This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

Objective To establish a scientific, comprehensive, and operable community resilience evaluation index system for emergent major infectious diseases. Methods Based on the social ecosystem theory, a preliminary evaluation index system was formed by using content analysis and boundary analysis. The index system was then supplemented and revised through panel discussions. The final index system and index weights were clarified by two rounds of Delphi method. Results The expert positive coefficient, expert authority coefficient, and expert coordination coefficient of the two rounds of expert consultations were examined. According to the screening principle of the ^“threshold method^”, the indicators were screened, and the weights of each indicator were determined in the second round of Delphi expert consultation. The analysis of the reliability of the indicator system showed Cronbach's α= 0.399 , indicating that the indicator system had a relatively high reliability. Factor analysis was carried out on 7 primary indicators, and the measure of sampling adequacy (MSA) values were all greater than 0.5, which passed the validity test. Conclusion A set of evaluation index system that can accurately reflect the resilience level of communities with emergent major infectious diseases has been constructed, including 7 primary indicators, 21 secondary indicators, 54 tertiary indicators, and 108 tertiary indicators, which has realized the quantitative evaluation of the hidden resilience level of communities.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Endometriosis （EMT） is a common disease with frequent occurrence and difficult to be cured in modern clinical practice of obstetrics and gynaecology. It is characterized by progressively worsening dysmenorrhoea， pelvic mass， and infertility. The incidence of EMT is growing and increasingly younger patients are diagnosed with this disease， which poses a serious threat to the reproductive and psychological health of women of childbearing age and adolescent females. However， the pathogenesis of EMT is still not completely clear， and the disease has a long course. Therefore， developing new therapies is an urgent clinical problem to be solved. Great progress has been achieved in the treatment of EMT with traditional Chinese medicine （TCM）， while the underlying mechanism remains in exploration. Programmed cell death （PCD） is a cell death mode mediated by a variety of bio-molecules with specific signaling cascades. The known PCD processes include apoptosis， pyroptosis， autophagy， ferroptosis， and cuproptosis， which all play a pivotal role in the development of EMT. Researchers have made achievements in the treatment of EMT with TCM， which regulates PCD via multiple pathways， routes， targets， and mechanisms. However， the progress in the regulation of PCD in the treatment of EMT with TCM remains to be reviewed. This paper reviews the research progress in the treatment of EMT with TCM from five PCD processes （apoptosis， pyroptosis， autophagy， ferroptosis， and cuproptosis）， with the aim of providing a theoretical basis for the clinical prevention and treatment of EMT.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To investigate the detection rate and related influencing factors of gastrointestinal diseases in grass-roots personnel.Methods A total of 481 grass-roots personnel were enrolled and examined by magnetic-controlled capsule endoscopy(MCCE).Multivariate logistic regression were used to analyze the influencing factors of gastrointestinal diseases detected by MCCE.Results All personnel completed MCCE,and gastrointestinal diseases were detected in 154(32.0%)cases,including 106 cases of erosive gastritis,25 cases of chronic atrophic gastritis,17 cases of digestive tract polyp,16 cases of gastric ulcer,5 cases of reflux esophagitis,4 cases of cardia,1 case of duodenitis,and 1 case of enteritis.Gastrointestinal diseases was correlated with special operation posts,long-term tasks within recent 6 months,abdominal distension,belching,nausea and vomiting,diarrhea and other symptoms(all P＜0.05).Multivariate logistic regression analysis showed that the risk factors of gastrointestinal diseases were working in special operation posts,performing long tasks within 6 months,belching,nausea and vomiting symptoms(all P＜0.05).Conclusion The overall detection rate of gastrointestinal diseases(mainly acid related diseases)is relatively high,and its incidence is closely related to working in special operation posts and performing long-term tasks within recent 6 months.Personnel working in special operation posts should be more alert to gastrointestinal diseases.Gastrointestinal symptoms have reference value for the prediction of lesions,but more attention should be paid to the identification of functional gastrointestinal diseases with endoscopy.

