide synthesis. In our experimental mouse model of NASH-driven HCC, HilpdaΔHep exhibited reduced hepatic steatosis and tumorigenesis but increased oxidative stress in the liver. Single-cell analysis supports a dual role of hepatic HILPDA in protecting HCC cells and facilitating the establishment of a pro-tumorigenic immune microenvironment in NASH. Conclusions Hepatic HILPDA is a pivotal oncometabolic factor in the NASH liver microenvironment and represents a potential novel therapeutic target. Impact and implications Non-alcoholic steatohepatitis (NASH, chronic metabolic liver disease caused by buildup of fat, inflammation and damage in the liver) is emerging as the leading risk factor and the fastest growing cause of hepatocellular carcinoma (HCC), the most common form of liver cancer. While curative therapeutic options exist for HCC, it frequently presents at a late stage when such options are no longer effective and only systemic therapies are available. However, systemic therapies are still associated with poor efficacy and some side effects. In addition, no approved drugs are available for NASH. Therefore, understanding the underlying metabolic alterations occurring during NASH-driven HCC is key to identifying new cancer treatments that target the unique metabolic needs of cancer cells.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF. We observed significant variations in baseline characteristics and longitudinal EF change behavior of the HF cohorts from a previous study that is based on HF registry data. Data gathered from this longitudinal study were used to develop multiple machine learning models to predict changes in ejection fraction measurements in HF patients. Across all three sites, we observed higher performance in predicting EF increase over a 1-year duration, with similarly higher performance predicting an EF increase of 30% from baseline compared to lower percentage increases. In predicting EF decrease we found moderate to high performance with low confidence for various models. Among various machine learning models, XGBoost was the best performing model for predicting EF changes. Across the three sites, the XGBoost model had an F1-score of 87.2, 89.9, and 88.6 and AUC of 0.83, 0.87, and 0.90 in predicting a 30% increase in EF, and had an F1-score of 95.0, 90.6, 90.1 and AUC of 0.54, 0.56, 0.68 in predicting a 30% decrease in EF. Among features that contribute to predicting EF changes, baseline ejection fraction measurement, age, gender, and heart diseases were found to be statistically significant.

Background Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS). Methods 2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA). Results These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803–0.845); internal validation set: AUC = 0.847 (95%CI 0.818–0.876); external validation set: AUC = 0.732 (95%CI 0.521–0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790–0.837); internal validation set: AUC = 0.844 (95%CI 0.812–0.876); external validation set: AUC = 0.780 (95%CI 0.674–0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806–0.872); internal validation set: AUC = 0.800 (95%CI 0.751–0.849); external validation set: AUC = 0.821 (95%CI 0.727–0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant ( p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) ( p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function ( https://juntaotan.shinyapps.io/dynnomapp_hcc/ ). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC. Conclusions Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.

