Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

MOTIVATION: Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. RESULTS: We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. AVAILABILITY AND IMPLEMENTATION: Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Incremental Value of Right Ventricular Outflow Tract Diameter in Risk Assessment of Chronic Heart Failure Patients with Implantable Cardioverter Defibrillators: Development of RVOTD-ICD Benefit Score in Real-World Setting.

Background: Left ventricular ejection fraction (LVEF) remains the basic reference for the prevention of sudden cardiac death (SCD) patients, while right ventricular (RV) abnormalities have now been associated with SCD risk. A modified benefit assessment tool incorporating RV function parameters in consideration of implantable cardioverter defibrillators (ICD) insertion should be taken into account. Methods: We enrolled 954 chronic heart failure (CHF) patients (age 58.8 ± 13.1 years; 79.0% male) with quantitative measurements of right ventricular outflow tract diameter (RVOTD) before ICD implantation and then divided them according to the median level of RVOTD. The predictive value of RVOTD in life-threatening ventricular tachycardia (VT)/ventricular fibrillation (VF) vs. non-arrhythmic mortality (defined as death without prior sustained VT/VF), was evaluated respectively. Based on RVOTD and other identified risk factors, a simple risk assessment tool, RVOTD-ICD benefit score, was developed. Results: A higher RVOTD level was significantly associated with an increased risk of VT/VF (per 1 standard deviation (SD) increase, hazard ratio [HR], 1.22; 95% confidence interval [CI], 1.11-1.33; p = 0.002) but not non-arrhythmic mortality (per 1 SD increase, hazard ratio, 0.93; 95% CI, 0.66-1.33; p = 0.709) after multivariable adjustment. Three benefit groups were created based on RVOTD-ICD benefit score, which was calculated from VT/VF score (younger age, higher RVOTD, diuretic use, prior non-sustainable VT, prior sustainable VT/VF) and non-arrhythmic mortality scores (older age, renin-angiotensin-aldosterone system inhibitors use, diabetes, higher left ventricular end-diastolic diameter, New York Heart Association III/IV, higher N-terminal pro-B-type natriuretic peptide levels). In the highest RVOTD-ICD benefit group, the 3-year risk of VT/VF was nearly 8-fold higher than the corresponding risk of non-arrhythmic mortality (39.2% vs. 4.8%, p < 0.001). On the contrary, the 3-year risk of VT/VF was similar to the risk of non-arrhythmic mortality (21.9% vs. 21.3%, p = 0.405) in the lowest benefit group. RVOTD-ICD benefit score system yielded improvement in discrimination for VT/VF, non-arrhythmic mortality, and all-cause mortality than Multicenter Automatic Defibrillator Implantation Trial (MADIT)-ICD benefit score in this cohort. Conclusions: Higher RVOTD was associated with significantly increased risk of sustained VT/VF in CHF patients. A simple risk assessment tool incorporating RVOTD (RVOTD-ICD benefit score) could be generalized to ICD populations, and optimize the decision-making process of ICD implantation.

Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study.

Background: Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses. Methods: A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups. Results: During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p < 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p < 0.001, respectively; p for trend < 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p < 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1. Conclusions: Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.

MYT1L deficiency impairs excitatory neuron trajectory during cortical development.

Mutations that reduce the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. Furthermore, MYT1L is routinely used as a proneural factor in fibroblast-to-neuron transdifferentiation. MYT1L has been hypothesized to play a role in the trajectory of neuronal specification and subtype specific maturation, but this hypothesis has not been directly tested, nor is it clear which neuron types are most impacted by MYT1L loss. In this study, we profiled 313,335 nuclei from the forebrains of wild-type and MYT1L-deficient mice at two developmental stages: E14 at the peak of neurogenesis and P21, when neurogenesis is complete, to examine the role of MYT1L levels in the trajectory of neuronal development. We found that MYT1L deficiency significantly disrupted the relative proportion of cortical excitatory neurons at E14 and P21. Significant changes in gene expression were largely concentrated in excitatory neurons, suggesting that transcriptional effects of MYT1L deficiency are largely due to disruption of neuronal maturation programs. Most effects on gene expression were cell autonomous and persistent through development. In addition, while MYT1L can both activate and repress gene expression, the repressive effects were most sensitive to haploinsufficiency, and thus more likely mediate MYT1L syndrome. These findings illuminate the intricate role of MYT1L in orchestrating gene expression dynamics during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome.

Effect of short-term cardiac function changes after cardiac resynchronization therapy on long-term prognosis in heart failure patients with and without diabetes.

Background: The relationship between short-term cardiac function changes and long-term outcomes in heart failure (HF) patients undergoing cardiac resynchronization therapy (CRT) remains uncertain, especially when stratified by diabetes status. Objectives: This study aims to assess the association between short-term cardiac function changes and outcomes such as all-cause mortality and HF hospitalization in patients undergoing CRT, stratified by diabetes status. Design: This is a cohort longitudinal retrospective study. Methods: A total of 666 HF patients, treated with CRT between March 2007 and March 2019, were included in this study. Among them, 166 patients (24.9%) were diagnosed with diabetes. Cardiac function was assessed at baseline and again at 6 months, incorporating evaluations of left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVEDD), left atrial diameter (LAD), N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and QRS duration. The QRS duration represents the time required for a stimulus to spread through the ventricles (ventricular depolarization). The primary endpoints of the study were all-cause mortality and HF-related hospitalization. Results: During a median follow-up of 2.51 years, 172 (25.8%) patients died and 197 (29.6%) were hospitalized for HF. Changes in LVEF, LVEDD, and LAD within 6 months had similar effects on adverse outcomes in both diabetic and nondiabetic patients. However, the presence of diabetes significantly modified the association between changes in NT-proBNP and QRS duration and adverse outcomes. Short-term changes in NT-proBNP and QRS duration were positively associated with all-cause mortality and HF hospitalization in patients without diabetes. However, the relationship between short-term changes in NT-proBNP and QRS duration and adverse outcomes was non-linear in diabetic patients. Conclusion: Improvement of cardiac function after CRT implantation can reduce long-term risk of all-cause mortality and HF hospitalization in HF patients. However, the presence of diabetes may affect the association between short-term changes in NT-proBNP and QRS duration and adverse outcomes.

