The prognostic value of prognostic nutritional index in postoperative onset of PAH in children with isolated VSD: a prospective cohort study based on propensity score matching analysis.

Background: The mechanism of pulmonary arterial hypertension (PAH) after surgery/intervention for isolated venticlular septal defect (VSD) in children is unknown. Reliable prognostic indicators for predicting postoperative PAH are urgently needed. Prognostic nutration index (PNI) is widely used to predict postoperative complications and survival in adults, but it is unclear whether it can be used as an indicator of prognosis in children. Methods: A total of 251 children underwent VSD repair surgery or interventional closure in Hunan Children's Hospital from 2020 to 2023 were collected. A 1:1 propensity score matching (PSM) analysis was performed using the nearest neighbor method with a caliper size of 0.2 Logistics regression analysis is used to examine factors associated with the development of PAH. Results: The cut-off value for PNI was determined as 58.0. After 1:1 PSM analysis, 49 patients in the low PNI group were matched with high PNI group. Children in the low PNI group had higher risk of postoperative PAH (P = 0.002) than those in the high PNI group. Multivariate logistics regression analysis showed that PNI (RR: 0.903, 95% CI: 0.816-0.999, P = 0.049) and tricuspid regurgitation velocity (RR: 4.743, 95% CI: 1.131-19.897, P = 0.033) were independent prognostic factors for the development of PAH. Conclusion: PNI can be used as a prognostic indicator for PAH development after surgery/intervention in children with isolated VSD.

Triglyceride to high-density lipoprotein cholesterol ratio associated with long-term adverse clinical outcomes in patients deferred revascularization following fractional flow reserve.

BACKGROUND: Guidelines on coronary intermediate lesions strongly recommend deferred revascularization after detecting a normal fractional flow reserve (FFR). Researches about triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) on cardiovascular diseases has also been well conducted. However, the association of TG/HDL-C and long-term adverse clinical outcomes remains unknown for patients deferred revascularization following FFR. METHODS: This study retrospectively included 374 coronary artery disease (CAD) patients with non-significant coronary lesions diagnosed by coronary angiography (CAG) and FFR. The main outcome measure was the combination of major adverse cardiovascular and cerebrovascular events (MACCEs). All patients were categorized into three subgroups in terms of TG/HDL-C tertiles (T1 < 0.96, 0.96 ≤ T2 < 1.58, T3 ≥ 1.58). Three different Cox regression models were utilized to reveal the association between TG/HDL-C and prevalence of MACCEs. RESULTS: 47 MACCEs were recorded throughout a median monitoring period of 6.6 years. The Kaplan-Meier survival curves showed a higher MACCEs rate occurred in the higher TG/HDL-C group (5.6% vs. 12.9% vs. 19.4%, log-rank P < 0.01). After adjustment, patients in T3 suffered a 2.6-fold risk compared to the T1 group (T3 vs. T1: HR 2.55, 95% CI 1.05-6.21, P = 0.038; T2 vs. T1: HR 1.71, 95% CI 0.65-4.49, P = 0.075; P for trend = 0.001). The restricted cubic spline (RCS) analysis demonstrated that the HR for MACCEs rose as TG/HDL-C increased. Both the receiver operating characteristic (ROC) and time-dependent ROC proved the excellent predictive ability of TG/HDL-C. CONCLUSION: The study illustrates that TG/HDL-C correlates with the risk of MACCEs in CAD patients deferred revascularization following FFR. TG/HDL-C could serve as a dependable predictor of cardiovascular events over the long term in this population.

Advancing Guideline-Directed Medical Therapy in Heart Failure: Overcoming Challenges and Maximizing Benefits.

The delayed titration of guideline-directed drug therapy (GDMT) is a complex event influenced by multiple factors that often result in poor prognosis for patients with heart failure (HF). Individualized adjustments in GDMT titration may be necessary based on patient characteristics, and every clinician is responsible for promptly initiating GDMT and titrating it appropriately within the patient's tolerance range. This review examines the current challenges in GDMT implementation and scrutinizes titration considerations within distinct subsets of HF patients, with the overarching goal of enhancing the adoption and effectiveness of GDMT. The authors also underscore the significance of establishing a novel management strategy that integrates cardiologists, nurse practitioners, pharmacists, and patients as a unified team that can contribute to the improved promotion and implementation of GDMT.

