ObjectiveTo explore the effects of Jingui Shenqiwan （JSW） and Liuwei Dihuangwan （LDW） on the reproductive ability and brain function of normal mice and compare the actions of the two medications. MethodsSeven groups of female and male mice were divided at a ratio of 2∶1. Except for the control group， the other six groups were as follows： a group of both males and females receiving JSW （3.0 g·kg^-1）， a group of both males and females receiving LDW （4.5 g·kg^-1）， a group of males receiving water and females receiving JSW， a group of males receiving water while females receiving LDW， a group of females receiving water while males receiving JSW， and a group of females receiving water while males receiving LDW. Each group was administered the drug for 14 days and then caged together at a 2∶1 （female∶male） ratio to detect the number of pregnant mice and calculate the pregnancy rate. Pregnant mice continued receiving the drug until they naturally gave birth， which was followed by the observation of newborn mice， calculation of their average number， and the measurement of the offspring's preference for sugar water and neonatal recognition index. At the end of the experiment， the weights of the thymus and spleen were measured to calculate the organ coefficients， and mRNA or protein expression was analyzed in the brain and testes or ovaries. A 1% sucrose solution was used to examine the euphoria of their brain reward systems， while novel object recognition test （NOR） was applied to assess their memory capabilities. mRNA expression was detected using real-time quantitative polymerase chain reaction （Real-time PCR） assay， and protein expression was analyzed with Western blot. ResultsCompared with the control group， oral administration of JSW to both male and female mice for 14 days significantly increased the pregnancy rate of female mice on day 2 after being caged together （P<0.05）， while LDW showed a trend but no statistical significance. Additionally， compared with the control group， JSW could upregulate the gene expression of gonadotropin-releasing hormone （GnRH） in the thalamus， as well as reproductive stem cell factor （SCF） and tyrosine kinase receptor （c-Kit） in the testes and reproductive stem cell marker mouse vasa homologue （MVH） in the ovaries， upregulate the expression of proteins influencing neuronal functional activity， such as brain-derived neurotrophic factor （BDNF）， in hippocampal neurons （P<0.05）， and enhance sucrose preference in male mice （P<0.05）. Compared with the control group， JSW significantly increased sucrose preference and novel object recognition index in offspring mice （P<0.05）， which was related to the upregulation of hippocampal dopamine D1 receptor （D1R） and N-methyl-D-aspartate receptor （Nmdar） gene expression. Compared with the control group， both JSW and LDW could upregulate the protein expression of glucocorticoid receptor （GR）， BDNF， and tyrosine kinase receptor B （TrkB） in the hippocampus of offspring mice （P<0.05）. ConclusionJSW significantly enhances the reproductive ability of normal mice， which is not only related to the release of gonadotropin but also associated with its regulation of brain function. Additionally， JSW has a certain regulatory effect on the brain function of the offspring mice.

