ObjectiveTo explore the potential mechanism underlying electroacupuncture therapy with the midnight-noon ebb-low method with hour-prescription for chronic obstructive pulmonary disease （COPD） with lung-qi deficiency syndrome from the perspective of brain and muscle basic helix-loop-helix ARNT-like 1 （BMAL1） as well as neutrophil extracellular traps （NETs）. MethodsThe experiment was conducted in two phases. For the therapeutic effectiveness observation， 24 rats were randomly allocated into control group 1， model group 1， midnight-noon electro-acupuncture group 1 and conventional electro-acupuncture group， with 6 rats per group. Rat models of COPD with lung-qi deficiency syndrome were established via cigarette smoke exposure combined with intratracheal instillation of lipopolysaccharide （LPS）. After successful modelling， rats in midnight-noon electro-acupuncture group 1 received electro-acupuncture at acupoints "Taiyuan （LU 9）"， "Feishu （BL 13）" and "Zusanli （ST 36）" during 05：00—07：00， 30 minutes per treatment once every other day for 14 consecutive days （7 sessions in total）. Rats in the conventional electro-acupuncture group received identical electroacupuncture manipulation at random daytime hours （08：00—18：00）. Pulmonary function parameters including forced expiratory volume in 0.3 second （FEV_0.3）， forced vital capacity （FVC） and FEV_0.3/FVC ratio were detected. Pulmonary histopathological changes were observed via hematoxylin-eosin （HE） staining. Plasma levels of pro-inflammatory factors interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， as well as pulmonary reactive oxygen species （ROS） content， BMAL1 protein expression and the levels of neutrophil extracellular traps （NETs） biomarkers such as myeloperoxidase （MPO）， neutrophil elastase （NE）， and citrullinated histone H3 （CitH3） in lung tissue were determined. For the mechanistic verification phase， 36 rats were divided into control group 2， model group 2， midnight-noon electro-acupuncture group 2， overexpression empty group， overexpression BMAL1 group and overexpression with midnight-noon electro-acupuncture group， with 6 rats in each group. At the 5th week of model construction， rats in the overexpression BMAL1 group and overexpression with midnight-noon electro-acupuncture group received intratracheal instillation of 100 μl adenoviral suspension carrying overexpressed Bmal1 gene. Rats in the overexpression empty group were injected with an equal titer and equal volume of blank adenovirus without the Bmal1 coding sequence into the lung at the identical time point. Midnight-noon electro-acupuncture group 2 and overexpression with midnight-noon electro-acupuncture group were consistent with the aforementioned protocol. Finally， pulmonary levels of MPO， NE and CitH3 were measured in all groups. ResultsCompared with the control group 1， the model group 1 exhibited decreased FEV_0.3， FVC and FEV_0.3/FVC ratio， elevated plasma IL-1β， TNF-α and pulmonary ROS levels， downregulated lung BMAL1 protein expression， and increased contents of MPO， NE and CitH3 （all P＜0.05）； typical NETs-like structures with sparse granular substances attached to fibrous networks were observed under scanning electron microscopy； HE staining revealed damaged alveolar architecture accompanied by inflammatory cell infiltration. Compared with model group 1， both the midnight-noon electro-acupuncture group 1 and conventional electro-acupuncture group achieved obvious improvements in all above indicators， with superior therapeutic effects in midnight-noon electro-acupuncture group 1 （P＜0.05）. Consistently， midnight-noon electro-acupuncture group 1 showed more prominent alleviation of NETs-like structure formation and lung pathological injury compared to the conventional electro-acupuncture group. Mechanistic validation results demonstrated that compared with model group 2， the average optical density of MPO as well as the protein levels of MPO， NE and CitH3 were markedly reduced in midnight-noon electro-acupuncture group 2， overexpression BMAL1 group and overexpression with midnight-noon electro-acupuncture group （P＜0.05）； furthermore， these parameters were significantly lower in the overexpression with midnight-noon electro-acupuncture group than in midnight-noon electro-acupuncture group 2 （P＜0.05）. ConclusionElectro-acupuncture based on the midnight-noon ebb-low method with hour-prescription may alleviate pulmonary inflammation in COPD by upregulating the expression of clock protein BMAL1 and inhibiting excessive accumulation of NETs in lung tissue.

