Objective: To analyze the characteristics of latent classes of dietary patterns among children and adolescents in Qinghai Province, and their association with overweight and obesity, so as to provide a scientific basis for conducting targeted nutritional interventions and weight management for the local population. Methods: From May to October 2025, a multi stage stratified cluster random sampling method was employed to recruit 1 611 children and adolescents aged 6-17 years from Chengdong District of Xining City and Gonghe County of Hainan Tibetan Autonomous Prefecture in Qinghai Province. Questionnaire survey was conducted using a questionnaire developed by the Chinese Center for Disease Control and Prevention, and physical examination was conducted by using unified measurement standards. Latent class analysis (LCA) was performed on the food intake frequency of the participants. The χ 2 test and Logistic regression analysis were applied to explore the relationship between dietary patterns and overweight/obesity. Results: The detection rates of overweight, obesity, and overall overweight/obesity among the participants were 10.4%, 5.8%, and 16.2%, respectively. Latent class analysis identified three dietary patterns: modern mixed pattern ( n =399, 24.8%), low diversity pattern ( n =476, 29.5%), and traditional dietary pattern ( n =736, 45.7%). The potential category distribution differences of three dietary patterns among children and adolescents with and without overweight or obesity were statistically significant (non overweight and obesity: 22.5%, 29.1%, 48.4%; overweight and obesity: 36.4%, 31.8%, 31.8%) ( χ 2=30.69, P <0.01). Logistic regression showed that, compared with the traditional dietary pattern, the modern mixed pattern was associated with a higher risk of overweight and obesity ( OR =1.68, 95% CI =1.13-2.49); female sex was associated with a lower risk of overweight/obesity compared with male sex ( OR =0.66, 95% CI =0.50-0.86) (both P <0.05). Conclusions A positive association exists between the modern mixed dietary pattern and obesity in children and adolescents. It is recommended to reduce the intake of grains and unhealthy foods among overweight and obese children and adolescents, while increasing the consumption of legumes, fish, and shrimp, in order to cultivate healthy and rational dietary habits.

To investigate the effect of mesenchymal stromal cells (MSCs) on the fibrosis progression and endometrial regeneration in mouse models of endometrial injury, a mouse endometrial injury model was established by physical scraping combined with lipopolysaccharide (LPS) chemical induction, and the endometrium morphology and collagen deposition were evaluated by HE and Masson staining after unilateral administration of MSCs. RT-qPCR and immunofluorescence were used to detect the expression of epithelial markers Epcam and collagen I (Col1). Primary endometrial epithelial cells were isolated to assess p21 expression. Fibrosis-related genes and signaling pathways were validated using transcriptome sequencing and RT-qPCR. The experimental results showed that MSCs increased endometrial thickness and number of glands (P<0.05), up-regulated the expression of Epcam (P<0.01), while significantly reducing uterine collagen fiber deposition (P<0.05) and the expression of p21 in primary epithelial cells (P<0.0001). Transcriptomic analysis revealed that MSCs attenuated collagen deposition by modulating genes involved in the TGF-β signaling pathway. This study elucidates that MSCs facilitate uterine repair by reversing endometrial fibrosis and promoting epithelial regeneration, and their role is related to the regulation of the TGF-β/SMAD pathway and suppression of aberrant collagen accumulation.

This study aimed to investigate the mechanism of liquiritin (LQ) in the improvement of ventricular remodeling (VR) after acute myocardial infarction (AMI). Molecular docking was used to predict the binding affinity of liquiritin to thioredoxin-interacting protein (TXNIP). After 2 weeks of modeling, the rats were randomly divided into a model group, a low-dose liquiritin group (20 mg/kg LQ), and a high-dose liquiritin group (40 mg/kg LQ). Liquiritin was administered by gavage once a day, and the sham group and model group were given the same volume of 0.5% sodium carboxymethylcellulose (CMC-Na), with intervention of 4 consecutive weeks. Echocardiography was employed to detect the cardiac function, HE staining was used to observe cardiological changes, ELISA was used to detect the activity of serum creatine kinase-MB (CK-MB) activity, and the colorimetric method was adopted to detect serum malondialdehyde (MDA), total superoxide dismutase (T-SOD) and catalase (CAT) activities. RT-qPCR was used to detect the gene expressions of TXNIP, thioredoxin (TRX) and NACHT, LRR, and PYD domains-containing protein 3(NLRP3). Western blot was used to detect the protein expressions of TXNIP, TRX and NLRP3 in rat myocardial tissue. Molecular docking results showed that liquiritin had a good binding affinity to TNXIP target. After 20 and 40 mg/kg liquiritin intervention, the levels of ejection fraction (EF) and fractional shortening (FS) were significantly increased (P<0.01), and the levels of LVIDs, LVIDd, LVESV, and LVEDV were decreased (P<0.01). The myocardial structure was significantly improved, the cell arrangement tended to be regular, and the area of inflammatory cell infiltration and necrosis was reduced. Liquiritin significantly reduced the level of CK-MB (P<0.01), decreased the activity of MDA, and increased the activities of CAT and T-SOD (P<0.01). Liquiritin effectively inhibited the overexpression of TXNIP and NLRP3 genes and proteins, and enhanced the expression of TRX genes and proteins in the myocardial tissues of AMI rats. In conclusion, liquiritin has a regulatory effect on the TXNIP/TRX signaling pathway, inhibits the activation of the NLRP3 inflammasome, and thus improves ventricular remodeling after acute myocardial infarction.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