ObjectiveTo investigate the therapeutic effect of Astragali Radix-mediated changes in gut microbiota on treating type 2 diabetes （T2DM）. MethodsA 12-week randomized， placebo-controlled clinical trial enrolled eighty patients with newly diagnosed type 2 diabetes and poor glycemic control in the Qi deficiency type. All patients received insulin therapy. The observation group （40 cases） was administered with Astragali Radix Granules， while the control group （40 cases） received a placebo. Both treamtents were taken orally twice daily. Changes in gut microbiota were assessed by 16s rDNA sequencing. Serum glucagon-like peptide-1 （GLP-1） levels were measured using enzyme-linked immunosorbent assay （ELISA）. Glucose metabolism indicators including fasting blood glucose （FPG）， 2-hour postprandial blood glucose （2 h PG），glycated albumin（GA）， and glycated hemoglobin （HbA1c） were evaluated. Pancreatic function was evaluated using fasting C-peptide （FCP）， 2-hour postprandial C-peptide （2 h CP）， and C-peptide area under the curve （AUC_cp）. Traditional Chinese medicine （TCM） syndrome scores， clinical efficacy， and safety indicators were also observed. ResultsIn terms of glucose metabolism indicators， compared with the baseline， both groups exhibited significantly lower FPG， 2 h PG， GA and HbA1C （P<0.01），while FCP， 2 h CP and AUC_cp were significantly higher （P<0.01）. Compared with the control group after the treatment， the observation group showed significantly lower FPG， 2 h PG， GA and HbA1C（P<0.05， P<0.01），and significantly higher FCP， 2 h CP and AUC_cp （P<0.05， P<0.01）， indicating that Astragali Radix can improve glucose metabolism. In terms of the diversity of gut microbiota， no significant differences were detected in the Chao1， Shannon and Simpson indexes of the two groups compared with their respective baselines. However， compared with the post-treatment control group， the observation group demonstrated significant increases in the Chao1， Shannon and Simpson indexes （P<0.05， P<0.01）. The β-diversity analysis showed significant separation in gut microbiota composition before and after treatment in both groups， indicating that Astragali Radix can significantly alter the structure and improve the diversity of gut microbiota. At the phylum level， compared with the baseline， both groups showed a significant increase in the relative abundance of Bacteroidota（P<0.01）. The relative abundance of the potentially harmful phylum Proteobacteria was significantly lower in the observation Group after treatment （P<0.01）. Compared with the post-treatment control group， the observation group had a significantly higher relative abundance of Bacteroidota（P<0.01）. No significant difference was found in Firmicutes/Bacteroidota （F/B） ratio between the two groups after treatment， and other phyla showed no significant differences. At the genus level， compared with the baseline， the observation group exhibited a significant increase in Bacteroides （P<0.01） and a significant decrease in Escherichia-Shigella （P<0.01）， whereas no significant difference was seen in the control group . Compared with the control group after treatment， the observation group after treatment had a significantly higher relative abundance of Bacteroides （P<0.01）. No significant differences were seen in other genera. Linear discriminant analysis （LDA） identified potential characteristics taxa： in the observation group， Bacteroidota at the phylum level and Bacteroides and Dubosiella at the genus level， in the control group， Proteobacteria at the phylum level as well as Barnesiella and Staphylococcus at the genus level. Correlation analysis based on a heatmap revealed that GLP-1 levels were positively correlated with Firmicutes， F/B ratio and Fusobacterium， and negatively correlated with Bacteroidota， Proteobacteria， Bacteroides and Escherichia-Shigella. In terms of clinical efficacy， compared with the control group， the total effective rate of the observation group was significantly higher （P<0.05）. Compared with the baseline， the scores for shortness of breath， fatigue， weakness， spontaneous sweating and reluctance to speak significantly decreased in both groups （P<0.01）. Compared with the control group after treatment， the score for weakness was significantly lower in the observation group （P<0.01），indicating that Astragali Radix could improve clinical symptoms and alleviate weakness symptoms. In terms of safety， compared with the baseline， alanine aminotransferase （ALT） levels significantly decreased in both groups （P<0.05，P<0.01），indicating that Astragali Radix did not induce any significant abnormalities in liver and kidney functions. ConclusionAstragali Radix demonstrates the potential to significantly improve the gut microbiota environment in patients of newly diagnosed type 2 diabetes with Qi deficiency. The therapeutic effect may contribute to glycemic control， possibly mediated by an elevation in GLP-1 level. These findings may support its further clinical investigations and potential applications.

Objective To explore the clinical factors influencing complete response in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). Methods Clinical data of LARC patients treated in the Department of Radiation Oncology at Beijing Shijitan Hospital between January 2013 and December 2024 were retrospectively collected. All patients received nCRT, after which surgery or a watch-and-wait approach was adopted based on treatment response. Univariable and multivariable logistic regression analyses were performed to identify prognostic factors influencing complete response. A clinical prediction model was constructed based on the multivariable analysis results, and its predictive performance was evaluated using the receiver operating characteristic curve. Results A total of 113 eligible patients were included. After nCRT, 19 patients (16.8%) achieved complete response, including 3 with clinical complete response and 16 with pathological complete response. Univariable analysis indicated that pretreatment clinical N stage, extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen were associated with complete response after nCRT (P<0.05). Multivariable logistic regression analysis identified pretreatment extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen as independent influencing factors for complete response (P<0.05). A prediction model incorporating these independent factors yielded an area under the receiver operating characteristic curve of 0.813 (95% confidence interval: 0.713-0.913), with a sensitivity of 89.5% and a specificity of 60.6%, demonstrating good predictive performance. Conclusion Pretreatment extramural venous invasion, carcinoembryonic antigen level, and neoadjuvant treatment regimen are independent factors influencing complete response after nCRT in LARC patients. The prediction model combining these factors may assist in evaluating treatment efficacy following nCRT in LARC patients.

