Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study.

BACKGROUND: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear. METHODS: We retrospectively analysed data from 1918 patients who underwent PCI. Patients were categorized into four groups based on body mass index (BMI, normal weight: BMI < 25 kg/m2; overweight and obese: BMI ≥ 25 kg/m2) and DM status (presence or absence). The primary endpoint was the occurrence of major adverse cardiac and cerebrovascular events (MACCE; defined as all-cause death, myocardial infarction, stroke, and unplanned repeat revascularization). RESULTS: During a median follow-up of 7.0 years, no significant differences in MACCE, myocardial infarction, or stroke were observed among the four groups. Overweight and obese individuals exhibited lower all-cause mortality rates compared with normal-weight patients (without DM: hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.37 to 0.78; with DM: HR: 0.57, 95% CI: 0.38 to 0.86). In non-diabetic patients, the overweight and obese group demonstrated a higher risk of unplanned repeat revascularization than the normal-weight group (HR:1.23, 95% CI:1.03 to 1.46). After multivariable adjustment, overweight and obesity were not significantly associated with MACCE, all-cause death, myocardial infarction, stroke, or unplanned repeat revascularization in patients with and without diabetes undergoing PCI. CONCLUSION: Overweight and obesity did not demonstrate a significant protective effect on long-term outcomes in patients with and without diabetes undergoing PCI.

The Associations of Two Novel Inflammation Biomarkers, SIRI and SII, with Mortality Risk in Patients with Chronic Heart Failure.

Background: The associations of two novel inflammation biomarkers, systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), with mortality risk in patients with chronic heart failure (CHF) are not well-characterized. Methods: This retrospective cohort study included patients with CHF in two medical centers of Chinese People's Liberation Army General Hospital, Beijing, China. The outcomes of this study included in-hospital mortality and long-term mortality. Associations of SIRI and SII with mortality were assessed using multivariable regressions and receiver operating characteristic (ROC) analyses. Results: A total of 6232 patients with CHF were included in the present study. We documented 97 cases of in-hospital mortality and 1738 cases of long-term mortality during an average 5.01-year follow-up. Compared with patients in the lowest quartile of SIRI, those in the highest quartile exhibited 134% higher risk of in-hospital mortality (adjusted odds ratio, 2.34; 95% confidence interval [CI], 1.16-4.72) and 45% higher risk of long-term mortality (adjusted hazard ratio, 1.45; 95% CI, 1.25-1.67). Compared with patients in the lowest quartile of SII, those in the highest quartile exhibited 27% higher risk of long-term mortality (adjusted hazard ratio, 1.27; 95% CI, 1.11-1.46). In ROC analyses, SIRI showed better prognostic discrimination than C-reactive protein (area under the curve: 69.39 vs 60.91, P = 0.01, for in-hospital mortality; 61.82 vs 58.67, P = 0.03, for 3-year mortality), whereas SII showed similar prognostic value with C-reactive protein. Conclusion: SIRI and SII were significantly associated with mortality risk in patients with CHF. SIRI may provide better prognostic discrimination than C-reactive protein.

Joint association of hyperuricemia and chronic kidney disease with mortality in patients with chronic heart failure.

Background: The joint association of hyperuricemia and chronic kidney disease (CKD) with mortality in patients with chronic heart failure (CHF) is not conclusive. Methods: This retrospective cohort study was conducted in Chinese People's Liberation Army General Hospital, Beijing, China. We included 9,367 patients with CHF, who were hospitalized between January 2011 and June 2019. The definitions of hyperuricemia and CKD were based on laboratory test, medication use, and medical record. We categorized patients with CHF into 4 groups according to the absence (-) or presence (+) of hyperuricemia and CKD. The primary outcomes included in-hospital mortality and long-term mortality. We used multivariate logistic regression and Cox proportional hazards regression to estimate the mortality risk according to the hyperuricemia/CKD groups. Results: We identified 275 cases of in-hospital mortality and 2,883 cases of long-term mortality in a mean follow-up of 4.81 years. After adjusting for potential confounders, we found that compared with the hyperuricemia-/CKD- group, the risks of in-hospital mortality were higher in the hyperuricemia+/CKD- group (odds ratio [OR], 95% confidence interval [CI]: 1.58 [1.01-2.46]), hyperuricemia-/CKD+ group (OR, 95% CI: 1.67 [1.10-2.55]), and hyperuricemia+/CKD+ group (OR, 95% CI: 2.12 [1.46-3.08]). Similar results were also found in long-term mortality analysis. Compared with the hyperuricemia-/CKD- group, the adjusted hazard ratios and 95% CI for long-term mortality were 1.25 (1.11-1.41) for hyperuricemia+/CKD- group, 1.37 (1.22-1.53) for hyperuricemia-/CKD+ group, and 1.59 (1.43-1.76) for hyperuricemia+/CKD+ group. The results remained robust in the sensitivity analysis. Conclusions: Hyperuricemia and CKD, both individually and cumulatively, are associated with increased mortality risk in patients with CHF. These results highlighted the importance of the combined control of hyperuricemia and CKD in the management of heart failure.

Efficacy of disease management program used among patients with chronic heart failure: protocol for a systematic review and network meta-analysis.

