ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

The Guidelines for Occupational Hazard Assessment and Control of Active Pharmaceutical Ingredient in the Pharmaceutical Industry (T/WSJD 60—2024) is the first guiding standard in the field of health in China that focuses on occupational health protection for active pharmaceutical ingredient (API). It covers the general principles, work procedures, assessment methods, and control strategies for API occupational hazard assessment, providing practical guidance and recommendations for pharmaceutical enterprises to eliminate or reduce occupational health risks associated with API, improve working environment, and enhance refined management practices. This article interpreted and analyzed the background of standard establishment, formulation process, fundamental basis, and main content, to provide scientific and comprehensive technical support for occupational health managers in the pharmaceutical industry to better apply this standard.

[This corrects the article DOI: 10.1016/j.apsb.2022.03.002.].

Background: This study aimed to investigate changes in gene expression related to matrix synthesis in individuals with fullthickness rotator cuff tears (RCTs) and normal tendon tissues. The study also aimed to examine the differences in gene expression according to 4 distinct tear sizes. Methods: A total of 12 patients with full-thickness RCTs were included in the study, all of whom underwent arthroscopic rotator cuff repair. The RCTs were stratified by size into small, medium, large, and massive. Tendon samples were harvested from the midpoint between the lateral end of the torn tendon and the musculotendinous junction. Subsequent analysis of the tissue samples revealed the mRNA expression levels of 11 collagen types, 6 proteoglycans, and 8 glycoproteins through real-time polymerase chain reaction techniques. For control purposes, supraspinatus tendon tissue was sourced from 3 patients who had proximal humerus fractures but did not present with RCTs. Results: Among the 11 collagens and 14 non-collagenous protein (NCP) genes examined in this study, COL3A1 and COL10A1 showed a significant increase, whereas COL4A1 and COL14A1 showed a tendency to decrease compared to those in the normal group. ACAN significantly increased by 8.92-fold (p < 0.001) compared to that in the normal group, whereas DCN and LUM showed a tendency to decrease. FN1 and TNC increased significantly by 3.47-fold (p = 0.003) and 5.38-fold (p = 0.005), respectively, and the genes ELN, LAMA2, and THBS1 were all significantly reduced compared to those in the normal group. In the NCPs, almost all the genes with increased expression levels had the highest level in small size RCTs, and gene expression decreased as the size increased. The 3 proteoglycans (ACAN, BGN, and FMOD) showed the highest levels of expression in small size RCTs compared to those in the normal group, and 5 glycoproteins (COMP, FBN1, FN1, HAPLN1, and TNC) also showed the highest expression in small size RCTs. Conclusions We confirmed that most of the detected extracellular matrix gene expression changes were related to the size of the full-thickness RCTs. In NCPs, gene expression was increased in small-size tears, and gene expression levels were significantly reduced when the size increased.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

OBJECTIVE: To explore the relationship between the effect of acupuncture and layered localization of computed tomography (CT) in treatment of lumbar disc herniation. METHODS: Based on the CT layered localization, the herniated lumbar discs were positioned in 5 layers, A, B, C, D and E among 300 patients with lumbar disc herniation. Combined with the horizontal and the frontal planes, the three-dimensional location was formed. Acupuncture was delivered at acupoints including bilateral Shenshu (BL23), Dachangshu (BL25), and Huantiao (GB30), Weizhong (BL40) on the affected side. One intervention of acupuncture was 30 min, once daily; 1 course of treatment was composed of 10 interventions and 2 courses were required. Before and after treatment, Japanese orthopaedic association (JOA) score was recorded, and the effect was evaluated. The curative effect was classified and compared with the CT layered localization. RESULTS: Of 300 patients, 226 cases were effective and the effective rate was 75.33%. The JOA scores of all patients, and in the effective group and the non-effective group were higher compared with the scores before treatment (P<0.05). With the layered localization considered, acupuncture was more effective on the cases positioned in C layer. Regarding the horizontal plane, the effect was better on the cases with zone 1 and zone 1-2 involved. In terms of the grade of frontal plane, acupuncture was more effective on the cases graded Ⅰ and Ⅱ. CONCLUSION The clinical effect of acupuncture on lumbar disc herniation is related with the layer and the horizontal zone of herniated disc positioned, as well as to the grade of the frontal plane.
Humans ; Acupuncture Therapy ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Middle Aged ; Adult ; Tomography, X-Ray Computed ; Lumbar Vertebrae/diagnostic imaging* ; Acupuncture Points ; Aged ; Young Adult ; Treatment Outcome

