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.

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.

This article reports a successful case of preimplantation genetic testing (PGT) in a patient with autosomal dominant polycystic kidney disease type 2 (PKD2) combined with Robertsonian translocation. The patient carried a heterozygous frameshift mutation ( PKD2 c.428del, p.Gly143Alafs*90) and a Robertsonian translocation between chromosomes 14 and 15. Through combined PGT for monogenic disorders, structural rearrangements and aneuploidy screening, one euploid blastocyst free of the PKD2 mutation was selected from six embryos for transfer, resulting in the successful delivery of a healthy female infant. Follow-up until June 2025 confirmed normal developmental milestones. This case demonstrates that PGT can effectively mitigate dual genetic risks (monogenic disease and chromosomal abnormality), providing critical clinical insights for optimizing reproductive outcomes in patients with complex genetic backgrounds.

Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.

Objective:To investigate the association between adiponectin-related copy number variation (CNV) region (CNVR) and gestational diabetes mellitus (GDM).Methods:Pregnant women who had prenatal screening in Anqing Municipal Hospital, Anhui Province, from February 2018 to December 2020 were surveyed for baseline information collection, and blood samples were collected. The outcome information was obtained by post pregnancy follow-up. Latex-enhanced immunoturbidimetry and ASA-CHIA chip were used to detect serum adiponectin levels and CNV of pregnant women, respectively. After genotyping, CNV data were processed with software PennCNV 1.0.5 following standard quality control procedure. CNVR were identified and integrated by using software R 4.3.3. Then the associations between CNVR and adiponectin was evaluated, and gene annotation and over-representation analysis were conducted. The log-binomial regression model was used to adjust relevant covariates and analyze the association between adiponectin-related CNVR and GDM.Results:The detection rate of GDM was 9.54% (176/1 845) in the pregnant women. The genotyping information of 1 840 people (99.73%) passed quality evaluation. A total of 33 878 CNVs were identified, and 1 449 CNVRs were obtained after integration. After the false discovery rate method correction, CNVR_132 (CHR2: 47611743-47635062), CNVR_254 (CHR3: 10182703-10183872), CNVR_691 (CHR7: 150637053-150834539) and CNVR_1101 (CHR14: 104248431-104830620) were correlated with adiponectin levels (all P<0.05). Over- representation analysis showed that the molecular function of ribonucleotide binding [Gene Ontology (GO): 0032553] was significantly enriched based on the GO database. The log-binomial regression model, adjusting age, pre-pregnancy BMI, history of miscarriage, smoking history, and family history of diabetes, indicated that CNVR_132 (CHR2: 47611743-47635062) and CNVR_1101 (CHR14: 104248431-104830620) were not statistically associated with the risk for GDM (both P>0.05). However, CNVR_254 (CHR3: 10182703-10183872, a RR=1.83, 95% CI: 1.15-2.91) and CNVR_691 (CHR7: 150637053-150834539, a RR=1.73, 95% CI: 1.23-2.43) might be associated with an increased risk for GDM (all P<0.05). Conclusion:Adiponectin-related CNVR_254 (CHR3: 10182703-10183872) and CNVR_691 (CHR7: 150637053-150834539) might be risk factors for the incidence of GDM.

Objective:To investigate the clinical and molecular characteristics of monogenic diabetes in children at a single-center in Henan Province.Methods:Clinical data from children diagnosed with monogenic diabetes at the Department of Endocrinology and Genetic Metabolism, Henan Children’s Hospital, between January 2016 and August 2023 were analyzed, alongside molecular genetic testing.Results:Of 84 children suspected of monogenic diabetes, 44 were genetically diagnosed with a 52.4% positive rate. Diagnosis included neonatal diabetes mellitus(NDM, 14 cases), maturity-onset diabetes of the young(MODY, 22 cases), and other diabetes-related genetic syndromes(8 cases). NDM patients had a younger age at onset, higher blood glucose levels at admission, and a greater incidence of being small for gestational age and diabetic ketoacidosis compared to MODY patients( P<0.001). Three novel heterozygous mutations were identified in IGF1R and GCK genes, namely IGF1R c. 2650G>T, GCK c. 572G>C, GCK c. 766G>T. Conclusions:The high molecular diagnostic rate based on clinical phenotype, birth history, and family history enhances the diagnosis, management, genetic counseling, and prognosis of monogenic diabetes in children.

