Acute brain injury (ABI) affects up to one-third of patients using mechanical circulatory support (MCS). In venoarterial extracorporeal membrane oxygenation (VA ECMO), ABI incidence (11%–40%) has not improved in two decades. Conversely, improvements in left ventricular assist devices (LVADs) have reduced the incidence of stroke, although it remains a major complication (10%–30%). The failure of MCS to ensure adequate cerebral protection may impair cerebrovascular autoregulation (CVAR) and disrupt microcirculatory function affected by reduced pulsatility, endothelial injury, acute perturbations in partial pressure of arterial carbon dioxide (PaCO2), and cerebral venous congestion. Here, we review evidence demonstrating that these factors alter microcirculatory dynamics and CVAR, thereby contributing to ABI through shared mechanistic pathways. Current methods for assessing CVAR are reviewed, including invasive indices such as the pressure reactivity index (PRx) from intracranial pressure monitoring and noninvasive metrics such as the cerebral oximetry index (COx) from near-infrared spectroscopy or flow-velocity correlations from transcranial Doppler. Each method is limited by feasibility, signal artifacts, and inter-modality variability. Our review identifies three priority areas for cerebral protection in MCS: preservation of pulse pressure, cautious titration of PaCO2, and integration of CVAR-informed blood pressure management. Preliminary evidence suggests that very low pulse pressure, rapid carbon dioxide correction, and persistent microcirculatory impairment are each associated with ABI risk. Future investigations should focus on validating bedside tools to assess CVAR and microcirculatory integrity, and on determining whether physiological targets derived from these measures can improve neurological outcomes in patients using MCS.

Chronic lung allograft dysfunction (CLAD) is a common complication after lung transplantation, which severely impairs the long-term survival of patients. At present, effective therapeutic approaches remain lacking. Dysregulation of the lung microbiome acts as a pathogenic factor for a variety of pulmonary diseases. Accumulating studies have demonstrated that the compositional characteristics of the lung microbiome after lung transplantation are closely associated with CLAD. Therefore, this article reviews the definition of CLAD, characteristics of the lung microbiome, the influence of the lung microbiome on the occurrence and progression of CLAD, the potential mechanisms underlying the lung microbiome in the pathogenesis of CLAD, and microbiome-based intervention strategies for CLAD. It aims to provide novel insights for the prevention and treatment of CLAD following lung transplantation and further improve the prognosis of lung transplant recipients.

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Objective:To analyze the changes in the costs of hospital rehabilitation after the reform of health insurance payments in the past 6 years, and to identify relevant factors which can provide a reference for the reform of the health insurance payment system in rehabilitation department.Methods:Information on 16, 827 patients hospitalized in the rehabilitation department of The First Affiliated Hospital of Xi′an Jiaotong University between May 2018 and May 2024 was collected and subjected to non-parametric analysis.Results:The average hospitalization cost of rehabilitation department patients over the six years was Y14, 574.92±10, 524.79. During that time the proportion of the cost attributable to Western medicine decreased from 17.1% in 2018 to 7.6% in 2024. The proportion of the patients with hypertension was 51.94%, followed by diabetes mellitus (20.10%). Those with infections had the highest total hospitalization costs. Motor disorders were the most common dysfunction (59.02%), followed by speech disorders (17.45%). Patients with swallowing disorders had the highest hospitalization costs. After the payment system shifted from fee-for-service (FFS) to payment by diagnosis-related group (DRG) in 2023, the average daily inpatient expenditures for rehabilitation patients with all types of diseases gradually declined, reaching its lowest level in 2024.Conclusions:After the health insurance payments shifted from FFS to DRG, the proportion of in patients′ total drug costs decreased annually, and the average daily costs of patients with different types of diseases also decreased significantly, but the comprehensive service fee and diagnostic costs increased.

As a major category of occupational hazards in China, physical factors are widely distributed in various industries and affect a large number of workers. The list and diagnostic criteria of occupational diseases caused by physical factors are important basis for occupational disease diagnosis and protection of occupational health rights and interests for occupational populations. This article compares the differences in the list of occupational diseases caused by physical factors at home and abroad, analyzes the problems in the current list of occupational diseases caused by physical factors and related diagnostic standards in China, and puts forward relevant suggestions for further adjusting the list of occupational diseases caused by physical factors, formulating and revising relevant diagnostic standards for occupational diseases, providing reference for improving the classification and catalogue of occupational diseases in China in the future.