Importance:The ThermoCool SmartTouch catheter (ablation catheter with contact force and 6-hole irrigation [CF-I6]) is approved by the US Food and Drug Administration (FDA) for paroxysmal atrial fibrillation (AF) ablation and used in routine clinical practice for persistent AF ablation, although clinical outcomes for this indication are unknown. There is a need to understand whether data from routine clinical practice can be used to conduct regulatory-grade evaluations and support label expansions. Objective:To use health system data to compare the safety and effectiveness of the CF-I6 catheter for persistent AF ablation with the ThermoCool SmartTouch SurroundFlow catheter (ablation catheter with contact force and 56-hole irrigation [CF-I56]), which is approved by the FDA for this indication. Design, Setting, and Participants:This retrospective, comparative-effectiveness cohort study included patients undergoing catheter ablation for persistent AF at Mercy Health or Mayo Clinic from January 1, 2014, to April 30, 2021, with up to a 1-year follow-up using electronic health record data. Exposures:Use of the CF-I6 or CF-I56 catheter. Main Outcomes and Measures:The primary safety outcome was a composite of death, thromboembolic events, and procedural complications within 7 to 90 days. The exploratory effectiveness outcome was a composite of AF-related hospitalization events after a 90-day blanking period. Propensity score weighting was used to balance baseline covariates. Risk differences were estimated between catheter groups and averaged across the 2 health care systems, testing for noninferiority of the CF-I6 vs the CF-I56 catheter with respect to the safety outcome using 2-sided 90% CIs. Results:Overall, 1450 patients (1034 [71.3%] male; 1397 [96.3%] White) underwent catheter ablation for persistent AF, including 949 at Mercy Health (186 CF-I6 and 763 CF-I56; mean [SD] age, 64.9 [9.2] years) and 501 at Mayo Clinic (337 CF-I6 and 164 CF-I56; mean [SD] age, 63.7 [9.5] years). A total of 798 (55.0%) had been treated with class I or III antiarrhythmic drugs before ablation. The safety outcome (CF-I6 - CF-I56) was similar at both Mercy Health (1.3%; 90% CI, -2.1% to 4.6%) and Mayo Clinic (-3.8%; 90% CI, -11.4% to 3.7%); the mean difference was noninferior, with a mean of 0.5% (90% CI, -2.6% to 3.5%; P < .001). The effectiveness was similar at 12 months between the 2 catheter groups (mean risk difference, -1.8%; 90% CI, -7.3% to 3.7%). Conclusions and Relevance:In this cohort study, the CF-I6 catheter met the prespecified noninferiority safety criterion for persistent AF ablation compared with the CF-I56 catheter, and effectiveness was similar. This study demonstrates the ability of electronic health care system data to enable safety and effectiveness evaluations of medical devices.

Since the catalytic activity of present nickel-based synthetic selenide is still to be improved, MoSe2-Ni3Se2 was synthesized on nickel foam (NF) (MoSe2-Ni3Se2/NF) by introducing a molybdenum source. After the molybdenum source was introduced, the surface of the catalyst changed from a single-phase structure to a multi-phase structure. The catalyst surface with enriched active sites and the synergistic effect of MoSe2 and Ni3Se2 together enhance the hydrogen evolution reactions (HER), the oxygen evolution reactions (OER), and electrocatalytic total water splitting activity of the catalyst. The overpotential of the MoSe2-Ni3Se2/NF electrocatalyst is only 259 mV and 395 mV at a current density of 100 mA/cm2 for HER and OER, respectively. MoSe2-Ni3Se2/NF with a two-electrode system attains a current density of 10 mA/cm2 at 1.60 V. In addition, the overpotential of HER and OER of MoSe2-Ni3Se2/NF within 80000 s and the decomposition voltage of electrocatalytic total water decomposition hardly changed, showing an extremely strong stability. The improvement of MoSe2-Ni3Se2/NF catalytic activity is attributed to the establishment of the multi-phase structure and the optimized inoculation of the multi-component and multi-interface.

ABSTRACTCOVID-19 is a significant cause of morbidity and mortality in blood cancer patients, especially those on immunosuppressive therapy. Despite extensive research, the specific factor associated with SARS-CoV-2 infection that mediates the life-threatening inflammatory cytokine response in patients with severe COVID-19 remains unidentified. Herein we demonstrate that the virus-encoded Open Reading Frame 8 (ORF8) protein is abundantly secreted as a glycoprotein in vitro and in symptomatic patients with COVID-19. ORF8 specifically binds to the NOD-like receptor family pyrin domain-containing 3 (NLRP3) in CD14+ monocytes to induce a non-canonical inflammasomal response, and a canonical response when the second activation signal is present. Levels of ORF8 protein in the blood correlate with severity and disease-specific mortality in patients with acute SARS-CoV-2 infection. Furthermore, the ORF8-induced inflammasome response was readily inhibited by the NLRP3 inhibitor MCC950 in vitro. Our study identifies a dominant cause of pathogenesis, its underlying mechanism, and a potential new treatment for severe COVID-19.Key pointsSecreted glycoprotein ORF8 induces monocytic pro-inflammatory cytokines involving the activation of the NLPR3 inflammasome pathway.ORF8 is prognostically present in the blood of symptomatic patients with covid-19 and is targetable with NLRP3 inhibitor MCC-950.