Basic Science and Pathogenesis.

Background: In Alzheimer's disease (AD), changes in the brain transcriptome are hypothesized to mediate the impact of neuropathology on cognition. Gene expression profiling from postmortem brain tissue is a promising approach to identify causal pathways; however, there are challenges to definitively resolve the upstream pathologic triggers along with the downstream consequences for AD clinical manifestations.

Method: We have functionally dissected 30 AD-associated gene coexpression modules using a cross-species strategy in Drosophila melanogaster models.

Rural patients' satisfaction with humanistic nursing care in Chinese Public Tertiary Hospitals: a national cross-sectional study.

Background: The provision of high-quality healthcare services and patient satisfaction are fundamental objectives in modern healthcare. Humanistic nursing care, which emphasizes empathy, respect for individuality, and cultural sensitivity, aims to build trust and improve the overall experience for patients. This approach is especially relevant for rural patients in China, who often face additional challenges in accessing care in large tertiary hospitals.

Deficiency of Sox7 leads to congenital aortic stenosis via abnormal valve remodeling.

Abnormal valve development is the most common congenital heart malformation. The transcription factor Sox7 plays a critical role in the development of vascular and cardiac septation. However, it remains unclear whether Sox7 is required for heart valve development.

Tandem Versus Single Autologous Stem Cell Transplantation for High-Risk Multiple Myeloma in the Era of Novel Agents: A Real-World Study of China.

Background: This study compares the efficacy and safety of single autologous stem cell transplantation (ASCT) versus tandem ASCT for multiple myeloma (MM) patients in the era of novel agents.

Methods: A total of 112 high-risk MM patients were included (single ASCT, (n = 57) or tandem ASCT(n = 55) in this retrospective multicenter study. Responses and outcomes were evaluated.

TCSRNet: a lightweight tobacco leaf curing stage recognition network model.

Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages.