The specific medications for pulmonary arterial hypertension at functional class III to IV: a systematic review and meta-analysis.

Purpose: To systematically evaluate the clinical efficacy and safety of targeted drugs in patients with pulmonary arterial hypertension (PAH) with cardiac function grades III-IV, and conduct a meta-analysis.

Methods: Two researchers independently searched the PubMed, EMBASE, and Cochrane Library databases for relevant studies, with the search period extending from the establishment of the databases to March 2024. Meta-analysis was performed using statistical software Review Manager 5.

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Background: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients.

Safety of different therapies for atrial fibrillation: a network meta-analysis.

Objective: Most published studies have aimed to compare the effectiveness of different treatment strategies for atrial fibrillation (AF), while few articles have comprehensively compared the safety of therapeutic measures.The aim of the article was to assess the safety of different therapeutic measures (different ablation techniques, antiarrhythmic drugs and surgery) in patients with AF.

Method: A comprehensive and systematic search was undertaken across various databases, namely PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying pertinent randomized controlled trials (RCTs) that delve into the safety aspects of diverse atrial fibrillation treatment strategies.

A noval noninvasive targeted therapy for osteosarcoma: the combination of LIFU and ultrasound-magnetic-mediated SPIO/TP53/PLGA nanobubble.

Purpose: Osteosarcoma (OS) is the most common type of primary malignant bone tumor. Transducing a functional TP53 gene can effectively inhibit OS cell activity. Poly lactic acid-glycolic acid (PLGA) nanobubbles (NBs) mediated by focused ultrasound (US) can introduce exogenous genes into target cells in animal models, but this technique relies on the passive free diffusion of agents across the body.

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.

Objective: The relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biological markers are lacking. This study aims to elucidate the interplay between gut microbial communities and macrophages, and to identify biomarkers associated with the destabilization of atherosclerotic plaques. The goal is to enhance our understanding of the underlying molecular pathways and to pave new avenues for diagnostic approaches and therapeutic strategies in the disease.

Gypenoside induces apoptosis by inhibiting the PI3K/AKT/mTOR pathway and enhances T-cell antitumor immunity by inhibiting PD-L1 in gastric cancer.

Gypenoside is a natural extract of (Thunb.) Makino, a plant in the Cucurbitaceae family. It has been reported to have antitumor effects on the proliferation, migration and apoptosis of various types of cancer cells.

Meta-analysis: clinical features and treatments of lung cancer in combined pulmonary fibrosis and emphysema.

Background And Aim: There are many epidemiological pieces of evidence that show combined pulmonary fibrosis and emphysema (CPFE) patients have an increased risk of lung cancer. We conducted a systematic review of all published data to define the characteristics and treatments of lung cancer that develops in CPFE by performing a meta-analysis.

Methods: Databases(including PubMed, Medline, CNKI, VIP, etc.

Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study.

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.

Development of a prediction model for the risk of 30-day unplanned readmission in older patients with heart failure: A multicenter retrospective study.

Background And Aim: Heart failure (HF) imposes significant global health costs due to its high incidence, readmission, and mortality rate. Accurate assessment of readmission risk and precise interventions have become important measures to improve health for patients with HF. Therefore, this study aimed to develop a machine learning (ML) model to predict 30-day unplanned readmissions in older patients with HF.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.

Objective: Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Identification and prediction of immune checkpoint inhibitors-related pneumonitis by machine learning.

Background: Immune checkpoint inhibitor (ICI)-related pneumonitis (IRP) is a common and potentially fatal clinical adverse event. The identification and prediction of the risk of ICI-related IRP is a major clinical issue. The objective of this study was to apply a machine learning method to explore risk factors and establish a prediction model.

miR-19a mediates the mechanism by which SPHK2 regulates hypopharyngeal squamous cell carcinoma progression through the PI3K/AKT axis.

This study explored the expression of sphingosine kinase 2 (SPHK2) and microRNA miR-19a-3p (miR-19a-3p) in patients with Hypopharyngeal squamous cell carcinoma (HSCC) together with pathways affecting HSCC invasion and metastasis. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting (WB) were performed to assess the differential expression of SPHK2 and miR-19a-3p in patients with HSCC lymph node metastasis (LNM). Immunohistochemical (IHC) results were analyzed together with clinical information to evaluate their clinical significance.

Deletion of DYRK1A Accelerates Osteoarthritis Progression Through Suppression of EGFR-ERK Signaling.

Dual-specificity tyrosine phosphorylation regulated kinase 1A (DYRK1A) signaling is involved in the dynamic balance of catabolism and anabolism in articular chondrocytes. This study aimed to investigate the roles and mechanism of DYRK1A in the pathogenesis of osteoarthritis (OA). The expressions of DYRK1A and its downstream signal epidermal growth factor receptor (EGFR) were detected in the cartilage of adult wild-type mice with destabilized medial meniscus (DMM) and articular cartilage of patients with OA.

SCD1 deficiency exacerbates osteoarthritis by inducing ferroptosis in chondrocytes.

Background: Osteoarthritis (OA) is a severe joint disease that causes cartilage destruction and mobility loss. Abnormal fatty acid metabolism of chondrocytes plays a role in OA development. Stearoyl-CoA desaturase (SCD1) is a rate-limiting enzyme in the anabolism of unsaturated fatty acids.

A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study.

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.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study.

Background: Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.