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 predict the value of admission dehydration state combined with fluid accumulation index for post-operative prognosis in elderly patients with intracerebral hemorrhage(ICH).Methods A retrospective study was conducted on 320 elderly ICH patients receiving surgical treatment in Department of Neurosurgery and then admitted to its Intensive Care Unit of the Affiliated Hospital of North China University of Science and Technology from May 2023 to March 2024.According to the survival status at 30 d after onset,they were divided into a survival group(202 cases)and a death group(118 cases).Clinical data such as basic information,admission vital signs,laboratory indicators,and fluid supplementation were compared between the two groups.ROC curve was plotted.Results The death group exhibited significantly advanced age,more bleeding sites,higher 7-day fluid accumulation index,and larger proportions of ventricular rupture,midline displacement and admission dehydration status,but lower uric acid level than the the survival group(P＜0.05,P＜0.01).Binary logistic regression analysis showed that admission dehydration status and 7-day fluid accumulation index were risk factors for 30-day mortality in elderly ICH patients after surgery(P＜0.01).ROC curve analysis showed that the AUC value of admission dehydration status combined with 7-day fluid accumulation index in predicting 30-day death of elderly ICH patients after surgery was 0.774(95％CI:0.722-0.825),and that of the combination was better than that of each indicator alone(P＜0.05).Conclusion Hospital dehydration status combined with 7-day fluid accumulation index has the best effectiveness in predicting 30-day mortality in elderly ICH patients after surgical treatment.

Objective To investigate the predictive value of early fluid balance combined with daily average fluid intake on the prognosis of patients who received hematoma evacuation for spontaneous intracerebral hemorrhage(sICH).Methods This study retrospectively and consecutively enrolled patients who underwent hematoma evacuation for sICH admitted to the Neurosurgical Intensive Care Unit of North China University of Science and Technology Affiliated Hospital between January 2023 and January 2025.Patients'baseline and clinical data were collected,including gender,age,admission vital signs(body temperature,respiratory rate,heart rate,systolic and diastolic blood pressure),medical history(hypertension,diabetes mellitus,coronary heart disease),admission Glasgow coma scale(GCS)score,admission laboratory parameters(including white blood cell count,red blood cell count,platelet count,urine bacterial levels,urine ketones,urine pH,homocysteine),hemorrhage location,hemorrhage volume,time from ictus to surgery,and hematoma evacuation rate(hematoma evacuation rate[％]=[preoperative hematoma volume-postoperative hematoma volume]/preoperative hematoma volume × 100％).Fluid intake,output,balance,daily average intake,and cumulative fluid balance during the early postoperative period(the first to seventh 24-hour periods)were assessed and recorded.Fluid intake comprised the sum of intravenously administered fluids(crystalloids,hypertonic fluids,colloids,blood products,and parenteral nutrition),enteral nutrition(nasogastric feeding solutions and water),and renal replacement therapy fluids.Fluid output comprised the sum of urine volume,vomitus,drainage fluid,and dialysis fluid losses.Fluid balance was defined as the difference between fluid intake and output.Daily average intake was calculated as the average of the fluid intake over the first seven postoperative 24-hour periods.Cumulative fluid balance was the sum of the fluid balances over the first seven postoperative 24-hour periods.Patient outcomes were assessed at 30 days postoperatively.Discharged patients underwent outpatient review or telephone follow-up,while inpatients were evaluated by neurosurgeons.Prognosis was evaluated using the 30-day modified Rankin scale(mRS)score:mRS scores 0-3 were classified as favorable prognosis,and mRS scores 4-6 as poor prognosis.Patients were subsequently divided into favorable prognosis and poor prognosis groups based on mRS scores.Factors with