Objective:To investigate the clinical characteristics of urea cycle disorder (UCD), the efficacy and safety of glyceryl phenylbutyrate (GPB) therapy in pediatric patients with UCD.Methods:This study was a retrospective, single-arm, multicenter clinical study. The clinical data of 20 pediatric patients with UCD who received GPB treatment at 9 hospitals nationwide between December 2021 and August 2024 were collected. The clinical manifestations, laboratory results, and molecular genetic characteristics were analyzed, ammonia levels and other laboratory results were evaluated pre-post GPB therapy by paired t-tests or Wilcoxon tests. Results:Among the 20 pediatric patients with UCD, there were 8 males and 12 females, and the onset age was 2.8 (1.4, 5.7) years. The ammonia levels were 174 (125, 342) μmol/L at first onset. The symptoms included vomiting in 6 cases, drowsiness in 5 cases, epilepsy in 5 cases, developmental delay in 5 cases, psychiatric and behavioral abnormalities in 3 cases, and lethargy in 1 case, and 18 cases exhibited abnormal liver function. Twenty cases included 6 UCD subtypes, with 11 cases being ornithine transcarbamylase deficiency. A total of 27 variants were identified, 11 (41%) of which were novel. The age of patients who began GPB therapy was 4.0 (1.5, 6.6) years. Ten cases stopped GPB after 4.2 (3.4, 5.3) months, with 4 patients undergoing liver transplantation and 6 discontinuing for financial reasons. The remaining ten patients continued GPB therapy for 11.6 (8.6, 14.0) months. The duration of GPB treatment was 6.0 (4.2, 12.3) months, at the final visit, the levels of ammonia, platelets and aspartate aminotransferase were lower compared to those of pre-treatment (all P<0.05). The serum albumin level was higher than that of pre-treatment ( P=0.016). Two patients suffered only one episode of acute hyperammonaemia, with ammonia levels of 232 and 141 μmol/L, respectively. Nine cases experienced adverse effects potentially related to GPB, decreased appetite in 6 cases, vomiting in 3 cases, abnormal skin oil odor in 2 cases, somnolence, fatigue and diarrhea each in 1 case, with symptoms improved within 6 (3, 10) days. Conclusions:UCD primarily manifests with neurological and gastrointestinal symptoms, and early diagnosis of UCD could be achieved through the analysis of ammonia. GPB may effectively reduce ammonia levels in UCD pediatric patients, with favorable safety and tolerability.

Abnormal deployment of the airbag during a frontal car collision can cause injuries to the upper extremity of drivers with non-standard driving postures.Finite element simulation offers an effective approach for evaluating such injury risks.In this study,a biomechanical finite element model of the upper limb of the 95th percentile human body with detailed anatomical structures was developed.The validity of the upper extremity-airbag collision model was confirmed by reconstructing the cadaveric forearm and airbag impact experiments.Based on the validated model,the influence of factors such as airbag mass rate parameters,upper limb grip angle,and grip force on upper limb injuries in frontal collisions was investigated.The results indicate that variations in these three parameters have a significant influence on upper extremity injury,and these factors should be considered in the assessment of upper extremity injuries during car collision.

Objective To explore the molecular mechanisms of moxibustion in improving osteoporosis in rats using high-throughput micro-RNA(miRNA)sequencing analysis.Methods A total of 18 female Sprague-Dawley(SD)rats were randomly divided into an operation group(12 rats),which was subjected to ovariectomy to induce osteoporosis,and a sham operation control(SO)group(6 rats).The 12 osteoporosis model rats were randomly divided into a model(OVX)group and a moxibustion(MOX)group(6 rats per group),which were treated with once-daily moxibustion at the"Shenshu"(BL23)and"Guanyuan"(CV4)acupoints for 20 minutes each time,for 12 weeks.Micro-computed tomography(CT)scans of the rat femur were taken to analyze the trabecular bone thickness(Tb.Th),trabecular separation(Tb.Sp),and bone volume total volume(BV/TV)ratio of the trabecular bone.Hematoxylin and eosin staining was used for morphological observation of tibial tissues.Serum alkaline phosphatase(ALP)and osteocalcin(OCN)levels were measured using ELISA.Three randomly selected rats from each group were used for miRNA high-throughput sequencing to screen for differentially expressed miRNAs,which were subjected to functional enrichment analysis and target gene prediction.Results Micro-CT images showed that,compared with the OVX group,the MOX group had superior bone density,significant increases in the Tb.Th and BV/TV score,and a significant decrease in Tb.Sp(P＜0.05).ELISA indicated that,compared with the OVX group,the MOX group showed a significant decrease in serum ALP activity and a significant increase in serum OCN content(P＜0.05).The miRNA sequencing result showed that 34 miRNAs were commonly expressed between the OVX group and the intervention group,and 15 miRNAs were commonly expressed between the OVX group and the intervention group.KEGG pathway enrichment analysis showed that the differentially expressed genes were mainly enriched in signaling pathways involving MAPK,Ras,FoxO,TNF,and cancer-related microRNAs.For the top five most differentially expressed microRNAs,namely miR-153-5p,miR-201-5p,miR-449c-5p,miR-451-3p,and miR-153-3p,target gene predictions yielded 10 major targets:Ebf2,Rtn4,Fbxl3,Naa15,Vamp2,Daam1,Akap6,Camta1,Ptprz1,and Lamp1.Conclusions Acupuncture slowed the progression of osteoporosis,improved bone microstructure,and balanced bone metabolism in rats.This therapeutic effect may be achieved through regulation of the expression of microRNAs and their target genes;however,the underlying mechanism requires further exploration.