The brain-gut interaction theory is a multidimensional integrative concept based on the brain-gut axis， involving neural， endocrine， and immune regulatory networks as well as the gut microbiota. Zang-fu organs （脏腑） theory in traditional Chinese medicine （TCM） shows a high degree of consistency with the brain-gut interaction theory， and the core functions such as the spleen and stomach governing the ascending of the clear and descending of the turbid， the liver governing the free flow of qi， and the heart governing mental and emotional activities are closely associated with the multi-level regulatory mechanisms of the brain-gut axis. TCM therapy can modulate brain-gut interactions through multiple pathways in the treatment of spleen and stomach diseases， including the regulation of gastrointestinal hormone secretion， neurotransmitter levels， the hypothalamic-pituitary-adrenal （HPA） axis， immune homeostasis and inflammatory responses， as well as the gut microecology. However， current basic research on the brain-gut interaction theory in TCM for spleen and stomach diseases still faces several challenges， such as difficulties in integrating TCM spleen-stomach theory with modern pathophysiology， lack of innovation in research concepts， and limitations in research methodologies. It is therefore proposed that multidisciplinary collaboration， multi-omics technologies， and targeted research approaches should be adopted to provide more comprehensive methods for basic research on TCM spleen and stomach diseases， thereby promoting the in-depth development of brain-gut interaction theory.

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

BACKGROUND: Biomarkers-based prediction of long-term risk of acute coronary syndrome (ACS) is scarce. We aim to develop a risk score integrating clinical routine information (C) and plasma biomarkers (B) for predicting long-term risk of ACS patients. METHODS: We included 2729 ACS patients from the OCEA (Observation of cardiovascular events in ACS patients). The earlier admitted 1910 patients were enrolled as development cohort; and the subsequently admitted 819 subjects were treated as validation cohort. We investigated 10-year risk of cardiovascular (CV) death, myocardial infarction (MI) and all cause death in these patients. Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was derived using main part of these variables. RESULTS: During 16,110 person-years of follow-up, there were 238 CV death/MI in the development cohort. The 7 most important predictors including in the final model were NT-proBNP, D-dimer, GDF-15, peripheral artery disease (PAD), Fibrinogen, ST-segment elevated MI (STEMI), left ventricular ejection fraction (LVEF), termed as CB-ACS score. C-index of the score for predication of cardiovascular events was 0.79 (95% CI: 0.76-0.82) in development cohort and 0.77 (95% CI: 0.76-0.78) in the validation cohort (5832 person-years of follow-up), which outperformed GRACE 2.0 and ABC-ACS risk score. The CB-ACS score was also well calibrated in development and validation cohort (Greenwood-Nam-D'Agostino: P = 0.70 and P = 0.07, respectively). CONCLUSIONS CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS. This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.

Pulpotomy, which belongs to vital pulp therapy, has become a strategy for managing pulpitis in recent decades. This minimally invasive treatment reflects the recognition of preserving healthy dental pulp and optimizing long-term patient-centered outcomes. Pulpotomy is categorized into partial pulpotomy (PP), the removal of a partial segment of the coronal pulp tissue, and full pulpotomy (FP), the removal of whole coronal pulp, which is followed by applying the biomaterials onto the remaining pulp tissue and ultimately restoring the tooth. Procedural decisions for the amount of pulp tissue removal or retention depend on the diagnostic of pulp vitality, the overall treatment plan, the patient's general health status, and pulp inflammation reassessment during operation. This statement represents the consensus of an expert committee convened by the Society of Cariology and Endodontics, Chinese Stomatological Association. It addresses the current evidence to support the application of pulpotomy as a potential alternative to root canal treatment (RCT) on mature permanent teeth with pulpitis from a biological basis, the development of capping biomaterial, and the diagnostic considerations to evidence-based medicine. This expert statement intends to provide a clinical protocol of pulpotomy, which facilitates practitioners in choosing the optimal procedure and increasing their confidence in this rapidly evolving field.
Humans ; Calcium Compounds/therapeutic use* ; Consensus ; Dental Pulp ; Dentition, Permanent ; Oxides/therapeutic use* ; Pulpitis/therapy* ; Pulpotomy/standards*