P＜0.05 in univariate comparisons and without collinearity(multicollinearity was defined by:tolerance＜0.1 and variance inflation factor＞10)were included in multivariate Logistic regression analysis to identify independent risk factors for poor 30-day prognosis after hematoma evacuation in sICH patients.Receiver operating characteristic(ROC)curve analysis was employed to evaluate the predictive value of relevant factors for poor 30-day prognosis after hematoma evacuation in sICH patients.Results A total of 320 patients who underwent hematoma evacuation for sICH were enrolled,including 192 males and 128 females,with an age range of 20-91 years(median age 62[53,68]years).Among them,144 patients were assigned to the favorable prognosis group and 176 to the poor prognosis group.The poor prognosis group exhibited significantly higher hemorrhage volume,homocysteine level,fluid intake at the 3rd,5th,6th,and 7th 24-hour periods,fluid balance at the 3rd,5th,6th,and 7th 24-hour periods,daily average intake,and cumulative fluid balance compared to the favorable prognosis group(all P＜0.05).The favorable prognosis group had a significantly higher hematoma evacuation rate,GCS score,proportion of supratentorial hemorrhage and negative urine ketones than the poor prognosis group(all P＜0.05).No significant differences were found in other clinical data(all P＞0.05).Fluid balance over the first seven postoperative 24-hour periods displayed a rise-decline-rise pattern in the poor prognosis group and a rise-decline pattern in the favorable prognosis group.Both groups peaked at the 3rd 24-hour period.Multivariate Logistic regression analysis identified fluid balance at the 3rd 24-hour period(OR,2.013,95％CI 1.386-2.922,P＜0.01)and daily average intake(OR,3.583,95％CI 1.793-7.161,P＜0.01)as independent influencing factors for poor 30-day prognosis after hematoma evacuation in sICH patients.ROC curve analysis revealed that the area under the curve(AUC)for fluid balance at the 3rd 24-hour period and daily average intake in predicting poor 30-day prognosis were 0.699(95％CI 0.642-0.757)and 0.765(95％CI 0.712-0.819),respectively.The combined model of fluid balance at the 3rd 24-hour period and daily average intake achieved an AUC of 0.804(95％CI 0.756-0.853),demonstrating significantly superior predictive performance compared to either variable alone(both P＜0.05).Conclusions The combination of fluid balance at the 3rd 24-hour period and daily average intake demonstrates modest predictive value for poor 30-day prognosis after hematoma evacuation in sICH patients.These findings require validation through multicenter,large-scale,prospective studies.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective:To compare the dose-response effects of single-fraction ultra-high dose rate (FLASH) and conventional dose rate (CONV) whole abdominal irradiation (WAI) with X-rays on acute radiation-induced intestinal injury in mice, in order to identify optimal dose parameters and potential mechanisms.Methods:A total of 186 male C57BL/6J mice were randomly assigned to a non-irradiation group ( n=6), FLASH irradiation groups ( n=90), and CONV irradiation groups ( n=90). Acute radiation-induced intestinal injury models were established using single-fraction WAI with 11, 12, 13, 14, and 15 Gy X-rays (200 Gy/s for FLASH and 4 Gy/min for CONV). Changes in body weight, stool characteristics, and disease activity index (DAI) scores were assessed at 9 d post-irradiation. At 7 d post-irradiation at 11, 12, and 13 Gy, the intestines were collected for macroscopic examination and length measurement. The small intestine was selected for HE staining and quantitative analysis of intestinal crypt number and mucosal epithelial thickness. The survival of mice was assessed at 15 d post-WAI across all dose groups. Results:After single-fraction WAI at 11, 12, and 13 Gy, the body weight was higher in the FLASH group than that in the CONV group ( t=10.17, 12.65, 10.16, P<0.05). The DAI scores for the FLASH group were 1.00±1.10, 3.17±0.75, and 2.83±1.17, respectively, which were lower than those of the CONV group (4.33±0.52, 7.00±0.00, 8.60±0.55; t=8.70, 11.71, 14.99, P<0.05). However, after WAI at 14 Gy and 15 Gy, there were