Objective: To investigate the dynamic influence mechanism of the interaction between physical exercise and pre-sleep screen time on sleep quality among college students, so as to provide a theoretical basis for sleep health interventions in the population. Methods: From March to April 2025, a convenience sample of 2 075 students was recruited from eight universities in Jiangsu Province to participate in an online survey. Physical exercise was assessed using the Physical Activity Rating Scale-3 (PARS-3), sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), and pre-sleep screen time behaviors were recorded using a self-designed structured questionnaire. Binary Logistic regression was used to examine the independent associations of physical activity level and pre-sleep screen time with sleep disorders. An interaction effect model was then applied to plot the dose-response relationship among the three variables. Results: The overall prevalence of sleep disorder among college students was 50.3%. Significant differences in the prevalence of sleep disorder were observed across groups of sex, academic discipline, grade, pre-sleep screen time, and physical exercise ( χ 2=42.45, 14.81, 18.99, 113.23, 40.61, all P <0.05). Binary Logistic regression showed that pre-sleep screen time was associated with the risk of sleep disorders (≤15 min: OR =1.70; 16- 30 min : OR =1.91; 31-60 min: OR =3.31; >60 min: OR =5.32), while both moderate ( OR =0.44) and high ( OR =0.57) physical exercise were associated with a reduced risk (all P <0.05). The interaction effect was statistically significant (Wald χ 2= 6.82 , P <0.01). The dose-response curves revealed that, in the low-exercise group, sleep quality deteriorated sharply with increasing screen time (the predicted mean dropped to 28% at the >60 min threshold, P <0.01); in the moderate exercise group, an inverted U-shaped trend was observed, with an optimal window of 16-30 min; and in the high exercise group, a significant buffering effect was found, with the predicted mean remaining at 43% even under >60 min screen exposure, representing a decrease of only 4% compared with the no-screen use group ( P >0.05). Conclusions High screen exposure combined with low physical exercise represents the primary behavioral pattern associated with a high prevalence of sleep disorders. Regular physical exercise can partially offset the negative effects of excessive screen time on sleep.

Objective:To fine-tune the mT5 (massively multilingual pre-trained text-to-text transformer) large language model, automatically generate report conclusions for teaching purposes from chest CT image descriptions, and assess the quality of automatically generated conclusions.Methods:The training set included 3 000 high-quality physical examination chest CT reports from one hospital, and the external validation set consisted of 600 physical examination chest CT reports from two other hospitals. Experienced radiology teaching physicians assessed the consistency between the generated conclusions and the original physician-written conclusions in the external validation set using a 5-point Likert scale across five linguistic indicators (correctness of examination information, correctness of lesion detection, standardization of terminology, applicability of the conclusions, and simplicity of conclusions). Using the original report conclusions as the reference, the accuracy of the conclusions generated based on the external validation set in describing four major thoracic conditions (pulmonary nodules, pneumonia, emphysema, pleural effusion) was evaluated. Perform chi square test using SPSS 25.0.Results:In the external validation set, the mean consistency score between the generated conclusions and the original conclusions given by the radiology teaching physicians was >4 points, indicating agreement with the original conclusions. In the generated conclusions, the description of the four major thoracic conditions demonstrated 0.95-1.00 (95% CI=0.91-1.00) accuracy, 0.76-1.00 (95% CI=0.59-1.00) sensitivity, and 0.97-1.00 (95% CI=0.91-1.00) specificity. Conclusions:The chest CT report conclusion generation system based on the mT5 large language model demonstrated high accuracy and is expected to provide immediate and efficient automated guidance for standardized residency training.

Objective:To develop and evaluate a medical visual question answering (VQA) model for neonatal lung ultrasound (LUS) images to enhance intelligent auxiliary diagnosis of neonatal pulmonary diseases.Methods:Using data from neonates admitted to Beijing Obstetrics and Gynecology Hospital, Capital Medical University (January 2023 to December 2024), an image-question-answer dataset comprising 251 LUS images was constructed [43 pneumonia (17.1%), 42 neonatal respiratory distress syndrome (16.7%), 83 transient tachypnea (33.1%), and 83 normal (33.1%) images] with a four-tier medical question-answer framework. Building upon the Qwen2.5-VL-7B base model and integrating LoRA fine-tuning with chain-of-thought prompting, we developed the NLUS-VQA model to enhance visual-language semantic alignment and enable stepwise clinical reasoning, achieving efficient small-sample adaptation. Model performance was comprehensively assessed through natural language generation metrics (BLEU-4, ROUGE-1/2/L), qualitative evaluation of characteristic recognition, and clinical consistency analysis.Results:(1) Quantitative evaluation demonstrated that NLUS-VQA achieved scores of 22.38 (BLEU-4), 48.26 (ROUGE-1), 22.40 (ROUGE-2), and 37.20 (ROUGE-L), representing significant improvements over baseline models. (2) Qualitatively, the model exhibited strong performance in identifying lung consolidation, coalescent B-lines, and snowflake signs, with its chain-of-thought strategy enhancing clinical interpretability and answer accuracy. (3) Clinically, NLUS-VQA achieved a Cohen's Kappa coefficient of 0.78 and diagnostic accuracy of 80.8% (21/26), indicating substantial agreement with clinical experts.Conclusion:The NLUS-VQA model demonstrates robust interpretability in recognizing key sonographic patterns (e.g. lung consolidation, confluent B-lines, and snowflake signs), providing a scalable framework for small-sample medical image analysis, though diagnostic performance on complex conditions remains limited by dataset scale and minority class representation.

