Objective To investigate muscle mass reduction and the effect of exercise training intervention in non-obese patients with type 2 diabetes (T2DM). Methods A total of 324 non-obese patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University were enrolled from February 2023 to February 2025. Dual-energy X-ray absorptiometry was adopted to detect and analyze the data of appendicular skeletal muscle index (ASMI). Non-obese T2DM patients were classified into an observation group (n=162, receive sports training intervention) and a control group (n=162, receiving routine exercise intervention) by adopting random number grouping criteria. Both groups were intervened for 3 months. The muscle mass indicators [ASMI, body mass index (BMI), and body fat rate], exercise ability [6-minute walking distance (6MWD), grip strength, and one-leg standing time], metabolic indicators [fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), and homeostasis model assessment insulin resistance index (HOMA-IR)], and quality of life [Diabetes Quality of Life Scale (DQOL)] were compared between the two groups to evaluate the effectiveness of sports training intervention. Results A total of 324 non-obese T2DM patients were enrolled, including 123 cases with reduced muscle mass (37.96%). There were no significant differences in the baseline data and the proportion of patients with muscle mass reduction between the two groups before intervention (P>0.05). After intervention, the ASMI, 6MWD, grip strength, and one-leg standing time in the observation group were higher or longer than those of the control group (P<0.05), while the body fat rate, FPG, HbA1c, HOMA-IR and DQOL scores were lower than those of the control group (P<0.05). Conclusion The incidence of muscle mass reduction is relatively high among non-obese T2DM patients, and exercise training intervention has significant effects on improving muscle mass, metabolic status, exercise capacity and quality of life in non-obese T2DM patients.

Puji Xiaoduyin， a specialized formula for the swollen-head epidemic， was recorded in the Catalogue of Ancient Classical Formula （the Second Batch）-Han Medicine， published in September 2023. It had been inherited and developed by medical experts of successive generations and passed down to this day. This paper sorted out the historical evolution of this formula using bibliometric methods. It also comprehensively analyzed key information on the formula name， historical origin， drug dosage， herb origin， processing methods， decocting methods， function， and clinical applications. Additionally， this paper analyzed the application of this formula in both modern and ancient times. Results showed that the formula was first recorded as "Puji Xiaodu Yinzi" in LI Dongyuan's Proven Formulas written by LI Gao from the Jin dynasty. The medicinal composition and dosage were： Scutellariae Radix and Coptidis Rhizoma （20.65 g each）， Ginseng Radix et Rhizoma 12.39 g， Scrophulariae Radix， Citri Reticulatae Pericarpium， and Glycyrrhizae Radix et Rhizoma （8.26 g each）， Forsythiae Fructus， Arctii Fructus， Isatidis Radix， and Lasiosphaera Calvatia （4.13 g each）， Bombyx Batryticatus and Cimicifugae Rhizoma （2.891 g each）， Bupleuri Radix and Platycodonis Radix （8.26 g each）. These medicines were grounded to fine powder. One dose， including 20.65 g of the powder， was mixed with 600 mL of water and decocted to 300 mL. After abandoning slag， the medicine should be taken warm frequently. In the formula， Bombyx Batryticatus is stir-fired. With the effect of dispersing wind and clearing heat， removing stagnation and dissipating mass， the formula is specialized in swollen-head epidemic， pestilence， red and swelling head， face， and neck， dry mouth and tongue， as well as other diseases resulting from toxic heat stagnated in the upper jiao. The formula is widely used in treating diseases involving the respiratory， dermal， ophthalmologic， otolaryngologic， and nervous systems. The formula is most frequently used for respiratory diseases， with a wide range of symptoms including parotitis/mumps （66 times）， followed by tonsillitis （28 times）. In conclusion， the broadly applied formula has accurate efficacy and great development value.

