Objective: To investigate the current status of benefit finding among young and middle-aged patients with type 2 diabetes mellitus (T2DM) and analyze its influencing factors, so as to provide a reference for improving the level of benefit finding in this population. Methods: From November 2022 to May 2023, young and middle-aged patients with T2DM aged 18-59 years hospitalized in the endocrinology departments of 2 tertiary hospitals in Hengyang City, Hunan Province were selected as survey subjects by a convenience sampling method. Basic demographic information was collected using a general questionnaire survey. Benefit finding, resourcefulness, and stigma were evaluated using the Benefit Finding Scale, the Chinese Version of the Resourcefulness Scale, and the Type 2 Diabetes Stigma Assessment Scale, respectively. A multiple linear regression model was used to analyze the influencing factors of benefit finding among young and middle-aged patients with T2DM. Results: A total of 305 young and middle-aged patients with T2DM were investigated, including 222 males (72.79%) and 83 females (27.21%). There were 231 cases aged 45-59 years, accounting for 75.74%. The scores for benefit finding, resourcefulness, and stigma were (42.86±6.06), (75.12±11.30), and (41.20±10.10), respectively. Multiple linear regression analysis showed that young and middle-aged patients with T2DM who were male (β′=0.088), aged 18-<45 years (β′=0.083), absence of diabetes complications (β′=0.124), and had higher resourcefulness scores (β′=0.679) had higher levels of benefit finding, while patients with higher stigma scores (β′=-0.097) had lower levels of benefit finding. Conclusion The level of benefit finding among young and middle-aged patients with T2DM was moderate, and was related to gender, age, diabetes complications, resourcefulness, and stigma.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Ulcerative colitis is a chronic relapsing inflammatory bowel disease. Modern research indicates that immune dysregulation resulting from disruptions in circadian rhythm is closely associated with its pathogenesis. Both Western chronomedicine and traditional Chinese medicine（TCM）" treatment based on temporal factors" emphasize the temporal relationship between natural rhythms and human physiology and pathology. The " midnight-noon and ebb-flow " theory represents the concrete application and deepening of TCM " treatment based on temporal factors" within the realm of chronomedicine. This article correlates the onset time of ulcerative colitis with specific periods in the " midnight-noon and ebb-flow"theory：the Mao period（05：00-07：00），when the yangming large intestine meridian of hand is dominant； the Si period（09：00-11：00），when the taiyin spleen meridian of foot is dominant； and the You period（17：00-19：00），when the shaoyin kidney meridian of foot is dominant. According to this perspective，if the disease manifests during the Mao period，the pathogenesis is attributed to dampnessheat accumulation and disorder of qi and blood. Treatment should focus on clearing heat，resolving dampness，and harmonizing qi and blood，using modified formulas such as Shaoyao Decoction or Baitouweng Decoction. If it occurs during the Si period，the pathogenesis involves spleen deficiency with dampness obstruction and disharmony of qi and blood. Treatment should focus on strengthening the spleen，eliminating dampness，and restoring qi and blood，using modified formulas such as Huangya Decoction or Shenling Baizhu Powder. If it presents during the You period，the pathogenesis is characterized by fire failing to warm earth，and consumption resulting in qi and blood leakage. Treatment should focus on warming the kidney and spleen，and securing qi and blood，using modified formulas such as Sishen Pill or Tianhun Decoction. In addition to oral administration of Chinese herbal medicine，comprehensive therapies including acupuncture，herbal enemas，and acupoint application can provide novel insights for the clinical diagnosis and treatment of ulcerative colitis.

To summarize the clinical experience of Professor WANG Jie in treating palpitation using the method of tonifying the kidneys and invigorating blood. It is believed that kidney yang deficiency and heart vessel stasis are the key pathogenesis of palpitation， and it is advocated to treat palpitation by tonifying the kidney and invigorating the blood. Meanwhile，the methods of draining water， dissolving phlegm， and fortifying the spleen to supplement the blood should be applied. For palpitation with kidney yang deficiency and heart vessel stasis with upward water， the treatment could use self-prescribed Bushen Huoxue Lishui Formula （补肾活血利水方）； for palpitation with phlegm obstruction in blood vessel， the treatment could use self-prescribed Bushen Huoxue Huatan Formula （补肾活血化痰方）； for palpitation with spleen yang deficiency， the treatment could use self-prescribed Bushen Huoxue Jianpi Formula （补肾活血健脾方）.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