Objective To observe the value of Swin2SR network based on Transformer architecture for reconstructing chest super-resolution CT images.Methods Chest CT data of 218 patients were retrospectively collected.Swin2SR model based on Transformer architecture was adopted to enhance standard 512 matrix(512 × 512)CT images(standard-512 group)into 1 024(SR-1 024 group)and 2 048(SR-2 048 group)matrix SR CT images,respectively.Subjective and objective evaluation of image quality were performed,and the results were compared among groups.Results The subjective scores of overall imaging quality and lesion clarity in SR-1 024 and SR-2 048 groups were both higher than those in standard-512 group(all P＜0.05),while no significant difference was found between the former two(P＞0.05).Meanwhile,no significant difference of objective indexes of imaging quality was observed among 3 groups(all P＞0.05).Conclusion Swin2SR model could reconstruct chest SR CT images without increasing noise and improve imaging quality.

Objective To observe the value of Swin2SR network based on Transformer architecture for reconstructing chest super-resolution CT images.Methods Chest CT data of 218 patients were retrospectively collected.Swin2SR model based on Transformer architecture was adopted to enhance standard 512 matrix(512 × 512)CT images(standard-512 group)into 1 024(SR-1 024 group)and 2 048(SR-2 048 group)matrix SR CT images,respectively.Subjective and objective evaluation of image quality were performed,and the results were compared among groups.Results The subjective scores of overall imaging quality and lesion clarity in SR-1 024 and SR-2 048 groups were both higher than those in standard-512 group(all P＜0.05),while no significant difference was found between the former two(P＞0.05).Meanwhile,no significant difference of objective indexes of imaging quality was observed among 3 groups(all P＞0.05).Conclusion Swin2SR model could reconstruct chest SR CT images without increasing noise and improve imaging quality.

Objective:To fine-tune the mT5 (massively multilingual pre-trained text-to-text transformer) large language model, automatically generate report conclusions for teaching purposes from chest CT image descriptions, and assess the quality of automatically generated conclusions.Methods:The training set included 3 000 high-quality physical examination chest CT reports from one hospital, and the external validation set consisted of 600 physical examination chest CT reports from two other hospitals. Experienced radiology teaching physicians assessed the consistency between the generated conclusions and the original physician-written conclusions in the external validation set using a 5-point Likert scale across five linguistic indicators (correctness of examination information, correctness of lesion detection, standardization of terminology, applicability of the conclusions, and simplicity of conclusions). Using the original report conclusions as the reference, the accuracy of the conclusions generated based on the external validation set in describing four major thoracic conditions (pulmonary nodules, pneumonia, emphysema, pleural effusion) was evaluated. Perform chi square test using SPSS 25.0.Results:In the external validation set, the mean consistency score between the generated conclusions and the original conclusions given by the radiology teaching physicians was >4 points, indicating agreement with the original conclusions. In the generated conclusions, the description of the four major thoracic conditions demonstrated 0.95-1.00 (95% CI=0.91-1.00) accuracy, 0.76-1.00 (95% CI=0.59-1.00) sensitivity, and 0.97-1.00 (95% CI=0.91-1.00) specificity. Conclusions:The chest CT report conclusion generation system based on the mT5 large language model demonstrated high accuracy and is expected to provide immediate and efficient automated guidance for standardized residency training.

Objective:To develop and evaluate a medical visual question answering (VQA) model for neonatal lung ultrasound (LUS) images to enhance intelligent auxiliary diagnosis of neonatal pulmonary diseases.Methods:Using data from neonates admitted to Beijing Obstetrics and Gynecology Hospital, Capital Medical University (January 2023 to December 2024), an image-question-answer dataset comprising 251 LUS images was constructed [43 pneumonia (17.1%), 42 neonatal respiratory distress syndrome (16.7%), 83 transient tachypnea (33.1%), and 83 normal (33.1%) images] with a four-tier medical question-answer framework. Building upon the Qwen2.5-VL-7B base model and integrating LoRA fine-tuning with chain-of-thought prompting, we developed the NLUS-VQA model to enhance visual-language semantic alignment and enable stepwise clinical reasoning, achieving efficient small-sample adaptation. Model performance was comprehensively assessed through natural language generation metrics (BLEU-4, ROUGE-1/2/L), qualitative evaluation of characteristic recognition, and clinical consistency analysis.Results:(1) Quantitative evaluation demonstrated that NLUS-VQA achieved scores of 22.38 (BLEU-4), 48.26 (ROUGE-1), 22.40 (ROUGE-2), and 37.20 (ROUGE-L), representing significant improvements over baseline models. (2) Qualitatively, the model exhibited strong performance in identifying lung consolidation, coalescent B-lines, and snowflake signs, with its chain-of-thought strategy enhancing clinical interpretability and answer accuracy. (3) Clinically, NLUS-VQA achieved a Cohen's Kappa coefficient of 0.78 and diagnostic accuracy of 80.8% (21/26), indicating substantial agreement with clinical experts.Conclusion:The NLUS-VQA model demonstrates robust interpretability in recognizing key sonographic patterns (e.g. lung consolidation, confluent B-lines, and snowflake signs), providing a scalable framework for small-sample medical image analysis, though diagnostic performance on complex conditions remains limited by dataset scale and minority class representation.

