Objective: To investigate the therapeutic efficacy and potential mechanisms of fecal microbiota transplantation (FMT) in patients with type 2 diabetes mellitus (T2DM), and to preliminarily identify the traditional Chinese medicine (TCM) syndrome element characteristics of FMT in the treatment of T2DM. Methods: Between March 25, 2023 and September 30, 2024, T2DM patients who met the inclusion and exclusion criteria were enrolled at the Department of Rheumatology and Endocrinology of the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine. Participants received oral microbiota capsules as an adjunct to metformin therapy. Information obtained by four diagnostic methods of TCM, along with clinical and laboratory parameters, was collected before and after the intervention. Metagenomic sequencing was employed to analyze the gut microbiota, and Spearman correlation analysis was used to explore the relationship between laboratory indicators and differential bacterial genera. According to the post-treatment reduction in glycosylated hemoglobin (HbA1c), patients were categorized into a response (R) group and a non-response (NR) group. Treatment outcomes, safety indicators, gut microbiota changes, and TCM syndrome element features were compared between the two groups. Results: A total of 53 T2DM patients were included in the final analysis, and 30 patients were assigned to R group and 23 to NR group. After treatment, the R group exhibited significant reductions in HbA1c, fasting plasma glucose (FPG), and 2-hour postprandial glucose (2hPG) (P < 0.05 or P < 0.01). The NR group also showed significant decreases in HbA1c and FPG levels P < 0.01 or P < 0.05. Compared with the NR group, after treatment, FPG level in the R group demonstrated significant reductions (P < 0.01). As compared with before treatment, pancreatic islet function demonstrated enhancement in the R group, a significant increase in the 2-hour pastprandial C-peptide (2hC-P) levels in R group (P < 0.05), whereas no marked change was observed in the NR group. Regarding body composition indicators, the R group showed significantly lower waist-hip ratio (WHR), visceral fat (VF), and subcutaneous fat (SF) levels compared with the NR group (P < 0.01). After treatment, the NR group exhibited a significant elevation in aspartate aminotransferase (AST) levels (P < 0.05). Other safety-related indicators fluctuated within normal reference ranges, and no other adverse events, such as diarrhea, fever, or nausea, were reported. Metagenomic sequencing showed that FMT improved the diversity and richness of the gut microbiota, remodeling its overall structure. At the phylum level, the abundance of p_Firmicutes decreased significantly (P < 0.01), while the abundances of p_Bacteroidota and p_Proteobacteria increased significantly (P < 0.01). At the family level, among the 125 identified taxa, the abundances of f_Bacteroidaceae, f_Lactobacillaceae, and f_Sutterellaceae were significantly elevated, whereas six families, including f_Lachnospiraceae, f_Ruminococcaceae, and f_Coriobacteriaceae, were significantly decreased (all P < 0.05). Among the 367 taxa at the genus level, the top 10 differential genera showed significantly increased abundances of g_Bacteroides and g_Sutterella, and significantly decreased abundances in eight genera, including g_Faecalibacterium, g_Ruminococcus, g_Blautia, and g_Collinsella (all P < 0.05). Correlation analysis suggested that the phylum p_Bacillota was positively correlated with improvements in T2DM laboratory parameters, g_norank_f_Prevotellaceae was significantly positively correlated with fasting C-peptide (FC-P) and 2hC-P (P < 0.05). HbA1c demonstrated a significantly positive correlation with g_Blautia and g_Gemmiger (P < 0.05) and a significantly negative correlation with g_Bacteroides and g_Collinsella (P > 0.05). Analysis of syndrome element characteristics revealed that the R group was primarily characterized by pathological patterns of dampness, phlegm, and Yang deficiency. Before treatment, statistically significant reductions in syndrome element scores were observed for dampness, Yang deficiency, spleen, phlegm, Qi deficiency, Qi stagnation, and Yin deficiency (P < 0.01), as well as for heat and liver (P < 0.05). The NR group was mainly featured with Qi deficiency and Yin deficiency. Statistically significant changes in their syndrome element scores after treatment were noted for Qi deficiency (P < 0.01), and for spleen, Qi stagnation, liver, and blood deficiency (P < 0.05). In this group, the score changes for Yang deficiency, Yin deficiency, heat, and dampness were not statistically significant (P > 0.05). Conclusion The principles of syndrome element differentiation can be effectively applied to predict treatment efficacy and facilitate patient selection for FMT in the treatment of T2DM. Patients with T2DM presented with specific TCM syndrome element characteristics, notably dampness, phlegm, and Yang deficiency, represent a highly responsive population to FMT therapy.

