Objective: To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases, addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods. Methods: The fundus multi-label classification dataset with 12 disease categories (FMLC-12) dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition (ODIR) and Retinal Fundus Multi-Disease Image Dataset (RFMiD), yielding 6 936 fundus images across 12 retinal pathology categories, and the framework was validated on both FMLC-12 and ODIR. Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine (TCM) ophthalmology, the dual-branch multi-label network (DBMNet) was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling. The architecture employed a TransNeXt backbone within a dual-branch design: one branch processed red-green-blue (RGB) images to capture color-dependent features, such as vascular patterns and lesion morphology, while the other processed grayscale-converted images to enhance subtle textural details and contrast variations. A feature interaction module (FIM) effectively integrated the multi-scale features from both branches. Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM. The performance of DBMNet was compared against four state-of-the-art methods, including EfficientNet Ensemble, transfer learning-based convolutional neural network (CNN), BFENet, and EyeDeep-Net, using mean average precision (mAP), F1-score, and Cohen's kappa coefficient. Results: The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline, increasing from 34.41% to 44.24%, and the addition of FIM further boosted mAP to 49.85%. On FMLC-12, DBMNet achieved an mAP of 49.85%, a Cohen’s kappa coefficient of 62.14%, and an F1-score of 70.21%. Compared with BFENet (mAP: 45.42%, kappa: 46.64%, F1-score: 71.34%), DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa, while BFENet achieved a marginally higher F1-score. On ODIR, DBMNet achieved an F1-score of 85.50%, comparable to state-of-the-art methods. Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture, significantly improving multi-label fundus disease classification. The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases, providing a promising and clinically applicable pathway for standardized, intelligent fundus disease classification.

Objective To investigate the epidemiological characteristics and changing trend of typhus in Shaanxi Province from 2005 to 2023 to provide a scientific basis for its prevention and control. Methods Excel 2007, SPSS 25.0, Joinpoint 4.9.1.0, and Geoda 1.6 were used for data collection and statistical analysis. Super Map was used for data visualization to describe the changing characteristics of the disease. Results A total of 394 typhus cases were reported in Shaanxi Province from 2005 to 2023. The average annual incidence of typhus was 0.054/100 000, showing a dynamic fluctuation trend(AAPC=-3.3, t=-0.3, P>0.05). The cases were mainly concentrated in Baoji, Hanzhong and Xi′an, accounting for 78.68%. The incidence peak was from May to October, accounting for 64.21% of annual incidence. The epidemic season was from May to October and December. The incidence of the disease was concentrated in the 40-69 age group, accounting for 58.88%, and the sex was 1.07:1. The main occupation was farmers, accounting for 72.08%. The median time from onset to diagnosis was 7 days. Global spatial autocorrelation analysis showed that there were significant spatial autocorrelations in 11 years from 2005 to 2023(P<0.05). Local spatial autocorrelation analysis detected a total of 43“high-high”clustering areas, mainly concentrated in Baoji City. Conclusions The overall incidence of typhus in Shaanxi Province showed a dynamic fluctuation trend, with notable seasonal and regional aggregation. The incidence of typhus was higher in middle-aged and elderly people in rural areas. Surveillance should be strengthened in typhus endemic areas in summer and autumn, and health education should be conducted for key population to form good health habits and reduce the incidence of typhus.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

Metabolic-associated fatty liver disease (MAFLD) and osteoporosis (OP) are two very common metabolic diseases. A growing body of experimental evidence supports a pathophysiological link between MAFLD and OP. MAFLD is often associated with the development of OP. Rutaecarpine (RUT) is one of the main active components of Chinese medicine Euodiae Fructus. Our previous studies have demonstrated that RUT has lipid-lowering, anti-inflammatory and anti-atherosclerotic effects, and can improve the OP of rats. However, whether RUT can improve both fatty liver and OP symptoms of MAFLD mice at the same time remains to be investigated. In this study, we used C57BL/6 mice fed a high-fat diet (HFD) for 4 months to construct a MAFLD model, and gave the mice a low dose (5 mg·kg^-1) and a high dose (15 mg·kg^-1) of RUT by gavage for 4 weeks. The effects of RUT on liver steatosis and bone metabolism were then evaluated at the end of the experiment [this experiment was approved by the Experimental Animal Ethics Committee of Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences (approval number: IMB-20190124D₃03)]. The results showed that RUT treatment significantly reduced hepatic steatosis and lipid accumulation, and significantly reduced bone loss and promoted bone formation. In summary, this study shows that RUT has an effect of improving fatty liver and OP in MAFLD mice.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To investigate the clinical and pathological characteristics of adrenal cortical carcinoma(ACC),compare differences between hypercortisolism and non-functional ACC,and assess the diagnostic value of indicators such as Ki-67 index.Methods The clinical data of 57 ACC patients admitted to the First Medical Center of Chinese PLA General Hospital from January 2015 to March 2025 were retrospectively analyzed.According to the results of endocrine function assessment,47 of these patients were divided into hypercortisolism group(n=19)and non-functional group(n=28).The differences in clinical and pathological characteristics between the two groups were compared,and non-parametric tests and Spearman correlation analysis were used to explore the relationship between Ki-67 index and tumor stage as well as imaging features.Results Among the 57 patients,there were 20 males and 37 females,with a male-to-female ratio of 1:1.85.The age ranged from 16 to 76 years,and the age at diagnosis was(48.7±13.3)years.The tumor diameter was(10.53±4.14)cm.The tumors were located on the right side in 12 cases(21.1%),on the left side in 34 cases(59.6%),and bilaterally in 11 cases(19.3%).Among them,16 cases(28.1%)were complicated with glucose metabolism disorders,31 cases(54.3%)had hypertension,and 20 cases(35.1%)had hypokalemia.According to ENSAT staging,there were 0 cases in stage Ⅰ,15 cases(26.3%)in stage Ⅱ,24 cases(42.1%)in stage Ⅲ,and 18 cases(31.6%)in stage Ⅳ.Endocrine function assessment was completed in 47 of the 57 patients,including 28 cases(59.6%)of non-functional ACC and 19 cases(40.4%)of hypercortisolism(including 1 case of hypercortisolism combined with increased sex hormone secretion).Compared with non-functional group,hypercortisolism group had a significantly higher prevalence of hypertension(P=0.014),later ENSAT stage(P=0.010),and a higher proportion of hypervascularization(P=0.048).The median Ki-67 index was 20%(10%-40%),showing no significant correlation with either the maximum tumor diameter or SUVmax value,but it was related to ENSAT staging,with Ki-67 index in stageⅣ patients being significantly higher than that in stage Ⅱ(P=0.032).Immunohistochemistry results showed that the positive rate of Inhibin-α was 84.8%,and the positive rate of Melan-A was 40.9%.Conclusions ACC is a rare malignant endocrine tumor.ACC patients with hypercortisolism are more likely to be complicated with hypertension,have later staging,and more common hypervascular manifestations.Clinically,their endocrine function should be prioritized for assessment,and more active treatment strategies should be adopted.Diagnosis should be combined with imaging characteristics(such as hypervascularization)and immunohistochemical indicators(Ki-67,Inhibin-α,Melan-A).The significant increase in Ki-67 is in the advanced stage can serve as an important prognostic indicator to guide individualized treatment.

