ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

Objective To construct prostate cancer(PCa)prediction models based on machine learning algorithms, so as to improve the accuracy of early diagnosis of PCa. Methods A retrospective analysis was performed on the clinical data of 504 patients who underwent prostate biopsy at our hospital during Jan. 2020 and Nov. 2024. Patients' age, body mass index(BMI), history of hypertension, diabetes and smoking, total prostate-specific antigen(tPSA), free prostate-specific antigen(fPSA), f/tPSA, prostate volume(PV), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio(NLR), Prostate Imaging Reporting and Data System(PI-RADS)score, digital rectal examination(DRE)results, and pathological findings were collected. The patients were divided into the training and testing sets at a ratio of 7:3. Ten early diagnosis prediction models of PCa were constructed using 10 supervised machine learning algorithms. Model performance was evaluated and validated using metrics including area under the receiver operating characteristic curve(AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis(DCA). SHAP analysis was used to interpret the models, and to clarify the importance of each feature and the basis for model decisions. Results All models showed good predictive value, with the gradient boosting machine(GBM)model performing the best(AUC=0.905, accuracy=84.1%, sensitivity=90.2%, specificity=80.0%). Calibration curves indicated good calibration and fitting of the GBM model, while DCA demonstrated favorable clinical net benefits. SHAP analysis identified the most significant features affecting PCa occurrence in descending order:tPSA, f/tPSA, PIRADS score, PV, age, and DRE. Conclusion The GBM model exhibits the optimal performance among the 10 models. The importance of features for predicting the occurrence of PCa, from the highest to the lowest, is as follows:tPSA, f/tPSA, PIRADS score, PV, age, and DRE.

Clinical data of a patient with alveolar soft part sarcoma (ASPS) treated at Zhongshan Hospital, Fudan University were retrospectively analyzed. The patient was initially diagnosed with abdominal ASPS with multiple lung metastases. After 6 weeks of treatment with nivolumab and ipilimumab, the patient achieved stable disease (SD). In the 7th week, the treatment was changed to a combination of nivolumab (30 mg, d1, q3w), anlotinib (8 mg, d1-14, q3w) and ipilimumab (50 mg, d1, q6w). The patient remained SD at the 12th week. The patient then underwent iliac artery embolization and intensity-modulated radiation therapy for the lesion in the psoas major muscle, while continuing the combination treatment. By the 24th week, the evaluation showed partial remission (PR) of both primary tumor and lung metastases. The patient experienced mild adverse reactions during treatment.

Xiaoyao Powder has pharmacological action such as neuroprotection, anti-tumor, anti-depression, liver protection, regulation of intestinal flora, anti-fatigue and so on. Based on the five aspects of quality transfer and traceability, component measurability, component effectiveness, component specificity and compound compatibility environment, the quality marker (Q-marker) of Xiaoyao Powder was predicted and analyzed. It was considered that paeoniflorin, atractylenolide Ⅰ, atractylenolid Ⅱ, atractylenolide Ⅲ, liquiritin,glycyrrhizic acid, Saikosaponin A, Saikosaponin D, ferulic acid, Z-ligustilide, pachymic acid, Menthol,6-gingerol 8-gingerol and 10-gingerol have strong measurability and can be used as Q-marker of Xiaoyao Powder.

Chronic fatigue syndrome(CFS)has emerged as a debilitating condition significantly impacting both physical health and psychological well-being,imposing substantial burdens on society and families.This article systematically summarizes Professor Chen Xinghua's clinical experience in acupuncture treatment for CFS,illustrated with representative case studies.Professor Chen postulates that the disease primarily results from visceral deficiency,with insufficiency of the"central earth"(spleen-stomach system)as its core pathogenesis,leading to concurrent impairment of both body constitution and mental state.Based on the"central earth"theory,he proposes the therapeutic principle of"prioritizing central earth while concurrently regulating the five zang-organs"to achieve comprehensive body-spirit regulation.In clinical practice,his treatment protocol combines acupuncture with traditional external therapies,emphasizing the application of Zusanli(ST36)and Neiguan(PC6)to reinforce earth and regulate the middle jiao,complemented by scalp acupuncture to nourish the spirit,benefit the heart,and regulate mental state to treat physical symptoms.The integrated acupuncture approach synergistically achieves fatigue relief and tonic effects,while external therapies serve to consolidate the fundamental treatment.

