The role of the TREM receptor family in cardiovascular diseases: Functions, mechanisms, and therapeutic target.

The Triggering Receptor Expressed in the Myeloid Cells (TREM) family represents an emerging subgroup within the immunoglobulin superfamily, which includes key members such as TREM-1, TREM-2, TREM-3, TREM-like transcript-1 (TLT-1), TLT-2, and TLT-4. TREM-1 serves as a potent amplifier of immune responses, exacerbating atherosclerosis and myocardial injury by enhancing inflammatory reactions. In contrast, TREM-2 exerts protective effects by regulating lipid metabolism, mitigating inflammation, and promoting phagocytic activity, thereby attenuating cardiovascular damage.

Effectiveness of a Four-Stage Death Education Model Based on Constructivist Learning Theory for Trainee Nursing Students.

Objective: To explore the effectiveness of the four-step death education model based on constructivist learning theory in enhancing the death coping abilities and attitudes of trainee nurses and improving course satisfaction.

Methods: Sixty-six trainee nurses from a tumour hospital in Shanxi Province were selected as the study subjects, and a class-experimental research design was adopted, in which the nursing students before the course implementation were taken as the control group and those after the course implementation were taken as the experimental group. Death Attitude Profile Revised Scale (DAP) was used to measure the cognitive attitude of nursing students towards death, and a self-made questionnaire was used to test the satisfaction of nursing students with the curriculum design.

Method for reconstructing safety and arming motion process by integrating Kalman filter and KCF.

This paper addresses the challenge of reconstructing the motion process of the safety and arming (S&A) mechanism in fuze by transforming the problem into a target detection and tracking problem. A novel tracking method, which fuses an improved Kalman filter with a temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction of grayscale images and directional gradient histogram (HOG) features of the target, followed by the use of an Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal and multi-scale features of the target; (2) Application of bilinear interpolation and hybrid filtering techniques to generate a spatial and temporal scale-adaptive bounding box for the filtered target, enabling dynamic adjustment of the tracking box size; (3) Integration of an occlusion-aware mechanism using average peak correlation energy (APCE) to trigger Kalman-based position prediction when the target is occluded, thus mitigating tracking drift.

Investigating ferroptosis-related genes NFE2L2 in neutrophils for ankylosing spondylitis: therapeutic potential of cassia twigs.

We determined the relationship between ferroptosis and immune cells in ankylosing spondylitis and the role of the Chinese herbal medicine Cassia twigs in treating ankylosing spondylitis. We analyzed clinical data on ankylosing spondylitis, transcriptome data, single-cell sequencing data, and genes related to ferroptosis and Cassia twigs. Clinical variables related to AS were selected through logistic regression analysis of the clinical data combined with machine learning.

Fetal Birth Weight Prediction in the Third Trimester: Retrospective Cohort Study and Development of an Ensemble Model.

Background: Accurate third-trimester birth weight prediction is vital for reducing adverse outcomes, and machine learning (ML) offers superior precision over traditional ultrasound methods.

Objective: This study aims to develop an ML model on the basis of clinical big data for accurate prediction of birth weight in the third trimester of pregnancy, which can help reduce adverse maternal and fetal outcomes.

Methods: From January 1, 2018 to December 31, 2019, a retrospective cohort study involving 16,655 singleton live births without congenital anomalies (>28 weeks of gestation) was conducted in a tertiary first-class hospital in Shanghai.