Instrument separation is a critical complication during root canal therapy, impacting treatment success and long-term tooth preservation. The etiology of instrument separation is multifactorial, involving the intricate anatomy of the root canal system, instrument-related factors, and instrumentation techniques. Instrument separation can hinder thorough cleaning, shaping, and obturation of the root canal, posing challenges to successful treatment outcomes. Although retrieval of separated instrument is often feasible, it carries risks including perforation, excessive removal of tooth structure and root fractures. Effective management of separated instruments requires a comprehensive understanding of the contributing factors, meticulous preoperative assessment, and precise evaluation of the retrieval difficulty. The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes. The current manuscript provides a framework for understanding the causes, risk factors, and clinical management principles of instrument separation. By integrating effective strategies, endodontists can enhance decision-making, improve endodontic treatment success and ensure the preservation of natural dentition.
Humans ; Root Canal Therapy/adverse effects* ; Consensus ; Root Canal Preparation/adverse effects*

ObjectiveTo explore the potential mechanism of Huoxue Xiaoyi Formula （活血消异方， HXF） in treating ovarian endometriosis （OEM） from the perspective of T follicular helper （Tfh） cells and the Janus kinase/signal transducer and activator of transcription （JAK/STAT） signaling pathway. MethodsForty-five female SD rats with normal estrous cycles were randomly divided into three groups， HXF group， model group， and normal group， with 15 rats in each group. A rat model of OEM was established by autologous endometrial tissue implantation. After successful modeling， the treatment group received HXF at 5.85 g/（kg·d） by gavage for 14 consecutive days. The model group and normal group received 1 mL/d of normal saline by gavage. RNA-sequencing data from human proliferative-phase endometriotic and normal endometrial tissues were downloaded from the GEO database. Transcriptomic sequencing was used to analyze gene expression in rat ovarian ectopic tissues and normal uterine tissues， and comparisons were made with human data to verify JAK/STAT pathway activation in proliferative-phase ectopic tissues. Immunohistochemistry was used to detect the positive expression of CXC chemokine receptor 5 （CXCR5） and interleukin-21 （IL-21） in rat ovarian ectopic and normal uterine tissues. Western Blotting was performed to detect the protein levels of IL-21， IL-21 receptor （IL-21R）， Janus kinase 1 （JAK1）， signal transducer and activator of transcription 6 （STAT6）， and B-cell lymphoma 2 （Bcl-2）. Tfh cell infiltration was analyzed using immune cell infiltration methods. ResultsGene set enrichment analysis showed that the JAK/STAT pathway was significantly activated in human proliferative-phase endometriotic tissues compared to normal endometrial tissues. Similarly， the JAK/STAT pathway was markedly activated in rat ovarian ectopic tissues in the model group compared to the normal group， but suppressed in the HXF group compared to the model group. Compared with normal uterine tissues， ovarian ectopic tissues in the model group showed increased Tfh cell infiltration scores， higher CXCR5 and IL-21 expression， and elevated levels of IL-21， IL-21R， JAK1， STAT6， and Bcl-2 proteins. Compared with the model group， HXF group showed reduced CXCR5 and IL-21 expression and decreased protein levels of IL-21， IL-21R， JAK1， STAT6， and Bcl-2. ConclusionHXF may suppress activation of the JAK/STAT signaling pathway in ovarian endometriotic tissues by inhibiting IL-21 secretion from Tfh cells.