BACKGROUND: Skin cancer is a common skin disease whose incidence and mortality rates have been showing yearly increases. In this report, we update the most recent data on skin cancer as obtained from GLOBOCAN 2022. METHODS: The incidence and mortality rates of skin cancer (melanoma of skin and non-melanoma skin cancer) in GLOBOCAN 2022 were reviewed. These data were analyzed and the characteristics of incidence and mortality across five continents and top five countries and regions in each continent are presented. In addition, correlations between Human Development Index (HDI) and age-standardized incidence and mortality rates of these two skin cancers are described. RESULTS: The GLOBOCAN 2022 data indicated that melanoma was the 17th most common cancer. An estimated 331,722 people were diagnosed with melanoma globally and approximately 58,667 died from this disease. For non-melanoma skin cancer, it ranks as the 5th most common cancer, and an estimated 1,234,533 people were diagnosed with non-melanoma skin cancer globally and approximately 69,416 died from this disease. The incidence of skin cancer varies across geographic regions and countries, with a predominance observed in Oceania, North America, and Europe. Australia was ranked first in terms of incidence, while incidence rates in Africa and Asia were very low. Despite these regional differences in incidence, there was little geographic variation in mortality rates. Currently, the number of deaths from non-melanoma skin cancer exceeds that of melanoma of skin. HDI was positively associated with the incidence of both types of skin cancers, with a positive correlation obtained between HDI and mortality from melanoma of skin and a negative correlation between HDI and mortality from non-melanoma skin cancer. CONCLUSIONS Skin cancer remains a major disease burden worldwide. Substantial variations are observed across countries and regions. Further research on skin cancer will be required to provide a rationale for more effective preventions and treatments of this condition.
Humans ; Skin Neoplasms/mortality* ; Incidence ; Melanoma/mortality* ; Prevalence ; Global Health ; Male ; Female

BackgroundCurrently， the problem of depressed mood in college students is becoming more prominent. The experience of childhood maltreatment is a significant contributor to depression among college students. Although the association between the two has been confirmed， the specific psychosocial mechanisms underlying how childhood maltreatment affects college students' mental health remain insufficiently evidenced. ObjectiveTo explore the mediating role of emotion regulation difficulties in the relationship between childhood maltreatment and depression among college students， and to investigate the moderated effects of psychological resilience and family socioeconomic status， aiming to provide references for improving depressive symptoms in college students. MethodsOn 14 March 2024， a cluster sampling method was employed to recruit 751 college students from a university in Heilongjiang Province. Participants were assessed with Childhood Trauma Questionnaire （CTQ）， Difficulties in Emotion Regulation Scale （DERS）， Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， 10-item Connor-Davidson Resilience Scale （CD-RISC-10） and Family Socioeconomic Status Questionnaire. Pearson correlation analysis was adopted to examine the correlation between the scores of scales. Model 4 and model 7 in Process 4.2 were used to test the mediating effects of emotional regulation difficulties and the moderated effects of psychological resilience and family socioeconomic status. Results① A total of 712 （94.81%） valid questionnaires were collected. ② College students' CTQ score was positively correlated with DERS score and PHQ-9 score （r=0.296， 0.507， P<0.01）， and negatively correlated with CD-RISC-10 score and Family Socioeconomic Status Questionnaire score （r=-0.148， -0.229， P<0.01）. ③ The indirect effect value of difficulties in emotion regulation on the relationship between childhood maltreatment and depression was 0.091 （95% CI： 0.018~0.046）， accounting for 17.95% of the total effect. ④ The first half of the mediation model "childhood maltreatment → difficulties in emotion regulation → depression" （childhood maltreatment → difficulties in emotion regulation） was moderated by psychological resilience （β=-0.030， t=-6.147， 95% CI： -0.040~-0.020） and family socioeconomic status （β=-0.051， t=-3.929， 95% CI： -0.077~-0.026）. ConclusionChildhood maltreatment exerts both a direct effect on college students' depression and an indirect effect through emotion regulation difficulties. The childhood maltreatment → emotion regulation difficulties pathway in this mediation model is moderated by psychological resilience and family socioeconomic status. ［Funded by Qiqihar Medical University Graduate Student Innovation Fund Project （number， QYYCX2023-48）； Special Research Fund Project for Young Doctors of Qiqihar Academy of Medical Sciences （number， QMSI2021B-08）］

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.