Objective: To investigate methods for constructing a high-quality instructional dataset for traditional Chinese medicine (TCM) mental disorders and to validate its efficacy. Methods: We proposed the Fine-Med-Mental-T&P methodology for constructing high-quality instruction datasets in TCM mental disorders. This approach integrates theoretical knowledge and practical case studies through a dual-track strategy. (i) Theoretical track: textbooks and guidelines on TCM mental disorders were manually segmented. Initial responses were generated using DeepSeek-V3, followed by refinement by the Qwen3-32B model to align the expression with human preferences. A screening algorithm was then applied to select 16 000 high-quality instruction pairs. (ii) Practical track: starting from over 600 real clinical case seeds, diagnostic and therapeutic instruction pairs were generated using DeepSeek-V3 and subsequently screened through manual evaluation, resulting in 4 000 high-quality practice-oriented instruction pairs. The integration of both tracks yielded the Med-Mental-Instruct-T&P dataset, comprising a total of 20 000 instruction pairs. To validate the dataset’s effectiveness, three experimental evaluations (both manual and automated) were conducted: (i) comparative studies to compare the performance of models fine-tuned on different datasets; (ii) benchmarking to compare against mainstream TCM-specific large language models (LLMs); (iii) data ablation study to investigate the relationship between data volume and model performance. Results: Experimental results demonstrate the superior performance of T&P-model fine-tuned on the Med-Mental-Instruct-T&P dataset. In the comparative study, the T&P-model significantly outperformed the baseline models trained solely on self-generated or purely human-curated baseline data. This superiority was evident in both automated metrics (ROUGE-L > 0.55) and expert manual evaluations (scoring above 7/10 across accuracy). In benchmark comparisons, the T&P-model also excelled against existing mainstream TCM LLMs (e.g., HuatuoGPT and ZuoyiGPT). It showed particularly strong capabilities in handling diverse clinical presentations, including challenging disorders such as insomnia and coma, showcasing its robustness and versatility. Data ablation studies showed that T&P-model performance had an overall upward trend with minor fluctuations when training data increased from 10% to 50%; beyond 50%, performance improvement slowed significantly, with metrics plateauing and approaching a saturation point. Conclusion This study has successfully constructed the specialized Med-Mental-Instruct-T&P instruction dataset for TCM mental disorders proposed the systematic Fine-Med-Mental-T&P methodology for its development, effectively addressing the critical challenge of high-quality, domain-specific data scarcity in TCM, and providing essential data support for developing intelligent TCM diagnostic and therapeutic systems.

Objective: To investigate the trends in incidence and mortality of lung cancer in Huangpu District, Shanghai Municipality from 2002 to 2019, so as to provide the evidence for formulating lung cancer prevention and control measures. Methods: Data of lung cancer incidence and mortality among residents in Huangpu District from 2002 to 2019 were collected through the Shanghai Cancer Registration and Reporting Management System. The crude incidence and mortality of lung cancer was calculated, and standardized by the data from the Chinese Fifth National Population Census in 2000 (Chinese-standardized rate) and the Segi's world standard population in 1960 (world-standardized rate). The trends in incidence and mortality of lung cancer among residents by age and gender were evaluated using annual percent change (APC). Results: A total of 12 965 cases of lung cancer were reported in Huangpu District from 2002 to 2019, and the crude incidence rate was 80.66/105, the Chinese-standardized incidence rate was 34.54/105, and the world-standardized incidence rate was 31.30/105, all showing upward trends (APC=4.588%, 2.933% and 3.247%, all P<0.05). A total of 10 102 deaths of lung cancer were reported, and the crude mortality rate was 62.30/105, showing an upward trend (APC=0.959%, P<0.05); the Chinese-standardized mortality was 25.93/105, and the world-standardized mortality was 22.05/105, both showing downward trends (APC=-1.282% and -1.263%, both P<0.05). The crude incidence and mortality rates of lung cancer in males were higher than those in females (101.39/105 vs. 60.52/105, 85.45/105 vs. 39.87/105, both P<0.05). The crude incidence and mortality rates of lung cancer showed upward trends with age (both P<0.05), reaching their peaks in the age groups of 80-<85 years (341.37/105) and 85 years or above (355.97/105), respectively. Conclusions The incidence of lung cancer showed an upward trend, while the mortality showed a downward trend in Huangpu District from 2002 to 2019. Elderly men were the high-risk group for lung cancer incidence and mortality.