A high level of uric acid is associated with long-term adverse cardiovascular outcomes in patients who received fractional flow reserve with coronary intermediate stenosis.

BACKGROUND AND AIMS: The role of fractional flow reserve (FFR) in coronary intermediate lesions is widely recommended by guidelines. The effect of uric acid (UA) on cardiovascular events is also well known. However, the relationship between UA and long-term cardiovascular outcomes in patients who received FFR with intermediate lesions remains unknown. METHODS AND RESULTS: We retrospectively included 428 patients who underwent both coronary angiography (CAG) and FFR. Participants were stratified into two groups based on the median UA. The primary endpoint was the composite of major adverse cardiovascular and cerebrovascular events (MACCEs), including repeat revascularization, nonfatal stroke, nonfatal myocardial infarction, and all-cause death. A Cox proportional hazards model was utilized to analyze the association between UA and the prevalence of MACCEs. During a median follow-up of 5.8 years, a higher MACCEs rate occurred in the high UA group compared to the low UA group (16.8% vs. 5.1%, p log-rank<0.01). Elevated UA was independently linked to a higher incidence of MACCEs, whether UA was treated as a categorical or continuous variable (hazard ratio [HR] 2.76, 95% confidence interval [CI] 1.27-6.03 or HR 1.01, 95% CI 1.01-1.02). The restricted cubic spline (RCS) analysis illustrated that the HR for MACCEs increased with increasing UA. CONCLUSION: The present study demonstrates that UA is associated with MACCEs risk and suggests that UA is a reliable predictor of long-term cardiovascular events in coronary intermediate stenosis patients.

Overcoming barriers for left atrial appendage thrombus: a systematic review of left atrial appendage closure.

BACKGROUND: Approximately 90% of intracardial thrombi originate from the left atrial appendage in non-valvular atrial fibrillation patients. Even with anticoagulant therapy, left atrial appendage thrombus (LAAT) still occurs in 8% of patients. While left atrial appendage closure (LAAC) could be a promising alternative, the current consensus considers LAAT a contraindication to LAAC. However, the feasibility and safety of LAAC in patients with LAAT have yet to be determined. METHODS: This systematic review synthesizes published data to explore the feasibility and safety of LAAC for patients with LAAT. RESULTS: This study included a total of 136 patients with LAATs who underwent successful LAAC. The Amulet Amplatzer device was the most frequently utilized device (48.5%). Among these patients, 77 (56.6%) had absolute contraindications to anticoagulation therapy. Cerebral protection devices were utilized by 47 patients (34.6%). Transesophageal echocardiography (TEE) is the primary imaging technique used during the procedure. Warfarin and novel oral anticoagulants were the main anticoagulant medications used prior to the procedure, while dual antiplatelet therapy was primarily used post-procedure. During a mean follow-up period of 13.2 ± 11.5 months, there was 1 case of fatality, 1 case of stroke, 3 major bleeding events, 3 instances of device-related thrombus, and 8 cases of peri-device leakage. CONCLUSIONS: This review highlights the preliminary effectiveness and safety of the LAAC procedure in patients with persistent LAAT. Future large-scale RCTs with varied LAAT characteristics and LAAC device types are essential for evidence-based decision-making in clinical practice.

Causal relationship between particulate matter and COVID-19 risk: A mendelian randomization study.