Objective To analyze the diversity and composition of gut microbiota in individuals with circadian rhythm disruption and their correlation with cognition.Methods Night shift workers and regular shift workers were subjected from our hospital during August 2022 and October 2024.The participants with circadian rhythm disorders were assigned into an experimental group(n=24),and those with normal circadian rhythms were into a control group(n=24).Their height,weight,age,gender,body mass index(BMI)and fresh fecal samples were collected,and Montreal Cognitive Assessment(MoCA)and Mini-Mental State Examination(MMSE)were used to evaluate their mental status.Metagenomics,Alpha and Beta diversity analyses,Linear Discriminant Analysis Effect Size(LEfSe),and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis were employed to investigate the diversity and function characteristics of gut microbiota in the participants.Results There were no statistical differences between the 2 groups in baseline data,such as height,weight,gender,age,and BMI(P>0.05).Alpha diversity analysis indicated that no statistical differences were observed in the ACE,Chao1,Shannon,or Simpson indices between the 2 groups,while beta diversity analysis revealed significant differences(P<0.01),suggesting different structure of gut microbiota between them.In the experimental group,the abundance of Faecalibacterium prausnitzii and Agathobacter rectalis was decreased,while that of Escherichia coli and Phocaeicola vulgaratus was increased,with significant differences when compared with the control group(P<0.05).Additionally,KEGG functional analysis showed that the experimental group had obviously higher expression levels in Th17 cell differentiation and the IL-17 signaling pathway than the control group(P<0.05).Agathobacter rectalis and Faecalibacterium prausnitzii were positively correlated with MoCA score and MMSE score(P<0.05,P<0.01).Agathobacter rectalis was negatively correlated with the IL-17 signaling pathway and Th17 cell differentiation.Conclusion Individuals with circadian rhythm disorders have significant changes in the structure and function of gut microbiota when compared to those with normal circadian rhythms.Agathobacter rectalis may be involved in the regulation of the IL-17 signaling pathway and differentiation of Th17 cells,thereby possibly impacting the increases of cognitive score related to circadian rhythm disorders.

Objective:To analyze the characteristics and changes in incidence and mortality of ischemic and hemorrhagic stroke among Chinese residents from 2015 to 2019.Methods:The incidence and mortality data of ischemic and hemorrhagic stroke from 2015 to 2019 were collected from the China Registry of Cardiovascular Events (China RACE), which was established in 2014 and covered 100 counties (cities and districts) in 31 provinces in China. The age-standardized incidence rate (ASIR) was calculated using the Seventh National Census data as the standard population. The ratio of the incidence rate of ischemic stroke to hemorrhagic stroke was calculated. The subtype-specific mortality-to-incidence ratio (M/I) was calculated by the ratio of the number of deaths to the reported incidence cases. The relative ratio (RR) of M/I for ischemic to hemorrhagic stroke was calculated. The Joinpoint model was used to analyze the annual percentage change (APC) and trend of the incidence rate of stroke.Results:From 2015 to 2019, a total of 1 354 614 new stroke cases were reported, including 1 077 244 (79.52%) ischemic stroke and 277 370 (20.48%) hemorrhagic stroke cases, respectively. A total of 248 620 stroke deaths were reported, including 119 819 (48.19%) ischemic stroke deaths and 128 801 (51.81%) hemorrhagic stroke deaths. The incidence ratio of ischemic/hemorrhagic stroke from 2015 to 2019 was 3.50∶1, 3.76∶1, 3.63∶1, 4.23∶1, and 4.35∶1, respectively. From 2015 to 2019, there was no statistically significant annual trend of ASIR of ischemic stroke in overall, urban and rural areas and males ( Ptrend>0.05), while there was a downward trend in females (APC=-1.02%, Ptrend=0.042). The incidence of hemorrhagic stroke in the whole population, rural areas, males and females showed a downward trend ( Ptrend<0.05). Patients aged 45-49 years had an upward trend in the incidence rate of ischemic stroke (APC=3.82%, Ptrend=0.011), while those aged 70-74 years (APC=-7.37%, Ptrend=0.034), 80-84 years (APC=-9.75%, Ptrend=0.001) and 85 years and over (APC=-11.22%, Ptrend=0.017) presented a downward trend in the incidence of hemorrhagic stroke. During the period, the overall relative ratio of M/I (RR) for ischemic to hemorrhagic stroke was 4.2∶1, which was lower in urban than in rural areas (3.8 vs. 4.3). The largest gap between urban and rural areas was in the 55-59 age group (6.8 vs. 9.3). Conclusion:The incidence and mortality of ischemic and hemorrhagic stroke among Chinese residents are severe from 2015 to 2019, and there are regional and population differences.