BACKGROUND: A large number of studies have provided a variety of heart failure management program (HF-MP) intervention modes. It is generally believed that HF-MP is effective, but the question of which type of program works best, what level of support is needed for an intervention to be effective, and whether different subgroups of patients are best served by different types of programs is still confusing. METHODS: This study will search for published and unpublished randomized clinical trials in English examining HF-MP interventions in comparison with usual care. MEDLINE, Medlin In-Process and Non-Indexed, CENTRAL, CINAHL, EMBASE, and PsycINFO will be the databases. We will calibrate our eligibility criteria among the team. Each literature will be screened by at least two reviewers. Conflicts will be resolved through team discussion. A similar process will be used for data abstraction and quality appraisal. The results will be synthesized descriptively, and a network meta-analysis will be conducted if the studies are deemed methodologically, clinically, and statistically acceptable (e.g., I2 < 50%). Moreover, potential moderators of efficacy will be analyzed using a meta-regression. DISCUSSION: This study will reduce the clinical heterogeneity and statistical heterogeneity of review and meta-analysis through a more scientific classification method to determine the most effective HF-MP in different subgroups of heart failure patients with different human resource investments and different intervention methods, providing high-quality evidence and guidance for clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021258521.

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.

Background: Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers. Objective: We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers. Methods: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis. Results: A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model. Conclusion: As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144 900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43 760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform. AVAILABILITY AND IMPLEMENTATION: All the results are publicly available at the website (http://covidkg.ai/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases.

OBJECTIVES: This study sought to develop a deep learning (DL) framework to automatically analyze echocardiographic videos for the presence of valvular heart diseases (VHDs). BACKGROUND: Although advances in DL have been applied to the interpretation of echocardiograms, such techniques have not been reported for interpretation of color Doppler videos for diagnosing VHDs. METHODS: The authors developed a 3-stage DL framework for automatic screening of echocardiographic videos for mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR) that classifies echocardiographic views, detects the presence of VHDs, and, when present, quantifies key metrics related to VHD severities. The algorithm was trained (n = 1,335), validated (n = 311), and tested (n = 434) using retrospectively selected studies from 5 hospitals. A prospectively collected set of 1,374 consecutive echocardiograms served as a real-world test data set. RESULTS: Disease classification accuracy was high, with areas under the curve of 0.99 (95% CI: 0.97-0.99) for MS; 0.88 (95% CI: 0.86-0.90) for MR; 0.97 (95% CI: 0.95-0.99) for AS; and 0.90 (95% CI: 0.88-0.92) for AR in the prospective test data set. The limits of agreement (LOA) between the DL algorithm and physician estimates of metrics of valve lesion severities compared to the LOAs between 2 experienced physicians spanned from -0.60 to 0.77 cm2 vs -0.48 to 0.44 cm2 for MV area; from -0.27 to 0.25 vs -0.23 to 0.08 for MR jet area/left atrial area; from -0.86 to 0.52 m/s vs -0.48 to 0.54 m/s for peak aortic valve blood flow velocity (Vmax); from -10.6 to 9.5 mm Hg vs -10.2 to 4.9 mm Hg for average peak aortic valve gradient; and from -0.39 to 0.32 vs -0.31 to 0.32 for AR jet width/left ventricular outflow tract diameter. CONCLUSIONS: The proposed deep learning algorithm has the potential to automate and increase efficiency of the clinical workflow for screening echocardiographic images for the presence of VHDs and for quantifying metrics of disease severity.

AIBN-initiated direct thiocyanation of benzylic sp3 C-H with N-thiocyanatosaccharin.

Direct thiocyanations of benzylic compounds have been implemented. Here, a new strategy, involving a free radical reaction pathway initiated by AIBN, was used to construct the benzylic sp3 C-SCN bond. In this way, the disadvantage of other strategies involving introducing leaving groups in advance to synthesize benzyl thiocyanate compounds was overcome. The currently developed protocol also involved the use of readily available raw materials and resulted in high product yields (up to 100%), both being great advantages for synthesizing benzyl thiocyanates.

Kaempferol ameliorates H9N2 swine influenza virus-induced acute lung injury by inactivation of TLR4/MyD88-mediated NF-κB and MAPK signaling pathways.

Kaempferol, a very common type of dietary flavonoids, has been found to exert antioxidative and anti-inflammatory properties. The purpose of our investigation was designed to reveal the effect of kaempferol on H9N2 influenza virus-induced inflammation in vivo and in vitro. In vivo, BALB/C mice were infected intranasally with H9N2 influenza virus with or without kaempferol treatment to induce acute lung injury (ALI) model. In vitro, MH-S cells were infected with H9N2 influenza virus with or without kaempferol treatment. In vivo, kaempferol treatment attenuated pulmonary edema, the W/D mass ratio, pulmonary capillary permeability, myeloperoxidase (MPO) activity, and the numbers of inflammatory cells. Kaempferol reduced ROS and Malondialdehyde (MDA) production, and increased the superoxide dismutase (SOD) activity. Kaempferol also reduced overproduction of TNF-α, IL-1ß and IL-6. In addition, kaempferol decreased the H9N2 viral titre. In vitro, ROS, MDA, TNF-α, IL-1ß and IL-6 was also reduced by kaempferol. Moreover, our data showed that kaempferol significantly inhibited the upregulation of toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (MyD88), phosphorylation level of IκBα and nuclear factor-κB (NF-κB) p65, NF-κB p65 DNA binding activity, and phosphorylation level of MAPKs, both in vivo and in vitro. These results suggest that kaempferol exhibits a protective effect on H9N2 virus-induced inflammation via suppression of TLR4/MyD88-mediated NF-κB and MAPKs pathways, and kaempferol may be considered as an effective drug for the potential treatment of influenza virus-induced ALI.