Background: Observational studies have linked exposure to fine (PM2.5) and coarse (PM10) particulate matter air pollution with adverse COVID-19 outcomes, including higher incidence and mortality. However, some studies questioned the effect of air pollution on COVID-19 susceptibility, raising questions about the causal nature of these associations. To address this, a less biased method like Mendelian randomization (MR) is utilized, which employs genetic variants as instrumental variables to infer causal relationships in observational data. Method: We performed two-sample MR analysis using public genome-wide association studies data. Instrumental variables correlated with PM2.5 concentration, PM2.5 absorbance, PM2.5-10 concentration and PM10 concentration were identified. The inverse variance weighted (IVW), robust adjusted profile score (RAPS) and generalized summary data-based Mendelian randomization (GSMR) methods were used for analysis. Results: IVW MR analysis showed PM2.5 concentration [odd ratio (OR) = 3.29, 95% confidence interval (CI) 1.48-7.35, P-value = 0.0036], PM2.5 absorbance (OR = 5.62, 95%CI 1.98-15.94, P-value = 0.0012), and PM10 concentration (OR = 3.74, 95%CI 1.52-9.20, P-value = 0.0041) increased the risk of COVID-19 severity after Bonferroni correction. Further validation confirmed PM2.5 absorbance was associated with heightened COVID-19 severity (OR = 6.05, 95%CI 1.99-18.38, P-value = 0.0015 for RAPS method; OR = 4.91, 95%CI 1.65-14.59, P-value = 0.0042 for GSMR method) and hospitalization (OR = 3.15, 95%CI 1.54-6.47, P-value = 0.0018 for RAPS method). No causal links were observed between particulate matter exposure and COVID-19 susceptibility. Conclusions: Our study established a causal relationship between smaller particle pollution, specifically PM2.5, and increased risk of COVID-19 severity and hospitalization. These findings highlight the importance of improving air quality to mitigate respiratory disease progression.

Amino acid profile alteration in age-related atrial fibrillation.

BACKGROUND: Amino acids (AAs) are one of the primary metabolic substrates for cardiac work. The correlation between AAs and both atrial fibrillation (AF) and aging has been documented. However, the relationship between AAs and age-related AF remains unclear. METHODS: Initially, the plasma AA levels of persistent AF patients and control subjects were assessed, and the correlations between AA levels, age, and other clinical indicators were explored. Subsequently, the age-related AF mouse model was constructed and the untargeted myocardial metabolomics was conducted to detect the level of AAs and related metabolites. Additionally, the gut microbiota composition associated with age-related AF was detected by a 16S rDNA amplicon sequencing analysis on mouse fecal samples. RESULTS: Higher circulation levels of lysine (Student's t-test, P = 0.001), tyrosine (P = 0.002), glutamic acid (P = 0.008), methionine (P = 0.008), and isoleucine (P = 0.014), while a lower level of glycine (P = 0.003) were observed in persistent AF patients. The feature AAs identified by machine learning algorithms were glutamic acid and methionine. The association between AAs and age differs between AF and control subjects. Distinct patterns of AA metabolic profiles were observed in the myocardial metabolites of aged AF mice. Aged AF mice had lower levels of Betaine, L-histidine, L-alanine, L-arginine, L-Pyroglutamic acid, and L-Citrulline compared with adult AF mice. Aged AF mice also presented a different gut microbiota pattern, and its functional prediction analysis showed AA metabolism alteration. CONCLUSION: This study provided a comprehensive network of AA disturbances in age-related AF from multiple dimensions, including plasma, myocardium, and gut microbiota. Disturbances of AAs may serve as AF biomarkers, and restoring their homeostasis may have potential benefits for the management of age-related AF.

Pulsed field ablation: A promising approach for ventricular tachycardia ablation.

Radiofrequency ablation (RFA) has been a central therapeutic strategy for ventricular tachycardia (VT). However, concerns about its long-term effectiveness and complications have arisen. Pulsed field ablation (PFA), characterized by its nonthermal, highly tissue-selective ablation technique, has emerged as a promising alternative. This comprehensive review delves into the potential advantages and opportunities presented by PFA in the realm of VT, drawing insights from both animal experimentation and clinical case studies. PFA shows promise in generating superior lesions within scarred myocardial tissue, and its inherent repetition dependency holds the potential to enhance therapeutic outcomes. Clinical cases underscore the promise of PFA for VT ablation. Despite its promising applications, challenges such as catheter maneuverability and proarrhythmic effects require further investigation. Large-scale, long-term studies are essential to establish the suitability of PFA for VT treatment.

Physical activity, sedentary behavior, and the risk of frailty and falling: A Mendelian randomization study.