Objective To systematically evaluate the risk factors for postoperative delirium (POD) in patients undergoing pneumonectomy. Methods PubMed, Web of Science, Cochrane Library, CNKI, Wanfang, and VIP databases were searched from the inception to November 7, 2024 for cross-sectional studies, case-control studies, and cohort studies on POD in patients undergoing pneumonectomy. Two researchers independently screened the literature, extracted data, and evaluated the quality of the literature. RevMan 5.4.1 software was used for meta-analysis. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the literature. Results A total of 12 studies were included, with 5 574 patients. The NOS scores of the literature were all≥6 points. Meta-analysis results showed that age (≥60 years) [OR=2.43, 95%CI (2.01, 2.93), P<0.01], American Society of Anesthesiologists (ASA) classification (Ⅳ) [OR=8.74, 95%CI (5.23, 14.61), P<0.01], history of diabetes [OR=12.81, 95%CI (10.45, 15.71), P<0.01], history of cerebrovascular disease [OR=3.00, 95%CI (2.46, 3.67), P<0.01], depression [OR=7.27, 95%CI (5.46, 9.67), P<0.01], squamous cell carcinoma [OR=4.79, 95%CI (1.83, 12.51), P<0.01], malnutrition [OR=5.25, 95%CI (3.35, 8.25), P<0.01], sleep disorders [OR=2.79, 95%CI (2.28, 3.42), P<0.01], and duration of one-lung ventilation during surgery [OR=1.32, 95%CI (1.11, 1.57), P<0.01] were all risk factors for POD, while high body mass index (BMI) [OR=0.96, 95%CI (0.95, 0.97), P<0.01] was a protective factor for POD. Conclusion Age (≥60 years), ASA classification (Ⅳ), history of diabetes, history of cerebrovascular disease, depression, squamous cell carcinoma, malnutrition, sleep disorders, and duration of one-lung ventilation during surgery are independent risk factors for POD, while high BMI is a protective factor.

ObjectiveTo develop a standardized classification model for traditional Chinese medicine （TCM） syndrome patterns of mixed hemorrhoids using multi-center real-world data， and unveil their distribution patterns and core risk factors， thereby providing evidence-based support for standardizing TCM syndrome differentiation and implementing precision interventions. MethodsA multi-center cross-sectional study was conducted， enrolling 13 283 mixed hemorrhoid patients from eight hospitals in Jiangsu Province between September 1st， 2023 and December 31st， 2024. DeepSeek-R1-Distill-Qwen-7B and LLaMA-3.3 large language models （LLM） were integrated with latent class analysis （LCA） to perform unsupervised learning and latent class modeling of TCM symptomatology. Potential risk factors were screened via univariate analysis， followed by logistic regression to identify independent risk factors for each syndrome pattern. ResultsThe model's performance indicators were stable and reliable across different clinical data types，i.e. in the outpatient records， past medical history （F1=99.7%）， current medical history （F1=94.9%）， and specialist examination （F1=90.7%）； in inpatient records， past medical history （F1=98.2%）， current medical history （F1=91.2%）， specialist examination （F1=90.3%）， and discharge status （F1=90.6%）. Latent class mode-ling identified four core TCM syndrome patterns including spleen deficiency and qi sinking syndrome （915 cases， 6.89%）， damp-heat pouring downward syndrome （10 820 cases， 81.46%）， qi stagnation and blood stasis syndrome （1252 cases， 9.43%）， and wind injuring intestinal collaterals syndrome （296 cases， 2.22%）， with respective latent class probabilities of 0.069， 0.815， 0.094， and 0.022. Logistic regression demonstrated that gender， age， disease duration， hypertension， diabetes， hyperlipidemia， constipation， smoking history， and alcohol consumption were independent risk factors for pattern differentiation （P＜0.05）. The efficacy validation evaluation revealed that the cure rates for patients with spleen deficiency and qi sinking syndrome and qi stagnation and blood stasis syndrome were higher than those for patients with damp-heat pouring downward syndrome （adjusted P＜0.05）， with no statistically significant differences among other syndrome patterns. ConclusionDamp-heat pouring downward syndrome is the predominant syndrome in mixed hemorrhoids. Gender， age， disease duration， hypertension， diabetes， hyperlipi-demia， constipation， smoking history， and alcohol consumption are independent risk factors for the differentiation of syndrome types.

