ObjectiveTo introduce visual teaching into the course design of medical humanistic care based on the concept of implementation science， evaluate the teaching implementation effect and feedback， and provide references for optimizing course teaching outcomes and improving students’ humanistic care competence. MethodsA visual teaching program for medical humanistic care was designed， with key steps including clarifying teaching objectives， content， methods， and curriculum assessment. This program was implemented in the medical humanistic care course teaching involving 50 elective students. Multi-dimensional evaluation of teaching effectiveness was conducted through course grades， visual teaching evaluation， and humanistic workshop assessment， combined with inductive content analysis of students’ learning experiences in the workshops. ResultsThe 50 students achieved above-average course grades （89.60±3.41） and demonstrated high satisfaction with the overall course and visual teaching. All the 6 groups obtained relatively high scores in the medical humanistic care workshops. Four themes were extracted， namely， enhancing humanistic care competencies， deepening familial and interpersonal relationships， realizing emotional expression and self-growth， and strengthening integration of humanistic care concepts with practice. ConclusionThe teaching of medical humanistic care course has achieved favorable effects， which contributes to deepening students’ understanding of humanistic care and enhancing their humanistic care competence. Students demonstrate high levels of recognition and satisfaction with the course.

Objective To analyze the main causes and risk factors of unplanned reoperation after liver transplantation. Methods The clinical data of 242 liver transplant recipients in the First Affiliated Hospital of Anhui Medical University from January 2015 to December 2024 were retrospectively analyzed. According to whether unplanned reoperation was performed during the same hospitalization after surgery, the recipients were divided into the reoperation group (n=36) and the non-reoperation group (n=206). The preoperative, intraoperative and postoperative data of the two groups, as well as donor and graft-related data, were compared to analyze the risk factors of unplanned reoperation after liver transplantation and the survival status of the two groups. Results Among the 242 liver transplant recipients, 36 underwent unplanned reoperations, with a total of 54 procedures including various laparotomies, endoscopic and interventional surgeries, among which there were 20 laparotomies, 18 endoscopic surgeries and 16 interventional surgeries. The most common cause of unplanned reoperation was biliary complications (20 times), followed by vascular complications (17 times). Compared with the non-reoperation group, the reoperation group had longer graft cold ischemia time, higher postoperative fatality rate of recipients, longer length of stay in the intensive care unit and postoperative hospital stay, and higher total hospitalization costs (all P<0.05). The incidence of unplanned reoperation was higher in recipients who underwent split liver transplantation (P<0.05). Multivariate analysis showed that intraoperative blood loss ≥1 000 mL, positive culture of graft perfusate and split liver transplantation were independent risk factors for unplanned reoperation (all P<0.05). The postoperative 7-day, 1-month, 3-month and 6-month survival rates of recipients in the reoperation group and the non-reoperation group were 100% vs. 98.1%, 88.9% vs. 94.2%, 69.4% vs. 90.8% and 66.7% vs. 90.8%, respectively, and the postoperative survival rate of recipients in the reoperation group was lower than that in the non-reoperation group (P<0.05). Conclusions The main causes of unplanned reoperation after liver transplantation are biliary complications, vascular complications, abdominal incision infection and intra-abdominal hemorrhage. Intraoperative massive blood loss, positive culture of graft perfusate and split liver transplantation are the risk factors associated with unplanned reoperation after liver transplantation.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

The traditional medical quality management model has many problems,such as excessive reliance on experience,poor coordination among departments,and lagging supervision,innovating medical quality management models has become an essential issue for the high-quality development of hospitals.A tertiary Grade A general hospital built a"data element multiplier"ecological chain for medical quality management,and established a complete closed-loop management system for medical quality management under the"Internet plus"model.Finally,the mobile medical quality management platform was built.Jointly driven by"Internet plus"and"data element multiplier",the level of lean management has been significantly improved,providing practical reference for hospitals at the same level.

Objective:To investigate the relationship between the changes in extracellular vesicles (EVs) size distribution before and after cardiopulmonary bypass (CPB) cardiac surgery and postoperative coagulation function.Methods:A total of 103 patients undergoing cardiac surgery with CPB were enrolled. Venous blood samples were collected at preoperation, postoperative 12 h and 3 days. Additionally, 50 age- and gender-matched healthy volunteers served as a control group. EVs were isolated using gradient centrifugation, and their size distribution was assessed by dynamic light scattering (DLS). The relationship between EV size characteristics, including peak diameter, peak height, and interquartile range( IQR), and postoperative coagulation function was analyzed. Results:Compared to patients with normal postoperative coagulation function, those with postoperative coagulation dysfunction had lower size at peak and IQR, and significantly higher peak intensity. Logistic regression analysis indicated that elevated peak intensity and lower size at peak and IQR were risk factors for coagulation dysfunction. The area under the curve ( AUC) for diagnosing coagulation dysfunction with 12 h postoperative EVs peak intensity was 0.76, with a positive predictive value of 85% at the optimal cutoff of 8.2; the AUC for IQR was 0.84, with a sensitivity of 83%, specificity of 82%, and negative predictive value of 86% at the optimal cutoff of 125.05 nm. Conclusion:The size distribution of circulating EVs show a correlation with coagulation function after cardiac surgery with CPB and may serve as a novel biomarker to predict postoperative coagulation dysfunction.

Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.

ObjectiveTo elucidate the chemical composition of Danshenyin and its blood components in rats after oral administration. MethodsUltra performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry（UPLC-Q-TOF-MS/MS） coupled with PeakView 1.2 software was used to systematically characterize and identify the components of Danshenyin aqueous extract and its migratory components in rat blood after oral administration based on the retention time， quasi-molecular ion peaks， secondary fragmentation ions， and literature reports， and a preliminary compounds identification of Salviae Miltiorrhizae Radix et Rhizoma aqueous extract， the co-decoction of Santali Albi Lignum and Amomi Fructus was carried out to attribute the chemical constituents of the aqueous extract of Danshenyin. ResultsA total of 73 compounds， including 21 phenolic acids， 23 diterpenes， 6 flavonoids， 7 organic acids， 3 volatile oils and 13 others， were identified from the aqueous extract of Danshenyin. And 36 prototypes and 15 metabolites were identified in rat plasma， the major metabolic pathways included reduction， hydration， hydroxylation， demethylation， methylation， sulfation and others， these metabolites were mainly derived from tanshinones and salvianolic acids. ConclusionThe main blood components of the aqueous extract of Danshenyin are salvianolic acids and tanshinones， which may be the material basis of the efficacy. This study can provide reference for pharmacological research， quality control， and clinical application of Danshenyin.

In recent years, with the rapid development of artificial intelligence technology, the application of deep learning in the field of pathology has been continuously expanding. Particularly, the rise of multimodal data fusion methods has opened up new technical paths for the precise diagnosis, prognosis assessment, and individualized treatment of tumors. By integrating multi-level and multi-source data such as clinical information, pathological omics, molecular omics, and imaging omics, deep learning models can identify potential associated features and key biological mechanisms that are difficult to reveal by a single modality, thereby significantly improving the accuracy of disease classification and the scientific nature of risk stratification. This article systematically reviews the research progress of multimodal data fusion methods based on deep learning in the field of pathology in recent years, focuses on sorting out different types of fusion strategies, evaluates their advantages and challenges in practical clinical applications, and looks forward to future development trends.