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 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 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 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.

ObjectiveBased on network pharmacology and molecular docking techniques， the mechanism of essential oil from Chimonanthus nitens leaves （CLO） in the treatment of acute lung injury （ALI） was predicted， and a rat model of ALI was established to verify the mechanism of CLO. MethodThe composition of CLO was determined by gas chromatography-mass spectrometry （GC-MS）. The component targets were obtained from PharmMapper and SwissTargetPrediction databases， ALI-related targets were obtained from GeneCards， Online Mendelian Inheritance in Man （OMIM） and DisGeNET， cross-over analysis with differential expressed genes （DEGs） of ALI obtained from Gene Expression Omnibus （GEO） on the Venny 2.1.0 platform yielded potential anti-ALI targets of CLO. Protein-protein interaction （PPI） analysis of potential targets was carried out by STRING 11.5. The tissue expression profiles of potential targets were obtained from the National Center for Biotechnology Information （NCBI） and the target tissue distribution maps were constructed. Potential targets were analyzed for Gene Ontology （GO） function and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment by RStudio 4.0.0 software. Composition-target-pathway network was constructed by Cytoscape 3.9.1 software， and key components and pathways were screened out and verified by molecular docking. ALI model was established by lipopolysaccharide （LPS） induction， levels of interleukin （IL）-6 and tumor necrosis factor （TNF）-α in serum of rats were measured， the expression levels of IL-17 protein in the lung tissue of ALI rats were analyzed by immunohistochemistry. ResultA total of 19 components of CLO were identified by GC-MS， and 18 potential targets were obtained by target screening. After PPI analysis， 15 target proteins with interaction relationship were obtained， further analysis showed that they were highly expressed in lung and thymus. The network diagram of component-target-pathway was analyzed to obtain the key components， including bornyl acetate， linalool， elemol， geranyl isobutyrate， and the core targets of matrix metalloproteinase 13 （MMP13）， S100 calcium binding protein A9 （S100A9）， spleen tyrosine kinase （SYK）， as well as the main signaling pathways， such as IL-17 and TNF. The results of molecular docking showed that the key components were stably bound to MMP13 and S100A9 of IL-17 signaling pathway. The results of pharmacological experiment confirmed that CLO could significantly inhibit the expression of IL-6 and TNF-α in serum of ALI rats， and decrease the expression of IL-17 protein in lung tissue of ALI rats. ConclusionCLO can achieve the therapeutic effect on ALI and protect lung tissue， the mechanism may be related to the decrease of the expression of IL-6 and TNF-α in serum and the inhibition of the activation of IL-17 signaling pathway in lung tissue of ALI rats.

BACKGROUND: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. METHODS: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). RESULTS: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. CONCLUSIONS A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.
Humans ; Female ; Breast Neoplasms/genetics* ; East Asian People ; Neoplasm Recurrence, Local/genetics* ; Breast ; Algorithms ; Chronic Disease ; Prognosis ; Tumor Microenvironment