This paper has constructed a traditional Chinese medicine （TCM） specialized disease dataset platform for mixed hemorrhoids based on a multimodal large model， and the preliminary application has been validated. The platform uses StarRocks to establish a four-level data warehouse system， enabling the aggregation， cleaning， and standardization of multi-source heterogeneous data. Using DeepSeek-R1-Distill-Qwen-7B as the base model， domain fine-tuning is performed through low-rank adaptation （LoRA） technology. Combined with LLaMA-3.3 natural language processing and reasoning chain techniques， the platform enables intelligent parsing and structured extraction of unstructured TCM medical records. It accurately identifies six major categories and 28 subcategories of entities， including symptoms and syndromes， with a fine-tuned model F1 score of 93.8%. The platform has established a high-quality specialized disease dataset containing more than 50,000 medical records and has been applied in a real-world study involving 17,831 patients， preliminarily verifying the efficacy of TCM heritage surgery.

The multimorbidity of rheumatoid arthritis （RA） and periodontitis （PD） has drawn increasing attention， as both conditions are characterized by chronic inflammation， immune dysregulation， and progressive bone destruction. Modern research confirms that PD is a significant risk factor for RA development， and their coexistence mutually exacerbates disease progression. However， traditional Chinese medicine （TCM） currently lacks a systematic theoretical explanation for this complex multimorbid relationship. This study， based on the TCM theory of abnormal collateral， thoroughly examines the intrinsic connection between RA and PD multimorbidity， proposing "abnormal collateral as the pivot， with accumulated toxins eroding bone" as the core TCM pathogenesis. The research elucidates PD as the "origin of abnormal collateral"， where its pathogens act as toxic factors that invade the joints through collaterals， triggering RA via mechanisms such as molecular mimicry. The dynamic pathological progression of RA-PD multimorbidity can be described as follows： the displacement of Ying and Wei at the microscopic level manifests as immune hyperactivation， leading to collateral malnutrition； heat-toxins traversing collaterals induce collateral hyperactivity， resulting in pathological angiogenesis； ultimately， toxin accumulation at the pivotal abnormal collateral site erodes bone， activating the receptor activator of nuclear factor kappa-B ligand （RANKL）-receptor activator of nuclear factor kappa-B （RANK） signaling pathway-driven osteoclast differentiation. This theoretical framework innovatively integrates modern findings in oral microbiology， immune-inflammation， and bone metabolism， offering a holistic and dynamic perspective to understand the complexity of multimorbidity. Given the limited efficacy of current periodontal treatments for RA and the scarcity of reported TCM compound interventions for multimorbidity， the abnormal collateral theory proposes a systematic intervention strategy centered on "governing diseases through collaterals and regulating collaterals with herbs"， along with TCM therapeutic principles such as "unblocking， clearing， and nourishing collaterals". Potential herbal treatments for multimorbidity are also highlighted. Future research should focus on refining TCM syndrome patterns in multimorbid patients and leveraging omics technologies for deeper exploration， thereby providing a theoretical foundation and research direction for TCM in addressing complex multimorbid conditions.