BACKGROUND: Due to inconclusive evidence from observational studies regarding the impact of physical activity (PA) and sedentary behavior on frailty and falling risk, we conducted a two-sample Mendelian randomization analysis to investigate the causal associations between PA, sedentary behavior, and frailty and falls. METHODS: We extracted summary data from genome-wide association studies conducted among individuals of European ancestry, encompassing PA (n = 90 667-608 595), sedentary behavior (n = 372 609-526 725), frailty index (n = 175 226), and falling risk (n = 451 179). Single nucleotide polymorphisms associated with accelerometer assessed fraction >425 milligravities, self-reported vigorous activity, moderate to vigorous physical acticity (MVPA), leisure screen time (LST), and sedentary behavior at work were taken as instrumental variables. The causal effects were primarily estimated using inverse variance weighted methods, complemented by several sensitivity and validation analyses. RESULTS: Genetically predicted higher levels of PA were significantly associated with a reduction in the frailty index (accelerometer assessed fraction >425 milligravities: ß = -0.25, 95% CI = -0.36 to -0.14, p = 1.27 × 10-5 ; self-reported vigorous activity: ß = -0.13, 95% CI = -0.20 to -0.05, p = 7.9 × 10-4 ; MVPA: ß = -0.28, 95% CI = -0.40 to -0.16, p = 9.9 × 10-6 ). Besides, LST was significantly associated with higher frailty index (ß = 0.18, 95% CI = 0.14-0.22, p = 5.2 × 10-20 ) and higher odds of falling (OR = 1.13, CI = 1.07-1.19, p = 6.9 × 10-6 ). These findings remained consistent throughout sensitivity and validation analyses. CONCLUSIONS: Our study offers evidence supporting a causal relationship between PA and a reduced risk of frailty. Furthermore, it underscores the association between prolonged LST and an elevated risk of frailty and falls. Therefore, promoting PA and reducing sedentary behavior may be an effective strategy in primary frailty and falls prevention.

Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection.

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

Device therapy for patients with atrial fibrillation and heart failure with preserved ejection fraction.

Device therapy is a nonpharmacological approach that presents a crucial advancement for managing patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF). This review investigated the impact of device-based interventions and emphasized their potential for optimizing treatment for this complex patient demographic. Cardiac resynchronization therapy, augmented by atrioventricular node ablation with His-bundle pacing or left bundle-branch pacing, is effective for enhancing cardiac function and establishing atrioventricular synchrony. Cardiac contractility modulation and vagus nerve stimulation represent novel strategies for increasing myocardial contractility and adjusting the autonomic balance. Left ventricular expanders have demonstrated short-term benefits in HFpEF patients but require more investigation for long-term effectiveness and safety, especially in patients with AF. Research gaps regarding complications arising from left ventricular expander implantation need to be addressed. Device-based therapies for heart valve diseases, such as transcatheter aortic valve replacement and transcatheter edge-to-edge repair, show promise for patients with AF and HFpEF, particularly those with mitral or tricuspid regurgitation. Clinical evaluations show that these device therapies lessen AF occurrence, improve exercise tolerance, and boost left ventricular diastolic function. However, additional studies are required to perfect patient selection criteria and ascertain the long-term effectiveness and safety of these interventions. Our review underscores the significant potential of device therapy for improving the outcomes and quality of life for patients with AF and HFpEF.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

Identification and validation of aging-related genes in atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in the clinic. Aging plays an essential role in the occurrence and development of AF. Herein, we aimed to identify the aging-related genes associated with AF using bioinformatics analysis. Transcriptome profiles of AF were obtained from the GEO database. Differential expression analysis was performed to identify AF-specific aging-related genes. GO and KEGG enrichment analyses were performed. Subsequently, the LASSO, SVM-RFE, and MCC algorithms were applied to screen aging-related genes. The mRNA expression of the screened genes was validated in the left atrial samples of aged rapid atrial pacing-induced AF canine models and their counterparts. The ROC curves of them were drawn to evaluate their diagnostic potential. Moreover, CIBERSORT was used to estimate immune infiltration. A correlation analysis between screened aging-related genes and infiltrating immune cells was performed. A total of 24 aging-related genes were identified, which were found to be mainly involved in the FoxO signaling pathway, PI3K-Akt signaling pathway, longevity regulating pathway, and peroxisome according to functional enrichment analysis. LASSO, SVM-RFE, and MCC algorithms identified three genes (HSPA9, SOD2, TXN). Furthermore, the expression levels of HSPA9 and SOD2 were validated in aged rapid atrial pacing-induced AF canine models. HSPA9 and SOD2 could be potential diagnostic biomarkers for AF, as evidenced by the ROC curves. Immune infiltration and correlation analysis revealed that HSPA9 and SOD2 were related to immune cell infiltrates. Collectively, these findings provide novel insights into the potential aging-related genes associated with AF. HSPA9 and SOD2 may play a significant role in the occurrence and development of AF.