[Objective] To determine the correlation between traditional Chinese medicine (TCM) inspection of spirit classification and the severity grade of depression based on facial features, offering insights for intelligent intergrated TCM and western medicine diagnosis of depression. [Methods] Using the Audio-Visual Emotion Challenge and Workshop (AVEC 2014) public dataset on depression, which conclude 150 interview videos, the samples were classified according to the TCM inspection of spirit classification: Deshen (得神, presence of spirit), Shaoshen (少神, insufficiency of spirit), and Shenluan (神乱, confusion of spirit). Meanwhile, based on Beck Depression Inventory-II (BDI-II) score for the severity grade of depression, the samples were divided into minimal (0 – 13, Q1), mild (14 – 19, Q2), moderate (20 – 28, Q3), and severe (29 – 63, Q4). Sixty-eight landmarks were extracted with a ResNet-50 network, and the feature extracion mode was stadardized. Random forest and support vectior machine (SVM) classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression, respectively. A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships. [Results] The analysis revealed a statistically significant and moderately strong association between TCM spirit classification and the severity grade of depression, as confirmed by a Chi-square test (χ2 = 14.04, P = 0.029) with a Cramer’s V effect size of 0.243. Further exploration using association rule mining identified the most compelling rule: “moderate depression (Q3) → Shenluan”. This rule demonstrated a support level of 5%, indicating this specific co-occurrence was present in 5% of the cohort. Crucially, it achieved a high Confidence of 86%, meaning that among patients diagnosed with Q3, 86% exhibited the Shenluan pattern according to TCM assessment. The substantial Lift of 2.37 signifies that the observed likelihood of Shenluan manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent—compelling evidence of a highly non-random association. Consequently, Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3, forming a clinically significant phenotype within this patient subgroup. [Conclusion] Automated facial analysis can serve as a common lens for TCM and western psychological assessments align in the diagnosis of depression. The inspection of spirit decline trajectory parallels worsening depression, supporting early screening and stratified intervention, and providing a reference for the intelligent assistance of integrated TCM and western medicine in the diagnosis of depression.

Abstract In the wave of digital and intelligent applications, artificial intelligence (AI) is transforming the development trajectories of industries across the globe. Traditional Chinese medicine (TCM), as a cultural treasure of the Chinese nation, carries thousands of years of wisdom and practical experience. However, in the context of the rapid advancements in modern medicine and technology, TCM faces dual challenges: preserving its heritage while innovating. DeepSeek, a major achievement in the field of AI, offers a new opportunity for the development of TCM with its powerful technological capabilities. Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.

With the continuous progress of modern science and technology, the research of TCM inspection is gradually moving towards a new stage of objectification and intelligence. By systematically sorting out the key technologies involved in the objectification of inspection and diagnosis, and summarizing its clinical application progress in the three core areas of face diagnosis, tongue diagnosis and visual diagnosis, it is found that the inspection and diagnosis of TCM can not only significantly improve the efficiency and accuracy of clinical diagnosis with the help of modern technology, but also achieve more results in disease differentiation, constitution identification and efficacy evaluation. The key technologies include digital image processing, infrared thermal imaging, spectroscopy, photoelectric volume and artificial intelligence (AI) technology. In terms of clinical application, facial diagnosis focuses on image and infrared thermography feature analysis, tongue diagnosis involves image, spectral feature research and AI model construction, and eye diagnosis focuses on white eye collaterals extraction, fundus sign analysis and eye movement information research. However, there are still some problems, such as imperfect standardization system, bottleneck of multi-modal fusion and interpretation, insufficient clinical verification and practicability. In the future, the deep integration of TCM observation and modern science and technology should be promoted by means of technological integration and innovation and strengthening interdisciplinary cooperation, so as to help the modernization of TCM diagnosis.

OBJECTIVE: To investigate the clinical features and pathogenesis of a child with Stargardt disease caused by variants of ABCA4 gene. METHODS: A child presented at Shenzhen Eye Hospital between September 5, 2020, and April 3, 2023 was selected as the study subject. Clinical data of the child were collected. Whole exome sequencing was performed on peripheral blood samples from the child and his parents. Candidate variants were validated by Sanger sequencing and bioinformatic analysis. This study was approved by the Medical Ethics Committee of Shenzhen Eye Hospital (Ethics No.: 2022KYPJ072). RESULTS: The child was a 10-year-old male presenting with uncorrected visual acuity of 0.1 in both eyes without improvement with refractive correction. Fundus photography showed diffusely distributed yellow-white flecks in the macular region. FAF revealing central hypofluorescence surrounded by a hyperfluorescent ring, and OCT demonstrating significant foveal thinning (right eye: 45 μm; left eye: 50 μm) with ellipsoid zone disruption. Whole exome sequencing and Sanger sequencing revealed that the child has harbored compound heterozygous variants of the ABCA4 gene, namely c.2384G>T (p.Gly795Val) and c.2903G>A (p.Arg968Glu), which were inherited from his phenotypically normal parents and consistent with an autosomal recessive inheritance. This specific combination of the variants was previously unreported. According to the guidelines from the American College of Medical Genetics and Genomics (ACMG) guidelines, both variants were classified as likely pathogenic (PM2_Supporting+PM3+PP3+PP4; PM1+PM2_Supporting+PP3+PP4). CONCLUSION The novel compound heterozygous variants of the ABCA4 gene probably underlay the genetic etiology of Stargardt disease type 1 in this child. Above finding has expanded the mutational spectrum of the ABCA4 gene among the Chinese population and provided further evidence for understanding the genetic heterogeneity and genotype-phenotype correlation of the Stargardt disease.
Humans ; Male ; Child ; ATP-Binding Cassette Transporters/genetics* ; Stargardt Disease/genetics* ; Heterozygote ; Mutation ; Exome Sequencing ; Macular Degeneration/congenital*

