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.

Alopecia areata （AA） is a common hair loss disorder， and the core pathogenesis is the internal gene-ration of turbid toxin caused by qi movement disorder in the liver， spleen， and kidney. Turbid toxin serves as both a pivotal etiological trigger and a pathological driver of disease exacerbation. Clinically， AA can be classified into four principal patterns， including liver constraint with spleen deficiency， internal accumulation of damp-heat， liver-kidney depletion， and qi-blood depletion. Therapeutic strategies prioritize clearing and resolving turbid toxin while regulating the qi movement of the liver， spleen， and kidney. Accordingly， different formulas were applied. Self-formulated Shugan Jianpi Huazhuo Formula （疏肝健脾化浊方） is suggested to drain dampness， resolve turbidity， and unblock qi movement. Self-formulated Sanjiao Fenxiao Jiedu Formula （三焦分消解毒方） can be used to clear heat， drain dampness and resolve toxin. Self-formulated Zishen Yanggan Toudu Decoction （滋肾养肝透毒汤） can clear and vent latent toxins， while Self-formulated Guiqi Shengfa Didu Formula （归芪生发涤毒方） is employed to tonify qi and blood， purge toxins， and regenerate vitality. By differentiating and treating AA based on the functional patterns of the liver， spleen， and kidney， this approach expands the application scope of the turbid toxin theory and provides valuable insights for treatment of AA.

Purpose/Significance Achieving automatic generation of medical imaging reports is important for reducing the workload of radiologists and promoting the standardization of clinical workflow.Method/Process Focusing on finding the chest report generation mod-els with open source code in recent years,the paper develops an automatic medical image report generation method based on the CDGPT2 model.Result/Conclusion The advantages of the model in report generation are still to be explored,the quality of reports generated after modifications to the decoder inputs of the model is not high.Future research could improve the performance of the model by using large datasets and incorporating more clinical information.

Tic disorders（TD）are common neurodevelopmental disorders whose pathogenesis is unknown.As a non-invasive examination，electroencephalography（EEG）is widely used in the diagnosis of TD.Due to the high temporal resolution of the EEG signal，when combined with standardized analysis techniques，it can not only be used to understand the physiological functions of the subject，but also to reflect the pathophysiological changes of the disease.Based on the data analysis technology of EEG，this paper reviews the progress of brain connectivity and brain network analysis，time-frequency analysis，and EEG complexity of TD，summarizes the shortcomings of existing research，and provides new ideas for further in-depth study of its pathophysiological mechanism.

Correctly identifying the treatment failure in chronic tic disorders can prevent the continued use of unnecessary treatments and facilitate timely escalation to experimental or even invasive treatment.However, there is still a lack of a standard definition for the treatment failure in chronic tic disorders.Led by Professor Davide Martino from the University of Calgary in Canada, several rounds of web-based Delphi surveys were conducted to formulate expert clinicians′ consensus-based definition for treatment failure in chronic tic disorders.This consensus defines the treatment failure in chronic tic disorders from 4 aspects of behavioral and pharmacological interventions: lack of efficacy, adherence, tolerability and behavioral disorders.In addition, it further defines the failure of behavioral intervention as a whole and pharmacological intervention as a whole.This article interprets the process and key contents of the consensus, and discusses the definition of refractory tic disorders to help clinicians improve their understanding of the treatment failure in chronic tic disorders.

Objective:To explore the influence of interpregnancy interval (IPI) on pregnancy complications in multiparas.Methods:This was a retrospective cohort study involving 7 669 singleton parturients who delivered at ≥28 gestational weeks in the Affiliated Hospital of Southwest Medical University between December 2015 and December 2020 and had given birth in the third trimester before. Clinical data were collected, including the baseline characteristics, pregnancy complications, gestational weeks at delivery, and neonatal birth weight. According to the IPI, these women were divided into five groups: <12 months ( n=350), 12-<24 months ( n=945), 24-<60 months ( n=2 544), 60-<120 months ( n=2 478), and ≥120 months ( n=1 352). Based on the recommendation of the World Health Organization, pregnant women with an IPI of 24-<60 months were the control group. A multivariate logistic model was used to adjust for confounders and calculate the risks of pregnancy complications, including gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy (HDP). The influences of maternal age and previous delivery mode on the associations between IPI and maternal complications were analyzed. Analysis of variance (ANOVA), Chi-square test, and Cochran-Mantel-Haenszel Chi-square test were used for statistical analysis. Results:Compared with the control group, the incidence of GDM and HDP increased in the 60-<120 months group ( OR=1.23, 95% CI: 1.01-1.48 and OR=1.47, 95% CI: 1.13-1.92) and ≥120 months group ( OR=1.37, 95% CI:1.07-1.78 and OR=1.92, 95% CI: 1.39-2.64); the risks of uterine rupture/postpartum hemorrhage and placental abruption increased in the <12 months group ( OR=1.54, 95% CI: 1.01-2.34) and 12-<24 months group ( OR=2.38 95% CI: 1.13-5.02), respectively. In the 60-<120 months group, the risk of GDM increased only in non-elderly women (adjusted OR=1.71, 95% CI: 1.36-2.14), so did the risks of GDM and HDP in the ≥120 months group (adjusted OR=3.11, 95% CI: 2.10-4.62 and adjusted OR=1.81, 95% CI: 1.12-2.91). Among women who had undergone a previous cesarean section, the risk of GDM increased in the ≥120 months group (adjusted OR=1.35, 95% CI: 1.00-1.81). In the 60-<120 months group and ≥120 months group, the risk of HDP increased in postpartum women (adjusted OR=1.79, 95% CI: 1.08-2.95 and adjusted OR=3.32, 95% CI: 1.91-5.77). Conclusion:IPI≥60 months is a risk factor for GDM and HDP, and the associations between IPI and maternal complications are influenced by maternal age.

