With the rapid development and application of cutting-edge neurotechnology， the ethical issues of brain data have become a research hotspot. Brain data， which directly accesses humans’ thoughts and the “loss of control” state under intentional control， deconstructs the subjects’ exclusive right to access their own brain， thereby comprehensively challenging human privacy. The subjects cannot fully control which brain activity signals are collected， and the decoded results of brain data violate the subjects’ autonomy while applying brain data to unknown purposes， posing challenges to informed consent. Brain data describes who you are from a “super first-person” perspective， and it can even “forge” a real first-person perspective to describe who you are. The external presentation of brain data analysis results involves ethical issues in three dimensions， namely， what should be presented， how others treat the analysis results of brain data， and the ethical risks brought about by presenting information in brain data. The governance strategies on the ethical issues of brain data require improving the relevant laws of brain data supervision， refining the ethical principles for brain data applications， leveraging the leading role of the government， and strengthening international cooperation.

Objective: To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods: A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM. Publications were retrieved from the China National Knowledge Infrastructure (CNKI), Wanfang Data, and China Science and Technology Journal Database (VIP), covering the period from database inception to July 3, 2025. A hybrid segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment and evolutionary analysis of topics across different stages. Results: A total of 3 919 publications published between 2003 and 2025 were included, and the research trajectory was divided into five stages based on data-driven breakpoint detection. The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis (2006 – 2010), to machine-learning-based syndrome and prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-graph construction, multimodal integration, and intelligent clinical decision-support systems, with recent studies (2024 – 2025) showing the emergence of large language models and agent-based diagnostic frameworks. Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis, alongside the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes, our findings offer a structured reference framework for the design of intelligent diagnostic systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.

This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

[摘 要] 目的：构建负载STING激动剂DMXAA的锰卟啉金属有机框架纳米颗粒（DPM），探讨其对三阴性乳腺癌（TNBC）细胞4T1及其小鼠移植瘤的治疗效果。方法：通过物理吸附法制备 DPM 纳米颗粒，利用透射电镜、扫描电镜及纳米粒度电位仪表征其形貌与理化性质。常规培养4T1细胞，细胞实验分为对照组、超声辐照组（US组）、DPM治疗组（DPM组）和DPM治疗联合超声辐照组（DPM + US组），用CCK-8法检测细胞活性，免疫荧光法检测高迁移率族蛋白B1（HMGB1）和钙网蛋白（CRT）的表达，WB法检测STING通路相关蛋白的表达。构建4T1细胞移植瘤小鼠模型，分为四组，处理同细胞实验，测量肿瘤体积，免疫荧光法检测移植瘤组织中Ki-67、HMGB1、CRT和缺氧诱导因子-1ɑ（HIF-1ɑ）蛋白的表达，TUNEL法检测细胞凋亡，流式细胞术检测免疫细胞活化情况，对主要器官进行H-E染色，以评估纳米材料的体内安全性。结果：DPM呈梭形，平均粒径（268 ± 3.302）nm，电位（33.1 ± 0.87）mV。细胞实验中，DPM联合超声辐照可明显抑制4T1细胞的增殖（P < 0.001），提高4T1细胞中ROS水平（P < 0.001），诱导4T1细胞CRT表达上调（P < 0.001），HMGB1从细胞核中移至细胞质，激活STING信号通路[p-STING、p-TBK1、p-IRF3蛋白表达均显著增加（均P < 0.001）]。体内实验中，DPM联合超声辐照可显著抑制4T1细胞移植瘤生长（P < 0.001）并促进免疫细胞表型转化（P < 0.001），抑制移植瘤组织中Ki-67、HIF-1α蛋白表达（均P < 0.01），谷胱甘肽（GSH）产生（P < 0.01），促进CRT、HMGB1蛋白表达、ROS产生（P < 0.001），对主要器官结构无明显影响。结论： DPM联合超声辐照可通过激活STING通路显著抑制4T1细胞及其移植瘤的生长，诱导抗肿瘤免疫应答，且对主要器官无明显毒性。

Based on the traditional Chinese medicine(TCM) theory of "kidney-brain interaction", a two-sample Mendelian randomization(MR) analysis was conducted to investigate the causal effects of chronic kidney disease(CKD) on Alzheimer's disease(AD) and analyze the potential mechanisms of kidney-tonifying and essence-replenishing TCM to improve AD. From the perspective that CKD is closely related to the core pathogenesis of AD, namely "kidney deficiency, essence loss, and marrow reduction", genome-wide association study(GWAS) data was used, with the inverse variance weighting(IVW) method as the main approach to reveal the causal association between CKD and AD. Sensitivity analysis was conducted to evaluate the robustness of the results. To further investigate the causal effects of CKD on AD, two different AD datasets were used as outcomes, and the urinary albumin-to-creatinine ratio(UACR) data was used as the exposure for a supplementary analysis. On this basis, the modern scientific mechanism of the kidney-tonifying and essence-replenishing method for improving AD was further explored. The IVW analysis show that CKD(ieu-b-2: OR=1.084, 95%CI[1.011, 1.163], P=0.024; ieu-b-5067: OR=1.001, 95%CI[1.000, 1.001], P=0.002) and UACR(ieu-b-2: OR=1.247, 95%CI[1.021, 1.522], P=0.031; ieu-b-5067: OR=1.001, 95%CI[1.000, 1.003], P=0.015) both have significant causal effects on AD in different datasets, with CKD increasing the risk of AD. The sensitivity analysis further confirmed the reliability of the results. Genetic studies have shown that CKD has a significant causal effect on AD, suggesting that controlling CKD is an important intervention measure for preventing and treating AD. Therefore, further research on CKD's role in AD is crucial in clinical practice. The research enriches the theoretical implication of "kidney-brain interaction", deepens the understanding of AD' etiology, and provides further insights and directions for the prevention and treatment of AD with TCM, specifically from a kidney-based perspective.
Humans ; Alzheimer Disease/genetics* ; Renal Insufficiency, Chronic/genetics* ; Kidney/metabolism* ; Brain/physiopathology* ; Genome-Wide Association Study ; Medicine, Chinese Traditional ; Mendelian Randomization Analysis