Implication of geriatric nutritional risk index on treatment response and long-term prognosis in patients with cardiac resynchronization therapy.

PURPOSE: Geriatric Nutritional Risk Index (GNRI) is a simple tool for assessing the nutritional status of the aging population. This study aims to explore the clinical implication of GNRI on treatment response and long-term clinical outcomes in heart failure (HF) patients receiving cardiac resynchronization therapy (CRT). METHODS: Patients who underwent CRT implantation or upgrade at our hospital were retrospectively included. The association of GNRI and its tertiles with the echocardiographic response, all-cause mortality or heart transplantation, and the first hospitalization due to HF were investigated. RESULTS: Totally, 647 patients were enrolled, with a median age of 60 [Interquartile Range (IQR): 52-67] years and mean score of GNRI at 107.9 ± 23.7. Super-response rates increased significantly among the GNRI T1, T2, and T3 groups (25.1%, 29.8% vs. 41.1%, P = 0.002). Patients with higher GNRI were more likely to have better LVEF improvement after multiple adjustments (OR = 1.13, 95% CI: 1.04-1.23, P = 0.010). Higher GNRI was independently associated with a lower risk of all-cause mortality or heart implantation (HR = 0.95, 95% CI: 0.93-0.96, P < 0.001) and HF hospitalization (HR = 0.96, 95% CI: 0.95-0.98, P < 0.001). The inclusion of GNRI enhanced the predictability of all-cause mortality based on traditional model, including sex, New York Heart Association functional class, left bundle branch block, QRS reduction, and N-terminal pro-B-type natriuretic peptide level (C statistics improved from 0.785 to 0.813, P = 0.007). CONCLUSION: Higher GNRI was associated with better treatment response and long-term prognosis in HF patients with CRT. Evaluation of nutritional status among CRT population is necessary for individualized choice of potential responders.

Association between triglyceride-glucose index and the risk of heart failure hospitalization in older diabetic patients received right ventricular pacing: a retrospective cohort study.

BACKGROUND: The prognostic value of triglyceride-glucose (TyG) index is not yet known for older diabetic patients received right ventricular pacing (RVP). We aimed to investigate the association between TyG index and the risk of heart failure hospitalization (HFH) in older diabetic patients received RVP. METHODS: This study was conducted between January 2017 and January 2018 at Fuwai Hospital, Beijing, China, and included older (age ≥ 65 years) diabetic patients that received RVP for the first time. TyG index were obtained before implantation. The primary endpoint was HFH. RESULTS: A total of 231 patients were divided into three groups according to the tertiles of TyG index: < 8.5 (T1, N = 77), 8.5-9.1 (T2, N = 77), and > 9.1 (T3, N = 77). T3 group had higher rate of HFH (Log-rank = 11.7, P = 0.003). Multivariate analyses showed that, TyG index served as an independent predictor for HFH, both as numerical variable (HR = 1.94, 95% CI 1.21-3.11, P = 0.006), and as categorical variable (HR = 2.31, 95% CI 1.09-4.89, P = 0.03). RCS demonstrated that the risk of HFH was relatively low until TyG index exceeded 8.8, beyond which the risk began to increase rapidly (P-non-linear = 0.006). CONCLUSION: Preimplantation TyG index emerges as a robust, independent predictor for HFH in older diabetic patients received RVP, and TyG index > 8.8 might be the optimal cut-off value.

Challenges and support needs in psychological and physical health among pilots: a qualitative study.

Introduction: Physical and mental health problems among pilots affect their working state and impact flight safety. Although pilots' physical and mental health problems have become increasingly prominent, their health has not been taken seriously. This study aimed to clarify challenges and support needs related to psychological and physical health among pilots to inform development of a more scientific and comprehensive physical and mental health system for civil aviation pilots. Methods: This qualitative study recruited pilots from nine civil aviation companies. Focus group interviews via an online conference platform were conducted in August 2022. Colaizzi analysis was used to derive themes from the data and explore pilots' experiences, challenges, and support needs. Results: The main sub-themes capturing pilots' psychological and physical health challenges were: (1) imbalance between family life and work; (2) pressure from assessment and physical examination eligibility requirements; (3) pressure from worries about being infected with COVID-19; (4) nutrition deficiency during working hours; (5) changes in eating habits because of the COVID-19 pandemic; (6) sleep deprivation; (7) occupational diseases; (8) lack of support from the company in coping with stress; (9) pilots' yearly examination standards; (10) support with sports equipment; (11) respecting planned rest time; and (12) isolation periods. Discussion: The interviewed pilots experienced major psychological pressure from various sources, and their physical health condition was concerning. We offer several suggestions that could be addressed to improve pilots' physical and mental health. However, more research is needed to compare standard health measures for pilots around the world in order to improve their physical and mental health and contribute to overall aviation safety.