The multimorbidity of rheumatoid arthritis （RA） and periodontitis （PD） has drawn increasing attention， as both conditions are characterized by chronic inflammation， immune dysregulation， and progressive bone destruction. Modern research confirms that PD is a significant risk factor for RA development， and their coexistence mutually exacerbates disease progression. However， traditional Chinese medicine （TCM） currently lacks a systematic theoretical explanation for this complex multimorbid relationship. This study， based on the TCM theory of abnormal collateral， thoroughly examines the intrinsic connection between RA and PD multimorbidity， proposing "abnormal collateral as the pivot， with accumulated toxins eroding bone" as the core TCM pathogenesis. The research elucidates PD as the "origin of abnormal collateral"， where its pathogens act as toxic factors that invade the joints through collaterals， triggering RA via mechanisms such as molecular mimicry. The dynamic pathological progression of RA-PD multimorbidity can be described as follows： the displacement of Ying and Wei at the microscopic level manifests as immune hyperactivation， leading to collateral malnutrition； heat-toxins traversing collaterals induce collateral hyperactivity， resulting in pathological angiogenesis； ultimately， toxin accumulation at the pivotal abnormal collateral site erodes bone， activating the receptor activator of nuclear factor kappa-B ligand （RANKL）-receptor activator of nuclear factor kappa-B （RANK） signaling pathway-driven osteoclast differentiation. This theoretical framework innovatively integrates modern findings in oral microbiology， immune-inflammation， and bone metabolism， offering a holistic and dynamic perspective to understand the complexity of multimorbidity. Given the limited efficacy of current periodontal treatments for RA and the scarcity of reported TCM compound interventions for multimorbidity， the abnormal collateral theory proposes a systematic intervention strategy centered on "governing diseases through collaterals and regulating collaterals with herbs"， along with TCM therapeutic principles such as "unblocking， clearing， and nourishing collaterals". Potential herbal treatments for multimorbidity are also highlighted. Future research should focus on refining TCM syndrome patterns in multimorbid patients and leveraging omics technologies for deeper exploration， thereby providing a theoretical foundation and research direction for TCM in addressing complex multimorbid conditions.

BACKGROUND:Gradient artificial bone repair scaffolds can mimic unique anatomical features in musculoskeletal tissues,showing great potential for repairing injured musculoskeletal tissues. OBJECTIVE:To review the latest research advances in gradient artificial bone repair scaffolds for tissue engineering in the musculoskeletal system and describe their advantages and fabrication strategies. METHODS:The first author of the article searched the Web of Science and PubMed databases for articles published from 2000 to 2023 with search terms"gradient,bone regeneration,scaffold".Finally,76 papers were analyzed and summarized after the screening. RESULTS AND CONCLUSION:(1)As an important means of efficient and high-quality repair of skeletal system tissues,gradient artificial bone repair scaffolds are currently designed bionically for the natural gradient characteristics of bone tissue,bone-cartilage,and tendon-bone tissue.These scaffolds can mimic the extracellular matrix of native tissues to a certain extent in terms of structure and composition,thus promoting cell adhesion,migration,proliferation,differentiation,and regenerative recovery of damaged tissues to their native state.(2)Advanced manufacturing technology provides more possibilities for gradient artificial bone repair scaffold preparation:Gradient electrospun fiber scaffolds constructed by spatially differentiated fiber arrangement and loading of biologically active substances have been developed;gradient 3D printed scaffolds fabricated by layered stacking,graded porosity,and bio-3D printing technology;gradient hydrogel scaffolds fabricated by in-situ layered injections,simple layer-by-layer stacking,and freeze-drying method;and in addition,there are also scaffolds made by other modalities or multi-method coupling.These scaffolds have demonstrated good biocompatibility in vitro experiments,were able to accelerate tissue regeneration in small animal tests,and were observed to have significantly improved histological structure.(3)The currently developed gradient artificial bone repair scaffolds have problems such as mismatch of gradient scales,unclear material-tissue interactions,and side effects caused by degradation products,which need to be further optimized by combining the strengths of related disciplines and clinical needs in the future.

Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.