Puji Xiaoduyin， a specialized formula for the swollen-head epidemic， was recorded in the Catalogue of Ancient Classical Formula （the Second Batch）-Han Medicine， published in September 2023. It had been inherited and developed by medical experts of successive generations and passed down to this day. This paper sorted out the historical evolution of this formula using bibliometric methods. It also comprehensively analyzed key information on the formula name， historical origin， drug dosage， herb origin， processing methods， decocting methods， function， and clinical applications. Additionally， this paper analyzed the application of this formula in both modern and ancient times. Results showed that the formula was first recorded as "Puji Xiaodu Yinzi" in LI Dongyuan's Proven Formulas written by LI Gao from the Jin dynasty. The medicinal composition and dosage were： Scutellariae Radix and Coptidis Rhizoma （20.65 g each）， Ginseng Radix et Rhizoma 12.39 g， Scrophulariae Radix， Citri Reticulatae Pericarpium， and Glycyrrhizae Radix et Rhizoma （8.26 g each）， Forsythiae Fructus， Arctii Fructus， Isatidis Radix， and Lasiosphaera Calvatia （4.13 g each）， Bombyx Batryticatus and Cimicifugae Rhizoma （2.891 g each）， Bupleuri Radix and Platycodonis Radix （8.26 g each）. These medicines were grounded to fine powder. One dose， including 20.65 g of the powder， was mixed with 600 mL of water and decocted to 300 mL. After abandoning slag， the medicine should be taken warm frequently. In the formula， Bombyx Batryticatus is stir-fired. With the effect of dispersing wind and clearing heat， removing stagnation and dissipating mass， the formula is specialized in swollen-head epidemic， pestilence， red and swelling head， face， and neck， dry mouth and tongue， as well as other diseases resulting from toxic heat stagnated in the upper jiao. The formula is widely used in treating diseases involving the respiratory， dermal， ophthalmologic， otolaryngologic， and nervous systems. The formula is most frequently used for respiratory diseases， with a wide range of symptoms including parotitis/mumps （66 times）， followed by tonsillitis （28 times）. In conclusion， the broadly applied formula has accurate efficacy and great development value.

Diabetic retinopathy(DR), the most common microvascular complication of diabetes, has become a leading cause of visual impairment and blindness across all age groups. The early diagnosis and severity assessment of DR rely on the precise evaluation of retinal microvascular alterations. The periarterial capillary-free zone(paCFZ), a physiological avascular region surrounding retinal arteries, has recently been recognized as an important biomarker reflecting the status of retinal microcirculation. Advances in optical coherence tomography angiography(OCTA)have enabled noninvasive, high-resolution quantification of the paCFZ, offering a novel approach for the early detection and stratification of DR. This review systematically summarizes the definition and developmental mechanism of the paCFZ, as well as its morphological characteristics across different stages of DR, with a particular focus on the advantages of OCTA in visualizing and quantifying the paCFZ. We further discuss the differential manifestations of the paCFZ in nonproliferative DR and proliferative DR, and its associations with retinal ischemia and oxygenation status. In addition, the potential clinical value of paCFZ in evaluating responses to anti-vascular endothelial growth factor(VEGF)therapy and predicting disease progression is summarized. Finally, the challenges in clinical translation and future research directions are addressed, aiming to provide theoretical support and new perspectives for early screening, risk stratification, and personalized management of DR.

