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 develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective: To study the effect of metformin sensitizing pancreatic cancer cells with radiotherapy, with a focus on elucidating the underlying mechanisms of radiotherapy resistance. In particular, the role of the PERK/P-eIF2/ATF4 signaling pathway in mediating these effects was preliminarily explored. Methods : Pancreatic cancer cell lines(PANC-1 and PANC-2) were categorized into control, radiotherapy, and drug treatment groups. Following the respective treatments, cell proliferation inhibition was assessed using the CCK-8 assay, colony formation assays, and cell death staining. Senescence was quantified by β-galactosidase(SA-β-Gal) staining. The expression of cell cycle regulators(P21, P16, γ-H2AX), apoptosis markers(Bax, Bcl-2, Cleaved caspase-3), and pathway-related proteins(PERK, P-eIF2, ATF4) was evaluated by Western blot and immunofluorescence. To further investigate the role of the PERK/P-eIF2/ATF4 axis in metformin-mediated modulation of pancreatic cancer cell senescence and radiosensitization, selective inhibitors(GSK2606414) and agonists(MK-28) of PERK were employed. Results : Radiotherapy markedly upregulated senescence-associated markers(P21, P16, γ-H2AX, and β-galactosidase activity) in pancreatic cancer cells. Senescent cells exhibited enhanced proliferative activity and increased tumor volume both in vitro and in vivo. Metformin mitigated radiotherapy-induced senescence by reducing the expression of senescence markers and significantly suppressing the clonogenic and proliferative capacity of treated cells. Mechanistically, radiotherapy activated the PERK signaling pathway, leading to increased expression of PERK, P-eIF2, and ATF4, thereby driving cellular senescence. Pharmacological inhibition of PERK reduced β-galactosidase activity, while PERK activation further promoted the expression of senescence-associated proteins—an effect that was reversed by metformin. Conclusion Metformin inhibits the activation of the PERK/P-eIF2/ATF4 signaling pathway in pancreatic cancer cells following radiotherapy, thereby delaying cellular senescence and reducing the associated radiotherapy resistance of senescent cells. This modulation contributes to the sensitization of pancreatic cancer cells to radiotherapy.

Guanylate binding protein subunit β-2(GNB2)is an important part of G protein family.In the process of cell signal transduction,GNB2 plays a structural support and regulatory role in G protein complex,ensuring the correct transmission and amplification of signals,interacting with receptors and regulating vari-ous effector enzymes and ion channels,and mediating a variety of physiological functions.Therefore,GNB2 plays a key role in cell signaling,and participates in the regulation of cell proliferation,differentiation and mi-gration and other biological processes.This article reviews the research progress of GNB2,focusing on its structure,function,regulatory mechanism,disease correlation and importance in tumor research.

