Pudong Medical Center

Exosomes serve as intercellular communication vectors and are involved in a broad range of physiological functions. Although exosome-based therapies have demonstrated diverse functional potential, the regulatory mechanisms underlying their biogenesis and secretion remain poorly understood. Here, we report that TEAD1 functions as a molecular switch, dramatically enhancing the synthesis and secretion of exosomes. Mechanistically, TEAD1 enhances exosome secretion by promoting the expression of exosome secretion-associated proteins RAB11, CD9, and SNAP23. We found that TEAD1 enhances exosome secretion from adipose-derived mesenchymal stem cells, thereby promoting skin wound healing in diabetic mice. Similarly, TEAD1 promotes the release of exosomes from bone marrow-derived mesenchymal stem cells, thereby facilitating spinal cord injury (SCI) repair. Our study elucidates a novel role for TEAD1 in driving exosome secretion in different cell types, highlighting the therapeutic potential of TEAD1 in enhancing tissue regeneration, particularly in diabetic wound healing and SCI repair.

The rapid advancement of spatial multi-omics technologies has unveiled opportunities for deciphering the intricate spatial heterogeneity; however, current computational approaches struggle to comprehensively integrate diverse molecular and spatial information. Here we propose 3d-OT, a deep geometry-aware framework that leverages spatial geometric and multi-omics information for feature extraction, spatial domains identification and heterogeneous slices alignment. 3d-OT utilizes modality fusion representation to align spatial slices, bridging the gap in spatial multi-omics alignment methods. Meanwhile, we handle nonrigid deformations in heterogeneous slice alignment through soft correspondence optimal transport, and the chamfer distance is introduced to quantify its performance. 3d-OT outperforms existing methods in capturing anatomical details of mouse brain cortex layers and tracking nonrigid deformations of heart and neural crest tissues at different resolutions. Finally, we construct the 3D spatiotemporal trajectory of mouse embryonic development. Overall, 3d-OT enables comprehensive understanding of existing spatial multi-omics data, offering a powerful computational tool to decipher the spatial complexity of biological tissues.

BackgroundTo explore the effectiveness of a flipped classroom (FC) combined with case-based learning (CBL) in pediatric clinical training.Methods115 undergraduate medical students undergoing pediatric clinical rotation were randomly assigned to either an intervention group receiving FC combined with CBL (FC + CBL) or a control group receiving traditional lecture-based teaching. Learning outcomes were objectively assessed using a theoretical knowledge test, Objective Structured Clinical Examination (OSCE), and routine performance evaluation. Subjective evaluation was conducted using the Self-Assessment Scale for Active Learning and Critical Thinking (SSACT) and a course satisfaction questionnaire. Correlation analysis was performed to explore underlying learning mechanisms.ResultsBaseline characteristics were comparable between the two groups. In terms of objective assessment, the intervention group scored significantly higher than the control group in total score, theoretical knowledge, and OSCE (p < 0.05), while no significant difference was observed in routine performance. Regarding subjective assessment, the intervention group reported significantly higher scores than the control group in active learning, critical thinking, learning efficiency, teamwork and communication, problem-solving ability, practical skills training, learning motivation, and overall teaching effectiveness (p < 0.01). Correlation analysis revealed stronger positive associations between active learning, critical thinking, learning motivation, and objective performance outcomes in the intervention group compared to the control group.ConclusionThe FC + CBL approach significantly improves both objective learning outcomes and subjective learning experiences in pediatric clinical training. The enhancement of active learning and critical thinking abilities may play a key mediating role in this process.

Artificial intelligence (AI) is transforming the diagnostic landscape of malignant tumors in the urinary system, including prostate cancer, bladder cancer, and renal cell carcinoma (RCC). By integrating imaging, pathology, and molecular data, AI enhances the precision and reproducibility of tumor detection, grading, and risk stratification. In prostate cancer, AI-assisted multiparametric Magnetic resonance imaging (MRI) and digital pathology systems improve lesion localization and Gleason scoring. For bladder cancer, deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging (CT/MRI) enable real-time lesion segmentation and non-invasive biomarker prediction, such as Programmed Cell Death-Ligand 1 (PD-L1) expression. In RCC, AI, combined with CT/MRI and multi-omics data, aids in subtype classification and prognostic prediction, supporting personalized therapy. However, despite these promising advances, challenges such as data standardization, model generalizability, interpretability, and regulatory compliance hinder AI's clinical translation. This review outlines the current state of AI in urological cancer diagnosis and prognosis, its technological innovations, and the clinical challenges and opportunities that lie ahead.