The cellular and molecular components of the tumor immune microenvironment (TIME) and their information exchange processes significantly influence the trends of anti-tumor immunity. In recent years, numerous studies have begun to evaluate TIME in the context of previous cancer treatment strategies. This review will systematically summarize the compositional characteristics of TIME and, based on this foundation, explore the impact of current cancer therapies on the remodeling of TIME, aiming to provide new insights for the development of innovative immune combination therapies that can convert TIME into an anti-tumor profile.
Tumor Microenvironment/immunology* ; Humans ; Neoplasms/therapy* ; Immunotherapy/methods* ; Animals

The Janus kinase/signal transducers and activators of transcription (JAK-STAT) control natural killer (NK) cells development and cytotoxic functions, however, whether long non-coding RNAs (lncRNAs) are involved in this pathway remains unknown. We found that miR155HG was elevated in activated NK cells and promoted their proliferation and effector functions in both NK92 and induced-pluripotent stem cells (iPSCs)-derived NK (iPSC-NK) cells, without reliance on its derived miR-155 and micropeptide P155. Mechanistically, miR155HG bound to miR-6756 and relieved its repression of JAK3 expression, thereby promoting the JAK-STAT pathway and enhancing NK cell proliferation and function. Further investigations disclosed that upon cytokine stimulation, STAT3 directly interacts with miR155HG promoter and induces miR155HG transcription. Collectively, we identify a miR155HG-mediated positive feedback loop of the JAK-STAT signaling. Our study will also provide a power target regarding miR155HG for improving NK cell generation and effector function in the field of NK cell adoptive transfer therapy against cancer, especially iPSC-derived NK cells.

OBJECTIVES: To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression. METHODS: We collected voice data from both normal individuals and participants with subthreshold depression by asking them to read specifically chosen words and texts. From each voice sample, 384-dimensional vocal emotional feature variables were extracted, including energy feature, Meir frequency cepstrum coefficient, zero cross rate feature, sound probability feature, fundamental frequency feature, difference feature. The Recursive Feature Elimination (RFE) method was employed to select voice feature variables. Classification models were then built using the machine learning algorithms Adaptive Boosting (AdaBoost), Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Lasso Regression (LRLasso), and Support Vector Machine (SVM), and the performance of these models was evaluated. To assess generalization capability of the models, we used real-world speech data to evaluate the best speech recognition classification model. RESULTS: The AdaBoost, RF, and LDA models achieved high prediction accuracies of 100%, 100%, and 93.3% on word-reading speech test set, respectively. In the text-reading speech test set, the accuracies of the AdaBoost, RF, and LDA models were 90%, 80%, and 90%, respectively, while the accuracies of the other 3 models were all below 80%. On real-world word-reading and text-reading speech data, the classification models using AdaBoost and Random Forest still achieved high predictive accuracies (91.7% and 80.6% for AdaBoost and 86.1% and 77.8% for Random, respectively). CONCLUSIONS Analyzing vocal emotional characteristics allows effective identification of individuals with subthreshold depression. The AdaBoost and RF models show excellent performance for classifying subthreshold depression individuals, and may thus potentially offer valuable assistance in the clinical and research settings.
Humans ; Machine Learning ; Emotions ; Depression/diagnosis* ; Algorithms ; Voice ; Support Vector Machine ; Male ; Female

BACKGROUND:With the continuous advancement of medical technology,stem cell therapy has been used to treat a variety of diseases,including amyotrophic lateral sclerosis. OBJECTIVE:To review the research progress of stem cell therapy for amyotrophic lateral sclerosis,and prospect the development trend of this field. METHODS:PubMed,China National Knowledge Infrastructure(CNKI),and WanFang Data were searched for articles published from 1995 to 2024 using the key words"amyotrophic lateral sclerosis,mesenchymal stem cells,neural stem/progenitor cells,pluripotent stem cells."A total of more than 1 700 articles were retrieved,and 58 articles were finally included in this review. RESULTS AND CONCLUSION:Amyotrophic lateral sclerosis is a neurodegenerative disease that affects lower motor neurons in the brainstem and spinal cord and upper motor neurons in the motor cortex.The related research of stem cells in the treatment of amyotrophic lateral sclerosis has become a research hotspot.In this review,we summarize the application of different types of stem cells in amyotrophic lateral sclerosis research,including mesenchymal stem cells,neural stem progenitor cells,and induced pluripotent stem cells,and evaluate the key points of preclinical research such as stem cell source,cell volume,stem cell modification methods,and drug delivery routes,which lays the foundation for the future application of stem cell therapy.

Informed consent in psychological counseling is the first step for clients to initiate the counseling process， and the degree and effectiveness of informed consent are important factors that determine the subsequent effectiveness and development of psychological counseling. By elaborating on the connotation and importance of informed consent in psychological counseling， the ethical dilemmas of the issue of informed consent in psychological counseling were classified and summarized. There were ethical dilemmas， such as the lack of consensus， procedural guarantee mechanisms， special clients procedures and informed consent principles， as well as non-standard informed consent procedures in online psychological counseling. The paper also proposed to clarify the explanatory obligations of counselors， strengthen procedural ethical constraints， formulate special norms for informed consent for special subjects， and enhance the ethical education and capacity building of psychological counselors.