Objective To compare the benefits and drawbacks of primary patch expansion versus pericardial tube right ventricular-pulmonary artery connection in patients diagnosed with pulmonary atresia with ventricular septal defect (PA/VSD). Methods A retrospective study was conducted on patients diagnosed with PA/VSD who underwent primary right ventricular-pulmonary artery connection surgery at our center between 2010 and 2020. Patients were categorized into two groups based on the type of right ventricular-pulmonary artery connection: a pericardial tube group and a patch expansion group. Clinical data and imaging findings were compared between the two groups. Results A total of 51 patients were included in the study, comprising 31 males and 20 females, with a median age of 12.57 (4.57, 49.67) months. The pericardial tube group included 19 patients with a median age of 17.17 (7.33, 49.67) months, while the patch expansion group consisted of 32 patients with a median age of 8.58 (3.57, 52.72) months. In both groups, the diameter of pulmonary artery, McGoon index, and Nakata index significantly increased after treatment (P<0.001). However, the pericardial tube group exhibited a longer extracorporeal circulation time (P<0.001). The reoperation rate was notably high, with 74.51% of patients requiring further surgical intervention, including 26 (81.25%) patients in the patch expansion group and 12 (63.16%) patients in the pericardial tube group. No statistical differences were observed in long-term cure rates or mortality between the two groups (P＞0.005). Conclusion In patients with PA/VSD, both patch expansion and pericardial tube right ventricular-pulmonary artery connection serve as effective initial palliative treatment strategies that promote pulmonary vessel development and provide a favorable foundation for subsequent radical operations. However, compared to the pericardial tube approach, the patch expansion technique is simpler to perform and preserves some intrinsic potential for pulmonary artery development, making it the preferred procedure.

Objective To explore the brain damage of SD rats under different time points of hypobaric hypoxia exposure.Methods A rat high-altitube cerebral edema(HACE)model was constructed by simulating an altitude of 6 000 m in a hypobaric hypoxia animal experimental chamber.Thirty-six SD male rats were randomly divided into the control group and the hypobaric hypoxia exposure 3,7 and 14 d groups,with 9 rats in each group.Except for the control group,the rats in each group were continuously exposed to hypobaric hypoxia for 3,7,and 14 d.At the end of the modeling period,serum was collected by blood sampling via the abdominal aorta,and brain tissue samples were taken.The wet-to-dry ratio(W/D)of brain tissue was calculated,and the levels of relevant oxidative enzymes in serum and brain tissue were measured.The expression levels of hypoxia-inducible factor-1α(HIF-1α)and aquaporin 4(AQP4)mRNAs in brain tissue were detected by real-time fluorescence quantitative polymerase chain reaction.Results The W/D of brain tissues in the control group and the group exposed to hypobaric hypoxia for 3,7 and 14 d were 4.46±0.12,4.98±0.16,5.07±0.18 and 4.95±0.07;the superoxide dismutase contents were(111.86±2.45),(90.73±1.48),(79.64±2.56)and(55.33±1.45)U·g-1;the glutathione contents were(126.91±5.18),(125.26±1.53),(56.20±2.17)and(122.73±1.78)μg·mL-1;the malondialdehyde contents were(230.94±2.00),(362.65±3.28),(407.34±3.47)and(237.50±1.59)nmol·g-1;the relative expression levels of HIF-1 α mRNA were 1.00±0,2.99±0.49,4.72±0.49 and 1.91±0.28;the relative expression levels of AQP4 mRNA were 1.00±0,2.62±0.34,8.38±0.84 and 5.27±0.42,respectively.Statistically significant differences were found between the above indexes in the 3,7 and 14 d of hypobaric hypoxia exposure group compared with the control group(P＜0.05,P＜0.01).Conclusion Different time of hypobaric hypoxia exposure can up-regulate the expression of AQPs proteins in HACE rats and cause the disruption of the blood-brain barrier,and the HACE model constructed in the hypobaric hypoxia chamber with 6 000 m intervention for 7 d was more stable.