Objective To explore the effects of home-based exercise rehabilitation on cardiac structure, valvular function, and exercise capacity in patients with severe aortic stenosis (AS) after transcatheter aortic valve replacement (TAVR). Methods 49 patients with severe AS who underwent TAVR at Zhongshan Hospital, Fudan University, from January 2024 to February 2025 were enrolled. They were divided into an exercise group (n＝25) or a non-exercise group (n＝24) based on participating or not in home-based rehabilitation after TAVR. The exercise group received 12 weeks of home-based exercise training (aerobic exercise plus resistance training every week); the non-exercise group received routine care. Transthoracic echocardiography (TTE) was used to assess cardiac structural parameters before discharge (T0) and after 12 weeks of exercise (T1). Functional outcomes including the 6-minute walk test (6MWT), Duke Activity Status Index (DASI), and Short Physical Performance Battery (SPPB) were compared between the two groups. A linear mixed-effects model was used to analyze the effect of home-based rehabilitation on echocardiographic parameters. Patients were stratified by baseline 6MWT (＜240 m as low-function subgroup, ≥240 m as high-function subgroup) to compare exercise-related outcomes between subgroups. Results At T1, the exercise group had a longer 6MWT distance than the non-exercise group (P＝0.012). The linear mixed-effects model showed that after 12 weeks of exercise, the left ventricular end-diastolic diameter (LVEDD) decreased in the exercise group but slightly increased in the non-exercise group, with a significant difference in changes over time between the two groups (P_interaction＝0.030). The exercise group also showed greater improvement in effective orifice area index (P_interaction＝0.028) and effective orifice area (P_interaction＝0.042) than the non-exercise group. Subgroup analysis revealed that in the low-function subgroup, the exercise group showed greater improvement in the 6MWT (P_interaction＝0.035) and the effective orifice area index (P_interaction＝0.046) compared to the non-exercise group; in the high-function subgroup, the exercise group showed greater improvement only in LVEDD compared to the non-exercise group (P_interaction＝0.046). Conclusions Home-based exercise rehabilitation improves exercise capacity, optimizes left ventricular remodeling, and enhances valvular function in patients with severe AS after TAVR, with greater benefits observed in patients with lower baseline 6MWT.

Objective To develop a personalized recommendation strategy for tranexamic acid administration during the perioperative period of orthopedic surgery based on machine learning,aiming to reduce perioperative bleeding and related complications and improving clinical outcomes.Methods A total of 11 727 patients undergoing orthopedic surgery from the INSPIRE database were subjected in this study.Missing data were handled using multiple imputation methods,and relevant feature variables were screened using Boruta analysis.We constructed various machine learning models,including Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),eXtreme Gradient Boosting(XGBoost),K-Nearest Neighbors(KNN),Neural Network(NNET),Naive Bayes(NB),and Random Forest(RF),to evaluate their performance in predicting intraoperative bleeding and prolonged postoperative length of hospital stay.The optimal model was then selected and further integrated using a weighted ensemble,aiming to achieve the best prognosis by recommending usage strategies for tranexamic acid.The predictive performance of the constructed model was then verified against the testing set,and compared with the physician decision-making to complete the evaluation.Results In predicting intraoperative bleeding,the RF model achieved an area under the receiver operating characteristic curve(AUC)of 0.73,which was significantly better than other models.In predicting the prolonged postoperative length of hospital stay,the XGBoost model performed the best,with an AUC value of 0.84.Based on the above best-performing models,an ensemble strategy was implemented.The patients who followed the recommended strategy had reduced intraoperative bleeding and shorter postoperative length of hospital stay.Conclusion The use of tranexamic acid is associated with intraoperative bleeding and postoperative length of hospital stay.Personalized decision-making recommendation based on our constructed model can effectively improve the outcomes of the patients undergoing orthopedic surgery.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Objective To investigate the protective effect of Gynostemma pentaphyllum on memory of individuals rapidly ascending to high altitudes.Methods Twenty-one healthy subjects were randomly divided into a G.pentaphyllum food group(n=12)and a control group(n=9).The first group consumed G.pentaphyllum food for seven consecutive days while the control group received placebos.Both groups ascended from the plains to an altitude of 3600 m.Memory function was assessed using the matching memory and sequential memory tests of a cognitive evaluation system on day 1 and day 7 on the plains,and at 24 and 48 h after ascending to the high altitude.Scores of acute mountain sickness symptoms were also recorded.Results After 24 h of stay at the high altitude,the score of headache of the G.pentaphyllum food group was significantly lower than that of the control group(P＜0.05).Cognitive test results showed that the matching memory accuracy and sequential memory accuracy of the control group at 24 and 48 h were significantly lower than those on the plains(P＜0.05).In contrast,the G.pentaphyllum food group performed significantly better than the control group in these metrics(P＜0.05).Conclusion Regular consumption of G.pentaphyllum food can effectively alleviate headache symptoms in individuals rapidly ascending to high altitudes and mitigate the decline in working memory,short-term memory,and memory spans caused by acute hypoxic exposure.