Objective: To explore the regulatory role of paternal involvement in parenting in the association between parental adverse childhood experiences (ACEs) and autism like behavioral problems in children aged 3-6, so as to provide a basis for intervention strategies targeting the intergenerational health effects of ACEs. Methods: A longitudinal study design was used to select 1 780 children aged 3-6 from 12 kindergartens in Wuhu City, Anhui Province in June 2021 as the subjects of the baseline survey by convenient sampling, and the follow up surveys were conducted once every six months, for a total of two follow ups. Pearson correlation analysis was used to examine the association of parental ACEs and paternal involvement in parenting with autism like behavioral problems in children. A binary Logistic regression model was used to explore the association between parental ACEs and autism like behavioral problems in children. Based on stratification, the potential role of paternal involvement in the association between parental ACEs and autism like behavioral problems in children aged 3-6 was explored. Results: Autism like behavioral problem score in children aged 3-6 was positively correlated with the total score ACEs of parents as well as scores of emotional abuse, physical abuse, sexual abuse, emotional neglect, physical neglect, community violence, peer bullying, and family dysfunction( r =0.18,0.16,0.18,0.14,0.06,0.05,0.12,0.18,0.09,all P <0.05). Autism like behavioral problem score in children was negatively correlated with score of paternal involvement in parenting( r =-0.16, P <0.01). After adjusting for covariates such as gender, age, and birth weight of children, binary Logistic regression analysis showed that children whose parents had experiences of childhood emotional abuse, physical abuse, sexual abuse, community violence, peer bullying, or family dysfunction had higher risks of developing autism like behavioral problems than those in the non exposed group ( OR =3.29, 3.28, 6.71, 2.34, 4.08, 2.02, all P <0.01). In the group with low paternal involvement in parenting, all dimensions of parental ACEs(emotional abuse, physical abuse, sexual abuse, community violence, peer bullying, and family dysfunction) were significantly associated with an increased risk of autism like behavioral problems in children ( OR =1.84-8.34, all P <0.05). In contrast, in the group with high paternal involvement in parenting, no statistically significant associations were found between any dimension of parental ACEs and children s autism like behavioral problems ( OR =0.57-1.82, all P >0.05). Conclusion A high level of paternal involvement in parenting can reduce the adverse effects of parental ACEs on the occurrence of autism like behaviors in preschool children.

Exportin 1 (XPO1) is aberrantly overexpressed in various malignant tumors and can lead to the loss of anti-tumor effects of important tumor suppressor proteins such as p53, RB1, and FOXO by mediating their nuclear export. Although XPO1 inhibitor Selinexor has entered clinical application, its single-agent anti-tumor activity remains suboptimal, which is closely related to the compensatory activation of multiple signaling pathways in response to XPO1 inhibition. Focusing on the core regulatory role of XPO1 in tumor cells, this article systematically summarizes the current landscape of combination therapies involving XPO1 inhibitors and various targeted agents, including inhibitors of CDK4/6, FLT3, BET, ATR, and BCL2/MDM2, aiming to provide some reference for the development of XPO1-centered combination therapy strategies.

The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Objective To investigate the effect of bone metabolic markers on sarcopenia in elderly patients with type 2 diabetes mellitus (T2DM). Methods A total of 412 patients with T2DM in the department of endocrinology of Nanjing Central Hospital from May 2020 to June 2025 were selected as the research subjects. According to Asian Working Group for Sarcopenia (AWGS) in 2019, these patients were evaluated for skeletal muscle mass index (ASMI), muscle strength, and muscle function, and were divided into a sarcopenia group (84 cases) and a non-sarcopenia group (328 cases). The glucolipid metabolic indexes were detected in both groups of patients, and the bone metabolic markers were evaluated, including procollagen type 1 N-terminal peptide (P1NP), beta-C-terminal telopeptide of type 1 collagen (β-CTX), and 25-hydroxy vitamin D [25-(OH)D]. The factors influencing the occurrence of sarcopenia in T2DM patients were analyzed by logistic regression analysis, and the diagnostic values of bone metabolic markers on sarcopenia in patients with T2DM were assessed by ROC curve. Results The levels of P1NP and 25-(OH)D were lower, while β-CTX level was higher in the sarcopenia group compared to the non-sarcopenia group, with statistical differences (P<0.05). After logistic correlation analysis, it was found that P1NP, β-CTX and 25-(OH)D were all influencing factors for the occurrence of sarcopenia in T2DM patients. ROC curve analysis suggested that combined detection of PINP, β-CTX, and 25-(OH)D had higher diagnostic value, with an area under the curve up to 0.805. Conclusion The abnormal expression of bone metabolic markers is associated with the increased risk of sarcopenia in patients with T2DM. The detection of serum bone metabolic markers expression level is of certain significance for the assessment of diabetes-related sarcopenia.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveTo develop a community-family management model for older adults with mild cognitive impairment (MCI) and to formulate detailed application specifications, and to fully leverage the initiative of communities and families under limited resource conditions, for achieving community-based early detection and early intervention for older adults with MCI. MethodsA systematic literature review was conducted to identify pertinent publications. Corpus-based research methodologies were employed to extract, refine, integrate and synthesize management elements, thereby establishing the specific content and service processes for each stage of the management model. Utilizing the 5W2H analytical framework, essential elements such as management stakeholders, target populations, content and methods for each stage were delineated. The model and its application guidelines were finalized through expert consultation and demonstration. ResultsAn expert evaluation of the management model yielded mean scores of 4.84, 4.32 and 4.84 for acceptability, feasibility and systematicity, respectively. By integrating the identified core elements with expert ratings and feedback, the final iteration of the community-family management model for older adults with MCI was formulated. This model comprised of five stages: screening and identification, comprehensive assessment, intervention planning, monitoring and referral pathways to ensure implementation, and enhanced support for communities, family members and caregivers. Additionally, it included 18 specific application guidelines. ConclusionThe proposed management model may theoretically help delay cognitive decline, improve cognitive function and potentially promote reversal from MCI to normal cognition. It may also enhance the awareness and coping capacity of older adults and their families, strengthen community healthcare professionals' ability to early identify and manage MCI.