Exploring immune related gene signatures and mechanisms linking non alcoholic fatty liver disease to atrial fibrillation through transcriptome data analysis.

Atrial fibrillation (AF) and related cardiovascular complications pose a heavy burden to patients and society. Mounting evidence suggests a close association between nonalcoholic fatty liver disease (NAFLD) and AF. NAFLD and AF transcriptomic datasets were obtained from GEO database and analyzed using several bioinformatics approaches. We established a NAFLD-AF associated gene diagnostic signature (NAGDS) using protein-protein interaction analysis and machine learning, which was further quantified through RT-qPCR. Potential miRNA targeting NAGDS were predicted. Gene modules highly correlated with NAFLD liver pathology or AF occurrence were identified by WGCNA. Enrichment analysis of the overlapped genes from key module revealed that T-cell activation plays essential roles in NAFLD and AF, which was further confirmed by immune infiltration. Furthermore, an integrated SVM-RFE and LASSO algorithm was used to identify CCL4, CD48, ITGB2, and RNASE6 as NAGDS, all of which were found to be upregulated in NAFLD and AF mouse tissues. Patients with higher NAGDS showed augmented T cell and macrophage immunity, more advanced liver pathological characteristics, and prolonged AF duration. Additionally, hsa-miR-26a-5p played a central role in the regulation of NAGDS. Our findings highlight the central role of T-cell immune response in linking NAFLD to AF, and established an accurate NAGDS diagnostic model, which could serve as potential targets for immunoregulatory therapy.

Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization.

The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner particles and then uses the winner particles to efficiently guide the search of the loser particles. This approach has very promising performance in solving large-scale multiobjective optimization problems (LMOPs). However, most studies of CSOs ignore the evolution of the winner particles, although their quality is very important for the final optimization performance. Aiming to fill this research gap, this article proposes a new neural net-enhanced CSO for solving LMOPs, called NN-CSO, which not only guides the loser particles via the original CSO strategy, but also applies our trained neural network (NN) model to evolve winner particles. First, the swarm particles are classified into winner and loser particles by the pairwise competition. Then, the loser particles and winner particles are, respectively, treated as the input and desired output to train the NN model, which tries to learn promising evolutionary dynamics by driving the loser particles toward the winners. Finally, when model training is complete, the winner particles are evolved by the well-trained NN model, while the loser particles are still guided by the winner particles to maintain the search pattern of CSOs. To evaluate the performance of our designed NN-CSO, several LMOPs with up to ten objectives and 1000 decision variables are adopted, and the experimental results show that our designed NN model can significantly improve the performance of CSOs and shows some advantages over several state-of-the-art large-scale multiobjective evolutionary algorithms as well as over model-based evolutionary algorithms.

A case report of multiple artery pseudoaneurysms associated with SARS-CoV-2.

Arterial pseudoaneurysms are rare vascular abnormalities that can occur as a complication of infections. Artery pseudoaneurysms associated with SARS-CoV-2 are a rare occurrence in COVID-19 patients, and their rupture can result in significant hemorrhage and sudden death. Few cases of SARS-CoV-2-associated artery pseudoaneurysms have been reported, and their underlying pathophysiological mechanisms remain unclear. This study presents the first reported case of a patient who developed both pulmonary and gallbladder artery pseudoaneurysms following SARS-CoV-2 infection. We investigate the potential pathogenesis of these pseudoaneurysms and aim to improve the understanding of this rare complication.

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.

Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.

Comparison of SSS-CN and PHQ-15 in the evaluation of patients with suspected psychological disorders in cardiovascular medicine.