Objective:To analyze the disease burden of colorectal cancer (CRC) among young and middle-aged people in China from 1990 to 2021, and to explore the influence of age, period and cohort on the incidence and mortality of CRC in young and middle-aged people of China.Methods:Data on CRC in patients aged 40-59 years in China from 1990 to 2021 were extracted from the Global Burden of Disease Study 2021 (GBD 2021) database. Statistics such as incidence rate, mortality rate, disability-adjusted life years (DALY), and their corresponding age-standardized rates were calculated to analyze the CRC incidence and mortality in different age and sex groups of young and middle-aged Chinese young people from 1990 to 2021. The Joinpoint regression model was used to analyze the CRC incidence, the mortality and the DALY rate, as well as to calculate the annual percentage change (APC) and the average annual percentage change (AAPC). The effects of three independent factors, namely age, period and cohort, on the incidence and mortality of CRC in young and middle-aged people of China were analyzed and evaluated through the age-period-cohort model.Results:From 1990 to 2021, there was a remarkable upward trend in the incidence, mortality, and DALY of CRC among Chinese young and middle-aged adults. In 2021, the number of incidence cases of CRC among young and middle-aged people in China reached 181 000, and the number of deaths reached 57 900, which were 236.80% and 75.48% higher than those in 1990 (53 800 and 33 000, respectively). During the same period, DALY increased by 62.59%, with the 55-59 age group having the largest increase at 83.35%. From 1990 to 2021, the age-standardized incidence rate (ASIR) increased by 49.04%, rising from 25.51/100 000 to 38.02/100 000, and the age-standardized mortality rate (ASMR) declined by 28.75%, decreasing from 17.01/100 000 to 12.12/100 000, respectively. The increase in ASIR was the greatest among the 40-44 age group, reaching 57.31%, while the decline in ASMR was the most significant among the 50-54 age group, amounting to 30.18%. However, the DALY rate declined by 26.66%, from 673.52/100 000 to 493.94/100 000. The decline in DALY was the greatest among the 50-54 age group, reaching 28.26%. Joinpoint regression model analysis showed that, from 1990 to 2021, the incidence of CRC in Chinese young and middle-aged adults rose on average by 1.32% annually, and the increase was higher in men (1.87%) than that in women (0.36%). The mortality rate showed a downward trend, with an average annual decline of 1.10%, with a higher decline in women than in men (-2.14% vs. -0.50%). The DALY rate showed a downward trend, with an average annual decline of 1.00%, and more decline in women than in men (-2.06% vs. -0.41%). All of these trends were statistically significant (all P<0.001). The age-period-cohort model analysis showed that, the net drift of CRC incidence was 1.21% (1.02%-1.41%) per year among Chinese young and middle-aged adults between 1990 and 2021, while the net drift in mortality was -1.40% (-1.59%--1.21%) per year. The impact of age on CRC incidence and mortality intensified with advancing age. Incidence attributable to age rose from 12.66% (11.90%- 13.46%) in the 40-44 age group to 56.68% (54.37%-59.08%) in the 55-59 age group. Similarly, mortality attributable to age increased from 6.47% (6.12%-6.85%) in the 40-44 age group to 25.74% (24.58%-26.96%) in the 55-59 age group. In all age groups, the role of CRC incidence and mortality attributable to age was higher in men than in women. Period effects on the RR value of CRC incidence showed a declining trend followed by an upward trend, with the highest risk during 2015-2019 ( RR=1.36, 95% CI: 1.28-1.43), using 2000-2004 as the reference. For mortality, period effects exhibited a declining trend, with the highest risk during 1990-1994 ( RR=1.23, 95% CI: 1.15-1.32), using 2000-2004 as the reference. Cohort effects indicated that later birth cohorts had higher incidence risks, with the highest incidence observed in the 1973-1977 birth cohort ( RR=1.30, 95% CI: 1.16-1.45), using the 1953-1957 birth cohort as the reference. Conversely, later birth cohorts had lower mortality risks, with the lowest mortality in the 1973-1977 birth cohort ( RR=0.78, 95% CI: 0.69-0.88), using the 1953-1957 birth cohort as the reference. Conclusions:From 1990 to 2021, the changes in the disease burden of CRC among young and middle-aged people in China are manifested as an increase in standardized incidence rate and a decrease in standardized mortality rate. Meanwhile, there are gender differences in the trend of temporal changes. Age, period and cohort all have a significant impact on the incidence and mortality trends of colorectal cancer in young and middle-aged people. Research on the etiology of CRC should be strengthened, and targeted prevention and control strategies should be formulated.

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.

Tic disorders are common neurodevelopmental disorders in children, characterized by repetitive movements and/or vocal tics.In recent years, the incidence of tic disorders has been increasing, whose mechanism may be partially related to neurogenetic, neurobiochemical, neuroimmune, social and environmental factors.At present, studies have shown a possible association between vitamin D and tic disorders and that vitamin D supplementation may be used as an auxiliary therapy.In this article, the correlation between vitamin D and tic disorders is reviewed.The results provide new ideas for the treatment of tic disorders.

Tic disorders are common neurodevelopmental disorders in children, characterized by repetitive movements and/or vocal tics.In recent years, the incidence of tic disorders has been increasing, whose mechanism may be partially related to neurogenetic, neurobiochemical, neuroimmune, social and environmental factors.At present, studies have shown a possible association between vitamin D and tic disorders and that vitamin D supplementation may be used as an auxiliary therapy.In this article, the correlation between vitamin D and tic disorders is reviewed.The results provide new ideas for the treatment of tic disorders.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To evaluate the quality of animal experiment reports on the treatment of Osteoporosis(OP)by acupuncture therapy,furtherly provide reference for future research.Methods Literature search was conducted in six databases,including China National Knowledge Infrastructure(CNKI),Wanfang,VIP,PubMed,Web of Science and Embase from inception to December 31,2022.After removing duplicates,title and abstract preliminary screening and full text re-screening,the included literatures were involved for quality evaluation according to ARRIVE guidelines and SYRCLE risk assessment tool.Results 643 literatures were initially examined and 136 were included for quality assessment.The evaluation results showed that the reports quality is relatively balanced on the whole,and score of the articles showed a slow fluctuation and upward trend.The current existing problems focus on the settings of the inclusion and exclusion standard,implementation of randomization and blindness,lack of ethical statements and animal care,and unclear report of experiment details,etc.The selection bias,implementation bias and reporting bias are negative factors which drag down essay ratings.Conclusion Current animal experiments on acupuncture therapy treatment of OP still existed some methodological problems including imrigorous design,opaque implementation and non-standard reporting.It is suggested that future researches should improve the design,implementation and reporting of experiments with the help of SYRCLE risk assessment tool and ARRIVE guideline,so as to improve the rigor and credibility of animal experimental results and promote the clinical promotion of acupuncture treatment for OP.