Abstract In recent years, mental health problems such as anxiety and depression among adolescents in China have attracted attention from all walks of life. Given that adolescence is a transitional and critical period for individual development, mental health affect the developmental opportunities. Therefore, in the review, the effects of environment, psychosocial factors and behavioral patterns on depressive symptoms are analyzed by combining with the characteristics of physical and mental development among adolescents. It is found that early adolescence and even childhood should be the key period for the prevention and intervention of depression. In order to formulate effective interventions and prevention strategies, it is proposed that future research should combine real situation in China with active exploration of protective factors and early predictors of depression.

Objective:To understand the incubation periods of infections of Omicron variants BA.2 and BF.7 in two COVID-19 epidemics and related factors in Beijing and provide basic parameters for the establishment of 2019-nCoV dynamic transmission model.Methods:The COVID-19 cases with specific exposure time and onset time in the Omicron variant BA.2 infection epidemic in April 2022 and in the Omicron variant BF.7 infection epidemic in October 2022 in Beijing were included in the analysis. The rank-sum test was conducted to estimate the differences in the incubation period between two types of infections. The incubation period distribution of the Omicron variant infection was fitted by using Weibull, Gamma and lognormal distributions. Multivariate analysis of variance was conducted to assess the effects of age, sex, variant type and vaccination status on the incubation periods.Results:A total of 64 cases of variant BA.2 infection and 58 cases of variant BF.7 infection were included. The M( Q1,Q3) of the incubation period was 3.00 (3.00, 4.00) days for BA.2 infection and 3.00 (2.00, 3.25) days for BF.7 infection. The lognormal distribution was the best fit. Multivariate analysis of variance showed that there were some differences in the incubation periods between two types of infections of Omicron variants, and the incubation period of variant BF.7 infection was shorter than that of variant BA.2 infection. Conclusion:Omicron variant BF.7 infection had shorter incubation period compared with Omicron variant BA.2 infection.

【Objective】 To explore the mutation type, clinical characteristics, molecular genetics and the two-hit type of a patient with familial Von Hippel Lindau (VHL) syndrome. 【Methods】 The data of the patient were collected. DNA was extracted from the peripheral blood and renal cell carcinoma sample. The VHL gene germline mutation site was detected with high throughput sequencing next generation sequencing (NGS). The two-hit site was identified with UCSCXena database, methylation-specific PCR (MSP) and microsatellite stability detection. 【Results】 The mutation site of the embryo line was located in c.500G>A R167Q mutation. The patient had single nucleotide polymorphism, but no clear loss of heterozygosity, methylation or system mutation. 【Conclusion】 The germline mutation in exon 3 is the basis for the clinical features of this familial renal cell carcinoma proband. The identification of the two-hit site is key to the occurrence of the disease, which is significant for the diagnosis and treatment. The use of the databases can guide the screening of mutations and methylation sites in familial renal cell carcinoma.

Breast cancer is the most commonly diagnosed cancer and the main cause of cancer deaths among women worldwide. HER2 positive breast cancer accounts for 15% of all breast cancer. This subtype of breast cancer is highly invasive and has a very poor prognosis. With the development of anti - HER2 targeted therapy, the prognosis of these patients has been improved. However, some patients have poor response to the anti-HER2 therapy. Therefore, it is necessary to select biomarkers that can predict the therapeutic effect for improving the efficacy of these patients. This article describes the research progress of HER2 positive biomarkers for breast cancer, focusing on biomarkers related to the efficacy of targeted therapy, in order to provide some reference for future clinical optimization of targeted therapy.