Objective To observe the effects of growth differentiation factor-15(GDF-15)on collateral circulation and cardiac function in rats with acute myocardial infarction(AMI)in relation to the nitric oxide(NO)/cyclic guanosine monophosphate(cGMP)/protein kinase G(PKG)signaling pathway.Methods An AMI rat model was constructed by ligating the left anterior descending coronary artery.After modeling,the rats were divided randomly into Sham,Model,and GDF-15 groups(n=12 rats per group).Rats in the GDF-15 group were injected intraperitoneally with recombinant GDF-15 protein,and the other two groups were injected with the same amount of normal saline twice a week for 8 consecutive weeks.Cardiac function was detected by echocardiography.Pathological damage to rat myocardial tissue was detected by hematoxylin and eosin staining and the collateral circulation was observed by CD31 immunohistochemical staining.Vascular endothelial growth factor(VEGF)mRNA expression was detected by quantitative polymerase chain reaction.Transcriptomic sequencing of heart tissues in the model and GDF-15 groups was performed and differentially expressed genes(DEGs)were screened.Pathway enrichment analysis of the DEGS was carried out according to the Kyoto Encyclopedia of Genes and Genomes(KEGG).Nitric oxide(NO),reactive oxygen species(ROS),and cGMP were detected using kits,and VEGF,endothelial nitric oxide synthase(eNOS)monomer,p-eNOSser1177monomer,eNOS dimer,and PKG protein were detected by Western blot.Results Left ventricular end-systolic diameter(LVEDs)and left ventricular end-diastolic diameter(LVEDd)were increased(P＜0.001),and left ventricular ejection fraction(LVEF)and the short-axis shortening rate(FS)were decreased in the Model group compared with the Sham group(P＜0.001).Myocardial cell necrosis was more severe,vascular density in the infarcted area was decreased(P＜0.05),but VEGF mRNA and protein levels were no change(P＞0.05),and levels of NO,eNOS dimer,cGMP,and PKG protein were decreased(P＜0.05),and expression levels of ROS,eNOS monomer,and p-eNOSser1177 monomer were increased(P＜0.05).LVEDs and LVEDd decreased(P＜0.05),LVEF and FS increased(P＜0.01),myocardial cell necrosis was relieved,vascular density in the infarcted area increased significantly(P＜0.0001),and VEGF mRNA levels increased(P＜0.0001),compared with the Model group.Transcriptomic sequencing identified 324 DEGs,including 230 up-regulated and 94 down-regulated genes.According to KEGG enrichment analysis,the cGMP-PKG signaling pathway showed the most significant difference in the T20 pathway.VEGF,NO,eNOS dimer,cGMP,and PKG protein levels were all increased(P＜0.05),while ROS,eNOS monomer,and p-eNOSser1177 monomer were decreased in the GDF-15 group(P＜0.05).Conclusions GDF-15 can promote collateral circulation in ischemic myocardium and improve cardiac function by inhibiting eNOS decoupling and activating the NO/cGMP/PKG pathway.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Objective To investigate the medication characteristics of Professor Gu Shizh in the treatment of lumbar disc herniation based on various data mining techniques.Methods The outpatient case records of Professor Gu was collected from January 2018 to September 2024 in the Guoyitang outpatient department of Beijing University of Chinese Medicine(BUCM)and BUCM Famous Elderly TCM Inheritance Research Integrated Platform.Patients with lumbar disc herniation mentioned in Professor Gu Shizhe's book of Zhi Zhen Zhi Yao were also be screened.R Studio 2024.4.3.2 was used for descriptive analysis and Apriori was used for association rule analysis.SPSS Statistics 27.0 was used for clustering analysis,and chiplot website was used for visualize the properties,tastes and meridians of drugs.Cytoscape 3.10.2 was used to build a network of"high frequency drug association",and combined with the TCM theories characteristics and medicine law used by professor Gu Shine in the treatment of lumbar disc herniation were summarized.Results A total of 63 cases were collected,involving 194 prescriptions and 187 kinds of Chinese materia medica.The top 30 drugs with the highest frequency were extracted,and the top 6 drugs were Angelicae Pubescentis Radix,Taxilii Herba,salt Eucommiae Cortex,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Chuanxiong Rhizoma,Gentianae Macrophyllae Radix.The main tastes were sweet,bitter and pungent,the main properties were warm and neutral.The main meridians were liver,spleen,lung,kidney and stomach meridians.Three core medicinal combinations were obtained by clustering analysis,including Duhuo Jisheng Decoction,Gegeng Decoction,Zhijing Powder and Xuduan Pill.Conclusion The focus of Professor Gu's treatment for lumbar disc herniation is to nourish the liver and kidneys,warm and replenish qi and blood,reflecting the characteristics of"qi and blood are in harmony,essence and blood are equally emphasized"in medication

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.