This study aims to explore the mechanism through which Yishen Jiangtang Decoction(YSJTD) regulates endoplasmic reticulum stress(ERS)-mediated NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome to improve diabetic nephropathy(DN) in db/db mice. Thirty db/db mice were randomly divided into the model group, YSJTD group, ERS inhibitor 4-phenylbutyric acid(4-PBA) group, with 10 mice in each group. Additionally, 10 db/m mice were selected as the control group. The YSJTD group was orally administered YSJTD at a dose of 0.01 mL·g~(-1), the 4-PBA group was orally administered 4-PBA at a dose of 0.5 mg·g~(-1), and the control and model groups were given an equal volume of carboxylmethyl cellulose sodium. The treatments were administered once daily for 8 weeks. Food intake, water consumption, and body weight were recorded every 2 weeks. After the intervention, fasting blood glucose(FBG), glycosylated hemoglobin(HbA1c), urine microalbumin(U-mALB), 24-hour urine volume, serum creatinine(Scr), and blood urea nitrogen(BUN) were measured. Inflammatory markers interleukin-1β(IL-1β) and interleukin-18(IL-18) were detected using the enzyme-linked immunosorbent assay(ELISA). Renal pathology was assessed through hematoxylin-eosin(HE), periodic acid-Schiff(PAS), and Masson staining, and transmission electron microscopy(TEM). Western blot was used to detect the expression levels of glucose-regulated protein 78(GRP78), C/EBP homologous protein(CHOP), NLRP3, apoptosis-associated speck-like protein containing CARD(ASC), cysteinyl aspartate-specific proteinase(caspase-1), and gasdermin D(GSDMD) in kidney tissues. The results showed that compared to the control group, the model group exhibited poor general condition, increased weight and food and water intake, and significantly higher levels of FBG, HbA1c, U-mALB, kidney index, 24-hour urine volume, IL-1β, and IL-18. Compared to the model group, the YSJTD and 4-PBA groups showed improved general condition, increased body weight, decreased food intake, and lower levels of FBG, U-mALB, kidney index, 24-hour urine volume, and IL-1β. Specifically, the YSJTD group showed a significant reduction in IL-18 levels compared to the model group, while the 4-PBA group exhibited decreased water intake and HbA1c levels compared to the model group. Although there was a decreasing trend in water intake and HbA1c in the YSJTD group, the differences were not statistically significant. No significant differences were observed in BUN, Scr, and kidney weight among the groups. Renal pathology revealed that the model group exhibited more severe renal damage compared to the control group. Kidney sections from the model group showed diffuse mesangial proliferation in the glomeruli, tubular edema, tubular dilation, significant inflammatory cell infiltration in the interstitium, and increased glycogen staining and blue collagen deposition in the basement membrane. In contrast, the YSJTD and 4-PBA groups showed varying degrees of improvement in renal damage, glycogen staining, and collagen deposition, with the YSJTD group showing more significant improvements. TEM analysis indicated that the model group had extensive cytoplasmic edema, homogeneous thickening of the basement membrane, fewer foot processes, and widening of fused foot processes. In the YSJTD and 4-PBA groups, cytoplasmic swelling of renal tissues was reduced, the basement membrane remained intact and uniform, and foot process fusion improved.Western blot results indicated that compared to the control group, the model group showed upregulation of GRP78, CHOP, GSDMD, NLRP3, ASC, and caspase-1 expression. In contrast, both the YSJTD and 4-PBA groups showed downregulation of these markers compared to the model group. These findings suggest that YSJTD exerts a protective effect against DN by alleviating NLRP3 inflammasome activation through the inhibition of ERS, thereby improving the inflammatory response in db/db DN mice.
Animals ; Endoplasmic Reticulum Stress/drug effects* ; Diabetic Nephropathies/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Inflammasomes/drug effects* ; Male ; Kidney/pathology* ; Endoplasmic Reticulum Chaperone BiP ; Humans ; Interleukin-18/genetics* ; Mice, Inbred C57BL

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

Central memory T (Tcm) cells are a crucial subset in T cell development, playing an important role in long-term immune responses. Tcm cells exhibit strong proliferative capacity, long-term survival characteristics, and re-activation potential, enabling them to rapidly differentiate into effector T cells (Teff) upon antigen re-exposure, thus providing robust immune protection. The function of Tcm cells is regulated by various factors, including antigen exposure, cytokines, and metabolic conditions. A deeper understanding of their metabolic and epigenetic mechanisms under different pathological conditions will contribute to the development of more precise and effective immunotherapeutic strategies. This review elaborates on the origin and characteristics of Tcm cells, as well as their roles in antiviral responses, tumor immunity, and immunotherapy.
Humans ; Memory T Cells/cytology* ; Animals ; Immunologic Memory ; Neoplasms/therapy* ; Immunotherapy