Objective To compare the differences in exosomes derived from testicular tissue between WT(wild type)mice and sdy mice with dysbindin-1(dystrobrevin binding protein 1)deletion mutations,and identify their protein components to explore the possible role of dysbindin-1 in the formation of exosomes derived from mouse testicular tissue.Methods The exosomes derived from mouse testicular tissue of WT and sdy mice were isolated by sucrose ultracentrifugation method.The expression of exosomes proteins was analyzed by Western blotting,the morphology of exosomes was observed by negative staining under transmission electron microscope(TEM),the particle size and distribution were analyzed by dynamic light scattering particle size analyzer,and the protein contents of exosomes were detected by mass spectrometry analysis.CD63+exosomes were obtained by immunoprecipitation with magnetic beads.Krt5(keratin5)protein was selected for validation.Results Dysbindin-1 deletion did not affect the morphology and quantity of exosomes,but decreased the expression of CD63,a marker of exosomes.Compared with the WT mice,there were 159 proteins that were highly expressed,209 proteins that were lowly expressed,and 184 proteins that were specifically expressed in the exosomes derived from sdy mice testicular tissue.In this experiment,CD63+exosomes from testicular tissue were obtained and 12 proteins were screened.There was indeed an interaction between krt5 protein and dysbindin-1.Interestingly,it was found that the expression of krt5 in the exosomes derived from sdy mice testicular tissue decreased after dysbindin-1 deletion.Conclusion After dysbindin-1 deletion,the morphology and quantity of exosomes derived from mouse testicular tissue are not affected,but dysbindin-1 may affect the types and content of exosomal proteins,by affecting the transport of exosome proteins through protein interactions.

Intravascular ultrasound(IVUS)is an ultrasound-based endovascular imaging technique,which has been widely used for the diagnosis and interventional treatment of coronary artery diseases.Intravascular ultrasound virtual histology(IVUS-VH)can qualitatively analyze the composition of intraarterial lesions according to the echo signal spectrum reflected by different pathological tissues,providing important reference information for cardiac and endovascular interventional procedures.In recent years,the application of IVUS-VH in lower extremity arterial revascularization has gradually attracted clinical attention.Especially,IVUS-VH may have certain value in predicting the prognosis.This article describes the application of IVUS-VH in lower extremity arterial revascularization,focusing on its value in predicting the prognosis.

Objective:To establish a mouse model of liver cancer resistant to PD-1 monoclonal antibody and analyze the changes in its metabolomics to explore the potential mechanism of drug resistance.Methods:BALB/c mice were randomly divided into control and treatment groups after being loaded with tumor,and a normal group was additionally set up.The normal and control groups were injected with saline,and the treatment group was injected with PD-1 monoclonal antibody,after which the mice in the treatment group were screened for drug resistant and response groups.Observed the drug-resistant situation,body mass,tumor growth and survival rate of mice in each group,calculate the spleen index.The pathological features of tumor tissues were observed by HE staining method.Serum metabolites were detected by non-targeted metabolomics.Finally,a bivariate Pearson correlation analysis was conducted between the differential serum metabolites and tumor size.Results:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established,and the drug resistance rate of the mice was 50％.Compared with the normal and response groups,mice in the resistant group showed an increase in body weight,a significant increase in tumor volume,a decrease in survival rate,and a significant increase in splenic index.There was less lymphocyte infiltration in the tumor tissue.Metabolomics analysis showed that the serum levels of glutamic acid and aspartic acid increased and malic acid decreased in the resistant mice compared with the response group,and these changes were closely related to the arginine biosynthesis pathway.Conclusions:The tumor-bearing mouse model with PD-1 monoclonal antibody resistance was successfully established.The changes in its peripheral serum metabolomics mainly involve arginine metabolism and the related changes of aspartate,malate and glutamate.