[摘 要] 目的：探讨m6A调控因子在宫颈癌预后评估、免疫微环境特征及治疗反应中的作用，构建基于m6A调控因子的预后风险模型，并分析其与免疫浸润、药物敏感性及细胞间通信的关系。方法：基于TCGA和GEO数据库中宫颈鳞癌及腺癌（CESC）转录组数据，分析22个m6A调控因子的表达与突变情况；采用LASSO‑Cox回归构建m6A相关预后风险模型，并依据风险评分将患者分为高、低风险组。利用CIBERSORT和ESTIMATE算法评估两组免疫细胞浸润及微环境特征；通过oncoPredict预测抗肿瘤药物敏感性。整合单细胞转录组数据（E-MTAB-12305），采用CellChat分析细胞间通信网络。结果：共鉴定出12个在宫颈癌中差异表达的m6A调控因子。高风险组患者总生存期缩短（P < 0.05），免疫评分降低，且对紫杉醇、氟尿嘧啶、多柔比星等抗肿瘤药物敏感性较差（IC50较高）。单细胞分析显示宫颈癌组织中细胞间通信网络发生改变，其中T细胞与内皮/上皮细胞间的相互作用可能主要由CCL5‑ACKR1和MIF‑（CD74 + CXCR4）配体‑受体介导。在临床样本中，对关键的m6A调控因子进行mRNA和蛋白水平的验证，结果显示部分调控因子在宫颈癌组织中的表达水平高于正常宫颈组织（P < 0.05）。结论：m6A调控因子特征可有效预测宫颈癌患者预后，其风险评分与肿瘤免疫浸润、药物敏感性及细胞通信密切相关，为宫颈癌的预后分层及个体化治疗提供了新的分子依据。

ObjectiveCandidate genes related to the regulation of litter size traits in English Springer Spaniel breeding bitches are explored, and the genetic mechanism underlying fertility in this breed is investigated, in order to provide reference molecular markers for genomic selection of high fecundity. MethodsWhole genome resequencing was performed on English Springer Spaniel breeding bitches that had given birth to at least 3 litters, and the bitches were divided into a high-litter-size group and a low-litter-size group according to the average litter size. Selection signal analysis was used to obtain the intersection of fixation index (F_st) and nucleotide diversity (Pi) signals as highly selected regions, and candidate genes were screened based on gene annotation and functional enrichment analysis. ResultsThe average litter size in the high-litter-size group (7.41±1.27) was significantly higher than that in the low-litter-size group (3.82±1.20) (P＜0.05), and the total number of live offspring in the high-litter-size group (7.06±1.10) was extremely significantly higher than that in the low-litter-size group (3.67±1.11) (P＜0.01). A total of 3 155 706 SNPs were detected in the two groups, 63.09% of which were located in intergenic regions, 33.96% in intronic regions, and 0.38％, 0.57％, and 0.09％ in exonic regions, 3' untranslated regions (UTRs), and 5'UTRs, respectively. Among the SNPs in exonic regions, 5 256 were nonsynonymous variants, accounting for 43.55%. A total of 1 752 differential genes were identified after annotation screening. Gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG) enrichment analyses identified 13 candidate genes that may affect reproductive performance and litter size traits, including WDR35, SMAD7, RPGR, RERGL, PGRMC2, LOC482182, GIMD1, COX7B2, COX16, BMPR2, BMP6, BICD1, and SLC9C1. Their functions mainly involve reproductive hormone regulation, embryonic development, GTPase activation, and oocyte apoptosis. ConclusionEnglish Springer Spaniel breeding bitches have undergone significant artificial selection for litter size traits. These 13 candidate genes play key roles in oocyte maturation and regulation during early pregnancy, providing a new molecular basis for elucidating the genetic mechanism of canine reproductive traits.