Objective To investigate the effects and underlying mechanisms of down-regulating m6A demethylase fat mass and obesity-associated protein(FTO)on proplatelet formation in the MEG-01 megakaryocytic cells.Methods ①MEG-01 cells were treated with 1 nmol/L phorbol myristate acetate(PMA)(treatment group)or DMSO(control group)for 72 h.FTO expression was measured by Western blotting and RT-qPCR.② MEG-01 cells were infected with targeted FTO shRNA(knockdown group,sh-FTO)or negative control shRNA(negative control group,sh-NC)viruses.FTO knockdown group and negative control group MEG-1 cells were treated with 1 nmol/L PMA for 72 h,and the protein and mRNA expression levels of FTO were detected by Western blotting and RT-qPCR.Cell cycle,viability and apoptosis were assessed by propidium iodide(PI)DNA staining,CCK-8 assay and Annexin V-FITC/PI double staining and TUNEL staining.The expression of cleaved Caspase-3 protein was determine by Western blotting.Megakaryocyte maturation was assessed by CD41/CD61 staining.Proplatelet formation was observed under bright field and detected by CD61 immunofluorescence assay.The expression of apoptosis-related molecules(Caspase3,BAD,BAK1,BCL2 and MCL1)was detected by RT-qPCR,and the protein change of BCL2 was further verified by Western blotting.The dataset was screened out from the gene expression omnibus(GEO)database,and then analyzed with University of California,Santa Cruz(UCSC)genome browser to compare the methylation sequencing peaks on BCL2 mRNA,and m6A methylated RNA immunoprecipitation(m6A-RIP)was used to assess the m6A methylation levels of BCL2 target gene mRNAs in MEG-01 megakaryocytes.Then,the changes in the m6A methylation enrichment level of BCL2 mRNA were observed between the sh-NC group and the sh-FTO group.mRNA stability and ribosome profiling assays were performed to assess translational efficiency of target genes.Results ①PMA treatment upregulated the expression of FTO at protein(P＜0.05)and mRNA(P＜0.01)levels.② FTO shRNA resulted in reduced FTO expression at both mRNA and protein levels(P＜0.01).Compared to the negative control group,the FTO knockdown group showed more cells arrested at the G1/S phase[(60.80±1.29)%vs(72.13±1.18)%,P＜0.01],significantly reduced cell viability[(1.17±0.03)%vs(0.69±0.05)%,P＜0.01],increased Annexin V-FITC/PI positive cells[(12.87±0.83)%vs(17.45±1.58)%,P＜0.01],more TUNEL positive cells[(1.03±0.27)%vs(17.49±9.91)%,P＜0.01],enhanced protein level of cleaved Caspase-3(P＜0.01),decreased proportion of CD41/CD61 positive cells[(51.63±1.13)%vs(34.08±0.53)%,P＜0.01],and less proplatelet formation in MEG-01 megakaryocytes[(26.49±6.73)%vs(13.31±5.97)%,P＜0.01].③Compared to sh-NC group,the FTO knockdown group had significantly decreased protein and mRNA expression of anti-apoptotic molecule BCL2(P＜0.01).UCSC GEO sequencing data revealed there were m6A methylation modification sites on BCL2 mRNA,which was verified through m6A-RIP experiment in MEG-01 megakaryocytes.Compared with GAPDH mRNA,BCL2 mRNA exhibited a significantly enriched m6A signal(P＜0.01).Compared to sh-NC group,a significant increase in m6A methylation modification was observed on BCL2 mRNA.BCL2 mRNA stability was significantly decreased,and its translation efficiency significantly was decreased(P＜0.01).Conclusion m6 A demethylase FTO rgulates BCL2 mRNA stability and translation efficiency,thereby promoting proplatelet formation in MEG-01 megakaryocytes.