Traditional treatment for type 1 diabetes mellitus （T1DM） primarily involves insulin replacement， yet some patients encounter issues such as significant blood glucose fluctuations， high risk of hypoglycemia， and weight gain. In recent years， the adjuvant therapeutic role of non-insulin hypoglycemic drugs in T1DM has gradually gained attention. This article reviews the mechanisms of action and clinical research progress of five types of non-insulin hypoglycemic drugs in the treatment of T1DM： amylin analogues （pramlintide）， biguanides （metformin）， sodium-glucose co-transporter 2 inhibitor， dipeptidyl peptidase-4 inhibitor， and glucagon-like peptide-1 receptor agonist. It is found that these drugs can enhance clinical benefits for T1DM patients by improving insulin sensitivity， delaying gastric emptying， promoting urinary glucose excretion， and regulating incretin levels， thereby reducing glycated hemoglobin levels， decreasing insulin dosage， and managing body weight. Simultaneously， these drugs also present limitations such as low patient compliance due to complex dosing regimens， increased risk of diabetic ketoacidosis， and heterogeneity in glycemic control. Future research could focus on developing individualized treatment strategies， combining pharmacogenomics with novel biomarkers to precisely identify subpopulations of patients who may benefit， and delving into the potential value of these drugs in delaying diabetic vascular complications and improving patients’ quality of life.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Objective To conduct comprehensive clinical evaluation of injectable anti-inflammatory and hepatoprotective drugs with different mechanisms of action,and to provide a basis for drug selection and rational drug use in medical institutions.Methods Twenty-two experts in clinical and pharmacological fields were organized to construct a quantitative rating scale for the comprehensive clinical evaluation of drugs by applying the literature research method,expert interview method,and Delphi method,through seminars and interviews,and by referring to the real-world clinical data and evidence-based medical evidence such as the Guidelines for the Management of Comprehensive Clinical Evaluation of Drugs,so as to conduct a comprehensive evaluation of eight injectable anti-inflammatory and hepatoprotective drugs in terms of six dimensions:effectiveness,safety,economy,appropriateness,accessibility and maturity.Results A comprehensive clinical evaluation index system of injectable anti-inflammatory and hepatoprotective drugs for the treatment of drug-induced liver injury was constructed,including 6 first-level indexes,14 second-level indexes,and 27 third-level indexes,with a total of 100 points.The scoring results showed that among the evaluated varieties,the scores were,in descending order,magnesium isoglycyrrhizinate injection,compound glycyrrhizin injection,polyene phosphatidylcholine injection,reduced glutathione for injection,thiopronin injection,compound ammonium glycyrrhizinate injection,acetylcysteine injection and diammonium glycyrrhizinate injection.Conclusion The constructed quantitative rating scale for comprehensive clinical evaluation of drugs is operable,and the evaluation process can provide academic guidance for exploring the standardized path of comprehensive clinical evaluation of drugs,which needs to be applied in combination with the actual drug varieties of the medical institutions as well as the specific conditions of the patients to make individualized therapeutic choices.

Objective:To explore the types and characteristics of self-management behaviors among stroke patients, as well as to analyze the influencing factors associated with these different types, providing a reference for developing intervention programs aimed at enhancing self-management behaviors in stroke patients.Methods:This study adopted a cross-sectional survey design. The stroke inpatients were selected through convenience sampling from the Department of Neurology at Huashan Hospital, Fudan University between October 2023 and August 2024. Data collection was conducted using the following instruments: the General Information Questionnaire, Stroke Self-Management Behavior Rating Scale, Self-Efficacy for Managing Chronic Disease 6-Item Scale, Stroke Health Knowledge Questionnaire, Social Support Rating Scale, Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, and the modified Rankin Scale. Stroke patients' self-management behaviors were categorized using systematic cluster analysis, and disordered multi-class Logistic regression was employed to identify the influencing factors associated with each category.Results:Finally, 210 stroke patients were enrolled, there were 148 males and 62 females, aged (60.82 ± 13.05) years. The total score of self-management behavior in stroke patients was (144.18 ± 23.24) points, with a score rate of 56.54%. Systematic cluster analysis identified four distinct self-management behaviors patterns: consistent implementers (25.71%, 54/210); unrealistically optimistic(54.76%, 115/210); optimistically proactive (13.81%, 29/210); and passive and resigned (5.71%, 12/210). Disordered multi-class Logistic regression analysis indicated that higher scores in stroke-related health knowledge were associated with a greater likelihood of being categorized as stable practice type and optimistic proactive type ( OR=1.130, 1.254, both P<0.05). Conversely, increased levels of depression correlate with a higher probability of being classified as passive waiting type ( OR=0.684, 0.722, 0.540, all P<0.05). Additionally, lower modified Rankin Scale scores were linked to an increased tendency to fall into the categories of stable practice type and blind optimism type ( OR=19.759, 23.148, both P<0.05). Conclusions:The self-management behaviors of stroke patients are generally suboptimal and exhibited distinct classification features. Significant differences are observed in stroke health knowledge, depression, and the modified Rankin Scale scores among the four patient types. Healthcare professionals should tailor intervention measures to the characteristics of each type to enhance patients' self-management capacity.

Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.