ObjectiveTo investigate the application value of a machine learning model based on preoperative clinical indicators in predicting the risk of hypoalbuminemia after partial hepatectomy. MethodsA retrospective analysis was performed for the clinical data of 700 patients who underwent partial hepatectomy in Nanfang Hospital， Southern Medical University， from January 2018 to January 2023， including demographic data， history of underlying diseases， tumor characteristics， preoperative laboratory markers， and perioperative indicators. The research data were divided into a training set and a test set at a ratio of 7∶3. The two-independent-samples t test was used for comparison of normally distributed continuous data between two groups； the two-independent-samples Wilcoxon rank-sum test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The least absolute shrinkage and selection operator （LASSO） regression analysis was used to identify characteristic variables， and 7 machine learning algorithms were used to construct predictive models， i.e.， logistic regression， decision tree， artificial neural network， K-nearest neighbors （KNN）， support vector machine， eXtreme gradient boosting， and light gradient boosting machine. The receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC） were used to assess the discriminatory ability of models， and the DeLong test was used for comparison of AUC. The calibration curve and decision curve analysis were used to assess the calibration and clinical practicability of models， and the models were compared with albumin-bilirubin （ALBI） score and Model for End-Stage Liver Disease （MELD） score. SHapley Additive exPlanations （SHAP） were used to interpret the key influencing factors for the optimal model. ResultsA total of 700 patients were finally enrolled， 283 （40.42%） developed hypoalbuminemia after surgery. The LASSO regression analysis identified 8 predictive factors of age， hepatitis B， fatty liver， blockade time， preoperative albumin （Alb）， time of operation， intraoperative blood loss， and preoperative aspartate aminotransferase （AST）. Among the 7 machine learning models， the KNN model showed the best overall predictive performance， with an AUC of 0.835 （95% confidence interval： 0.781 — 0.889）， a sensitivity of 84.0%， and a specificity of 65.5% in the test set. ALBI and MELD scores had an AUC of 0.652 and 0.524， respectively， and the KNN model had a better predictive performance than these two scores （Z=5.309 and 8.945， both P <0.001）. The calibration curve showed good consistency between predicted probabilities and actual incidence rates， and the decision curve analysis showed that the KNN model had net clinical benefit across a wide threshold range. The SHAP analysis showed that preoperative Alb， hepatitis B， time of operation， and age were the most significant influencing factors， and a synergistic effect was observed between hepatitis B and age/time of operation. ConclusionThe KNN machine learning model constructed based on preoperative clinical indicators can effectively predict the risk of hypoalbuminemia after partial hepatectomy and has a better performance than traditional scoring models， which provides a reference for the early identification of high-risk patients in clinical practice.

ObjectiveTo evaluate the effect of neuromuscular electrical stimulation (NMES) on pain, quadriceps strength and motor function of patients after total knee arthroplasty (TKA). MethodsThe databases of CNKI, Wanfang data, VIP, PubMed, Embase, Cochrane, Web of Science and Scopus were retrieved from inception to April, 2025. Randomized controlled trials (RCT) related to the intervention of NMES after TKA were collected. The quality of the included literature was evaluated using the Cochrane Risk of Bias Assessment Tool and the Physical Therapy Evidence Database (PEDro) scale. Meta-analysis was performed with RevMan 5.4. ResultsA total of twelve RCT were included, involving 772 subjects. The PEDro scale score ranged from four to seven. NMES improved postoperative quadriceps muscle strength (SMD = 0.61, 95%CI 0.34 to 0.88, P < 0.001) and knee flexion range of motion (SMD = 1.35, 95%CI 0.30 to 2.41, P = 0.010), and reduced the Timed Up and Go Test (TUGT) time (SMD = -0.86, 95%CI -1.45 to -0.26, P = 0.005). Compared with the control group, the intervention group achieved better outcomes in the 2-Minute Walk Test (2MWT) (SMD = 19.44, 95%CI 10.44 to 30.43, P < 0.001), 3-Minute Walk Test (SMD = 23.57, 95%CI 14.77 to 32.36, P < 0.001), 6-Minute Walk Test (SMD = 42.29, 95%CI 9.71 to 74.86, P = 0.010), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (SMD = -0.52, 95%CI -1.00 to -0.04, P = 0.040), as well as the Physical Component Summary (PCS) (SMD = 2.90, 95%CI 0.73 to 5.06, P = 0.009) and Mental Component Summary (MCS) (SMD = 2.84, 95%CI 1.40 to 4.28, P = 0.040) of the 36-Item Short Form Health Survey (SF-36). Subgroup analysis demonstrated that two to four weeks after surgery, NMES enhanced patients' quadriceps muscle strength (SMD = 0.90, 95%CI 0.58 to 1.21, P < 0.001), shortened TUGT time (SMD = -1.28, 95%CI -2.57 to -0.02, P < 0.05) and increased walking distance in 2MWT (SMD = 20.43, 95%CI 10.44 to 30.43, P < 0.001). For patients followed up for two to three months, NMES yielded improvements in WOMAC scores (SMD = -0.44, 95%CI -0.79 to -0.09, P = 0.010) and SF-36 MCS scores (SMD = 4.17, 95%CI 2.43 to 5.91, P < 0.001). ConclusionNMES can significantly improve the quadriceps strength and walking ability of the TKA patients two to four weeks after surgery, and enhance the quality of life of patients two to three months after surgery.

ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

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.