Objective A serum proteomic approach was used to explore the targets of action of Peitu Qingxin Granules(composed of Rhizoma Atractylodis Macrocephalae,Forsythiae Fructus,Imperatae Rhizoma,Pseudostellariae Radix,etc.)in the treatment of atopic dermatitis.Methods Five patients with atopic dermatitis were selected and treated with Peitu Qingxin Granules for 12 weeks,and five healthy volunteers were used as controls.The clinical core evaluation indexes of atopic dermatitis patients after treatment,including Eczema Area and Severity Index/Scoring Atopic Dermatitis(EASI/SCORAD),Pruritus Score,Patient-Oriented Eczema Measure(POEM),and quality of life index,were assessed.Serum samples were examined using data-independent acquisition-mass spectrometry(DIA-MS)technology,and serum differential proteins between atopic dermatitis patients and healthy people,as well as serum differential proteins in atopic dermatitis patients before and after treatment with Peitu Qingxin Granules were screened according to P＜0.05 and Fold Change>1.2.GO function enrichment analysis and KEGG pathway enrichment analysis were performed on the differential proteins.Results(1)Compared with the pre-treatment period,the clinical core evaluation indexes of patients with atopic dermatitis,including the EASI/SCORAD,Pruritus Score,POEM,and quality-of-life index,were significantly improved after treatment,and the differences were all statistically significant(P＜0.05,P＜0.01).(2)A total of 28 differential proteins were analyzed in the healthy control group and atopic dermatitis group,of which 12 proteins expressions were increased and 16 proteins were decreased,including ALAD(δ-aminolevulinic acid dehydrogenase),LTA4H(leukotriene A-4 hydrolase),CA1(carbonic anhydrase 1),F11(coagulation factor XI),and LCP1(lymphocyte cytoplasmic protein 1),etc..The main signaling pathways involved are PI3K-AKT signaling pathway,lipids and atherosclerosis,ECM-receptor interaction,platelet activation,NF-κB signaling pathway,and neutrophil extracellular trap formation.(3)A total of 12 different proteins were analyzed in atopic dermatitis patients before and after treatment with Peitu Qingxin Granules,of which 8 proteins were increased and 4 proteins were decreased,including ALAD,FGA(fibrinogen α-chain),IGHV3-64D,and IGHV3-38.They were mainly involved in signaling pathways such as lipids and atherosclerosis,complement pathway,Staphylococcus aureus infection,NF-κB signaling pathway,fluid shear stress and atherosclerosis.(4)The expressions of three protein targets including ALAD,FGA and IGHV3-64D,were significantly down-regulated in patients with atopic dermatitis and significantly up-regulated after treatment with Peitu Qingxin Granules.Conclusion The differentially expressed proteins ALAD,FGA and IGHV3-64D may be the action targets of Peitu Qingxin Granules in the treatment of atopic dermatitis,which lays the foundation for further experimental validation.

Objective To construct a traditional Chinese medicine(TCM)knowledge base using knowl-edge graph based on deep learning methods,and to explore the application of joint models in intelligent question answering systems for TCM. Methods Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medicawere applied to construct a comprehensive knowledge graph serving as the founda-tion for the intelligent question answering system.In the study,a BERT+Slot-Gated(BSG)deep learning model was applied for the identification of TCM entities and question inten-tions presented by users in their questions.Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user.The Flask framework and BSG model were utilized to develop the intelligent question answering sys-tem of TCM. Results A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn.In the question answer-ing test assisted by a question corpus,the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9,and the accuracy rate for identifying intentions was 99.75%.This indicates that the system is both feasible and practical.Users can interact with the system through the WeChat Official Account platform. Conclusion The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension,indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering sys-tems in TCM.

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM),artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However,the research on intelligent eye diagnosis still faces many challenges,including the lack of standardized and precisely labeled data,multi-modal information analysis,and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis,and explores the implications for the research of eye diagno-sis intelligence.First,a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next,the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last,the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary,research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Pruritus is a common symptom in the patients with end-stage renal disease,up to 80％of the patients receiving hemodialysis suffer from pruritus,40％of these patients experience moderate to severe pru-ritus,which causes poor sleep,depression,work interference,decrease of overall quality of life and increase of death risk.However,at present,the clinical understanding of skin pruritus associated with end-stage kidney disease is not sufficient,and there is a lack of effective theories to reveal its pathogenesis.This article summa-rizes the latest research progress on the pathogenesis of this disease,hoping to provide the new ideas for clini-cal diagnosis and treatment.