Aims: Somatic Symptom Scale-China (SSS-CN) has been applied to assess the presence and severity of somatization symptom disorders (SSD) in Chinese patients. However, there was no study comparing SSS-CN with Patient Health Questionnaire-15 (PHQ-15). The aim of this study was to compare the consistency of the SSS-CN with the PHQ-15 in evaluating SSD in patients with suspected psychological disorders in cardiovascular medicine and to explore the relationship between scores on the two SSD self-rating scales and scores on self-rating scales for anxiety or depression. Methods: In this study, 1,324 subjects were enrolled by using a "three-question method." Then, they completed four self-assessment scales, i.e., SSS-CN, PHQ-15, Patient Health Questionnaire-9 (PHQ-9), and General Anxiety Disorder-7 (GAD-7), in turn. The ability of SSS-CN to diagnose SSD was analyzed by the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) value, sensitivity, and specificity were calculated. Reliability analysis was performed with the Kappa statistic to determine consistency between SSS-CN and PHQ-15. The relationship between two qualitative variables was analyzed by Spearman correlation analysis. Results: The proportions of SSD evaluated by SSS-CN and PHQ-15 were 83.2 and 87.0%, respectively. SSS-CN score was significantly correlated with PHQ-15 one (r = 0.709, p < 0.001). The AUC of the SSS-CN for the diagnosis of SSD was 0.891, with a high sensitivity and acceptable specificity. There was a moderate agreement between SSS-CN and PHQ-15 in assessing SSD, with a Kappa value of 0.512. Anxiety and/or depression were detected in about 70% of patients with SSD. There was significant correlation between the score of each SSD scale and that of GAD-7 or PHQ-9 (SSS-CN: r = 0.614 or 0.674; PHQ-15: r = 0.444 or 0.582, all p < 0.001). In addition, the SSS-CN score was more closely correlated with the GAD-7 or PHQ-9 score than the PHQ-15 score, and a higher proportion of patients with anxiety or depression was detected in those with moderate and severe SSD evaluated by SSS-CN. Conclusion: The SSS-CN could be one of the ideal scales for the rapid screening of patients with suspected psychological disorders in cardiovascular medicine.

Review of single-cell RNA-seq data clustering for cell-type identification and characterization.

In recent years, the advances in single-cell RNA-seq techniques have enabled us to perform large-scale transcriptomic profiling at single-cell resolution in a high-throughput manner. Unsupervised learning such as data clustering has become the central component to identify and characterize novel cell types and gene expression patterns. In this study, we review the existing single-cell RNA-seq data clustering methods with critical insights into the related advantages and limitations. In addition, we also review the upstream single-cell RNA-seq data processing techniques such as quality control, normalization, and dimension reduction. We conduct performance comparison experiments to evaluate several popular single-cell RNA-seq clustering approaches on simulated and multiple single-cell transcriptomic data sets.

Nicotinamide mononucleotide attenuates HIF-1α activation and fibrosis in hypoxic adipose tissue via NAD+/SIRT1 axis.

Background: Fibrosis is increasingly considered as a major contributor in adipose tissue dysfunction. Hypoxic activation of hypoxia-inducible factor 1α (HIF-1α) induces a profibrotic transcription, leading to adipose fibrosis. Nicotinamide mononucleotide (NMN), a member of the vitamin B3 family, has been shown to relieve hepatic and cardiac fibrosis, but its effects on hypoxic adipose fibrosis and the underlying mechanism remain unclear. We aimed to elucidate the roles of NMN in regulating HIF-1α and fibrosis in hypoxic adipose tissue. Methods: Mice were placed in a hypobaric chamber for four weeks to induce adipose fibrosis. NMN (500 mg/kg, every three days) was administered by intraperitoneal injection. In vitro, Stromal vascular fractions (SVF) cells were treated by hypoxia with or without NMN (200µM), sirtinol (25µM, a SIRT1 inhibitor) and CoCl2 (100µM, a HIF1α enhancer). The effects of NMN on hypoxia-associated adipose fibrosis, inflammation, NAD+/SIRT1 axis alteration, and HIF-1α activation were evaluated by real-time polymerase chain reaction (PCR), western blots, immunohistochemistry staining, immunoprecipitation, and assay kits. Results: Mice placed in a hypoxic chamber for four weeks showed obvious adipose fibrosis and inflammation, which were attenuated by NMN. NMN also restore the compromised NAD+/SIRT1 axis and inhibited the activation of HIF-1α induced by hypoxia. In hypoxia-induced SVFs, the SIRT1 inhibitor sirtinol blocked the anti-fibrotic and anti-inflammatory effects of NMN, upregulated the HIF-1α and its acetylation level. The HIF1α stabilizer CoCl2 showed similar effects as sirtinol. Conclusion: NMN effectively attenuated HIF-1α activation-induced adipose fibrosis and inflammation by restoring the compromised NAD+/SIRT1 axis.