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.

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.

[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.

This article is based on modern psychology and TCM emotional theory,combined with facial expression recognition technology,to apply deep learning methods to the digital research of TCM emotions to more accurately capture and analyze patients'emotional states.A cross-disciplinary framework was established by synthesizing facial expression-emotion correlations from psychological and TCM perspectives.The methodology included:Data annotation of TCM-defined emotional expressions using standardized coding systems;Facial expression acquisition,spatiotemporal feature extraction and emotion classification through a 3D convolutional neural network(3D CNN).The framework achieved 91.43%accuracy in video-based emotion classification.High-arousal emotional states demonstrated superior recognition performance,with anger showing optimal recall(1.000 0)and F1-score(0.946 3),while surprise attained the highest precision(0.976 0).These findings aligned with TCM pathological characteristic of"anger induces qi ascending,surprise disrupts qi flow".The digital recognition framework for TCM emotional quantification based on facial expression recognition technology exhibits strong alignment with TCM observation diagnosis,providing clinicians with an objective tool to assess the"seven emotions"and elucidating emotion-facial correlations in classical TCM theory.Future research should focus on longitudinal validation across diverse populations and establish development pathways for AI-assisted TCM diagnostic systems.

Objective:To translate the quality of life tool for patients with aplastic anaemia and paroxysmal nocturnal haemoglobinuria (QLQ-AA/PNH) into Chinese, and to test its reliability and validity.Methods:According to the scale translation principle, the Chinese version of QLQ-AA/PNH was formed through translation, back translation and cross-cultural adaptation. A cross-sectional survey method was used to conveniently select 58 patients with aplastic anemia who were treated in the hematology department of the Second Affiliated Hospital of Dalian Medical University from January 2018 to September 2023 for investigation, and to evaluate the reliability and validity of the scale.Results:The Chinese version of QLQ-AA/PNH retains 36 items, and 5 common factors (psychological status dimension, life burden dimension, physical condition dimension, illness anxiety dimension and other symptom dimension) were extracted through exploratory factor analysis. The cumulative variance contribution rate reached 71.33%, and the factor load of each entry was greater than 0.5 on corresponding common factors. The Cronbach α coefficient of the scale as a whole was 0.971, the broken half reliability coefficient was 0.985, the Cronbach α coefficient of each common factor was 0.637 to 0.954, and the broken half reliability coefficient was 0.637 to 0.930. Conclusions:The Chinese version of QLQ-AA/PNH has been proved to be valid and reliable. It is a valuable tool for evaluating the quality of life among patients with aplastic anaemia.

Objective:To construct a nursing quality evaluation indicator system for pulse indicator continuous cardiac output (PiCCO) monitoring and provide a basis for evaluating the nursing quality of PiCCO monitoring.Methods:Using Donabedian′s "structure-process-result" three-dimensional quality evaluation model as the theoretical framework, the nursing quality evaluation indicator system for PiCCO was established through literature search, Delphi expert correspondence, and hierarchical analysis during the period May to August 2023.Results:Of the 22 experts, 6 were male and 16 were female, aged (46.59 ± 4.34) years. The return rates of the questionnaires of the 2 rounds of expert correspondence were both 100%, the coefficients of expert authority were both 0.945, and the Kendall coordination coefficients were 0.045-0.186 ( χ2 values were 2.00-221.46, all P<0.05) and 0.045-0.132 ( χ2 values were 2.00-82.16, all P<0.05), respectively. The finalized nursing quality evaluation indicator system for PiCCO included 3 primary indicators, 7 secondary indicators, and 36 tertiary indicators. Conclusions:The nursing quality evaluation indicator system for PiCCO is practical and scientific, which can provide reference for PiCCO monitoring nursing quality evaluation.

As an important part of TCM diagnosis,eye diagnosis is of great significance and value in identifying the symptoms of diseases and predicting the regression of diseases.With the progress of science and technology,the research on"syndrome differentiation by observing the eyes"is gradually converging towards digitalization,standardization and objectification.Based on the theoretical foundation of"syndrome differentiation by observing the eyes",this article reviewed the modern clinical applications of objectifying eye diagnosis from aspects such as observing the scleral vessels,observing fundus signs,and observing eye movement information,and found that modern technological means such as eye diagnostic instrument,eye movement instrument and artificial intelligence algorithms have provided accurate and objective diagnostic bases for TCM syndrome differentiation by observing the eyes to identify the syndrome and have greatly promoted the development of the objectification of eye diagnosis.However,the objective application of eye diagnosis still faces challenges related to technology,data and standardization,calling for further in-depth research and the establishment of unified standards.Exploring the current research status and strategies can provide theoretical references for the objectification of TCM eye diagnosis and the improvement of clinical visual diagnosis accuracy.

Objective:To analyze the clinical features of nasal pleomorphic adenoma and to share clinical insights into its diagnosis and treatment.Methods:This was a case series study. Clinical data of 12 patients with nasal pleomorphic adenoma, confirmed by histopathology, admitted to the Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Qingdao University from 2014 to 2023, were retrospectively analyzed. This cohort included 3 males and 9 females, aged 12-84 years old. The pathogenesis, clinical manifestations, imaging features, pathological features, treatment methods and prognosis were analyzed.Results:Among the 12 patients with nasal pleomorphic adenoma, the most common symptom was nasal obstruction (8 cases), and the most common site was nasal septum (7 cases). Of the 12 patients, 9 had benign tumors, and 3 had malignant tumors. Postoperative follow-up ranged from 10 months to 9 years. One benign case recurred at 5 years after surgery and was left untreated after recurrence. The remaining 11 cases had shown no recurrence to date.Conclusions:Nasal pleomorphic adenoma is rare in clinical practice, typically occurring in the nasal septum. The primary symptom is nasal obstruction. Diagnosis is primarily based on histopathology, and surgical resection is the primary treatment.

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.