Objective·To evaluate the relationship between body mass index(BMI)and chronic metabolic diseases.Methods·The elderly(≥60 years old)who were underwent physical examination in the Physical Examination Center of Renji Hospital,Shanghai Jiao Tong University School of Medicine from 2014 to 2021 were studied.Their results of biochemical indicators were collected.Their height,body weight,and blood pressure were measured by trained nurses.The history of chronic metabolic diseases was collected by self-reported questionnaire.Systolic blood pressure≥140 mmHg(1 mmHg=0.133 kPa),diastolic blood pressure≥90 mmHg,or self-reported hypertension history was defined as hypertension.Fasting blood glucose≥7.0 mmol/L or self-reported history of diabetes was defined as diabetes.Total cholesterol≥6.2 mmol/L,triglyceride≥2.3 mmol/L,or self-reported history of dyslipidemia was defined as dyslipidemia.The relationship between BMI and hypertension,diabetes,and dyslipidemia was evaluated by using receiver operator characteristic(ROC)curve analysis and binary logistic regression.Results·Data of 59 083 subjects were collected[30 807 men and 28 276 women,average age:(67.9±6.3)years old].The prevalence of hypertension,diabetes and dyslipidemia was 76.5%(45 219/59 083),24.1%(14 225/59 083)and 50.0%(29 544/59 083),respectively.Compared to the elderly people aged 60?74 years,those aged 75 years and above had a higher proportion of hypertension and diabetes,and a lower proportion of dyslipidemia and no metabolic abnormalities.With ROC analysis,the BMI cut-off values for hypertension,diabetes,and dyslipidemia were 24.3,23.9,and 23.9 kg/m2.The BMI cut-off values for hypertension and diabetes in elderly men were similar to those in elderly women(for hypertension:24.3 kg/m2 in elderly men vs 24.2 kg/m2 in elderly women;for diabetes:24.0 kg/m2 in elderly men vs 23.7 kg/m2 in elderly women);however,BMI cut-off value for dyslipidemia was obviously higher in elderly men than that in elderly women(24.0 kg/m2 in elderly men vs 22.5 kg/m2 in elderly women).The BMI cut-off value for chronic metabolic diseases was higher in the elderly people aged 60?74 years than that in the elderly people aged 75 years and above(24.2?24.7 kg/m2 vs 22.9?23.8 kg/m2).Conclusion·Elderly people aged 60?74 years should maintain the BMI below 24.0 kg/m2,while those aged 75 years and above should aim for the BMI below 23.0 kg/m2,so as to reduce the risk of chronic metabolic diseases.

With persistent advancement of surgical instruments, methods and techniques, clinical efficacy of liver transplantation has been steadily enhanced. However, the length of anhepatic phase is still an important factor affecting the efficacy of liver transplantation. Rat is one of the major animal models for liver transplantation-related basic research. In this article, multiple approaches for prolonging the anhepatic phase and shortening the operation time during anhepatic phase in rat liver transplantation were reviewed, which consisted of sevoflurane inhalation anesthesia, intravenous infusion via jugular vein indwelling needle, clamping of the abdominal aorta before anhepatic phase, injection of normal saline into portal vein before anhepatic phase, subcutaneous transposition of the spleen, electrocoagulation of hepatic esophageal artery, magnetic ring anastomosis of the superior and inferior hepatic vena cava, cannula anastomosis of the superior and inferior hepatic vena cava, stent anastomosis of the superior and inferior hepatic vena cava, rapid connection device and cannula of portal vein, and ring-shaped cannula of hepatic tissue-preserving inferior hepatic vena cava, aiming to add evidence for prolonging the duration of anhepatic phase, improving the operation efficiency during anhepatic phase and elevating the success rate of rat liver transplantation.