no significant differences in body weight and DAI between the FLASH group and the CONV group ( P>0.05). At 7 d after single-fraction WAI at 11, 12, and 13 Gy, mice in the FLASH group exhibited less intestinal congestion, edema, and shortening compared with the CONV group. The difference between the FLASH and CONV groups were statistically significant in small intestine length at 11 and 13 Gy ( t=4.42, 3.78, P<0.05), and in colorectal length at 11 and 12 Gy ( t=3.97, 3.12, P<0.05). Small intestine HE staining revealed superior preservation of intestinal architecture in the FLASH group compared with the CONV group, characterized by longer villi, increased crypt numbers, thicker mucosal epithelium, and enhanced structural integrity. The differences in crypt number and mucosal epithelial thickness were statistically significant ( tcrypt=13.10, 23.80, 11.90; tmucosal=5.75, 2.64, 7.74; P<0.05). At 15 d post-irradiation, the survival rate in the 15 Gy FLASH group was higher than that in the CONV group (50% vs. 10%, χ2=5.39, P<0.05), with a median survival extension of 6 d ( HR=0.340, 95% CI: 0.115 4-0.999 9). No significant survival differences were observed between the FLASH group and the CONV group at 11, 12, 13, and 14 Gy ( P>0.05). Conclusions:FLASH irradiation significantly alleviated acute radiation-induced intestinal injury from medium single-fraction WAI with 11, 12, and 13 Gy X-rays compared with CONV irradiation, and showed potential to improve mouse survival after single-fraction WAI at 15 Gy. This effect is likely associated with the preservation of intestinal crypts and exhibits a dose-dependent relationship.

Objective To explore the effect of remote ischemic conditioning(RIC)combined with binaural beat music(BBM)train-ing in patients with cognitive dysfunction after cerebral infarction.Methods From June,2024 to January,2025,200 patients with cognitive dysfunction after cerebral infarction in the Affil-iated Hospital of North China University of Science and Technology were randomly divided into control group(n=50),RIC group(n=50),BBM group(n=50)and combined group(n=50).All the groups received stan-dardized routine training,RIC group received RIC,BBM group received BBM,and the combined group received RIC+BBM,for 14 days.They were assessed with Montreal Cognitive Assessment(MoCA)and Stroke-Specific Quality of Life Scale(SS-QOL)at baseline,day 14,and day 90.Results Two cases in the control group,four in RIC group,three in BBM group and three in the combined group dropped out.For MoCA scores,the inter-group effect,intra-group effect and interaction effect were all significant(F>13.463,P<0.001).After intervention,on both day 14 and day 90,the score of MoCA was higher in each in-tervention group than in the control group(P<0.05),and was higher in the combined group than in both BBM and RIC groups(P<0.05);and on day 90,it was higher in RIC group than in BBM group(P<0.05).For SS-QOL,the score increased with time in all the groups(χ2>75.182,P<0.001).After intervention,there was signif-icant difference at each time point among four groups(H>18.260,P<0.001).On day 14 and day 90,the score of SS-QOL was higher in the combined group than in the control and BBM groups(|Z|>3.149,P<0.05);on day 90,the score was higher in RIC group than in the control group(|Z|=3.590,P<0.05),and it was higher in the combined group than in RIC group(|Z|=3.186,P<0.05).Conclusion RIC,BBM and their combination all improved cognitive function after cerebral infarction.RIC was superior to BBM,and the combined intervention yielded the greatest benefit.Both RIC and the combined intervention im-proved quality of life,with the combined approach being the most effective.

Objective To compare and find an optimal model for predicting the risk of DKD occurrence in patients with type 2 diabetes mellitus(T2DM).Methods A total of 2005 patients with T2DM were enrolled in this study from The Second Hospital of Shijiazhuang City during December 2017 to December 2022.All the subjects were divided into a training set(n=1403)and a validation set(n=602)according to the ratio of 3∶1 by simple random sampling.With the occurrence of DKD as the outcome variablein the training set,important feature variables were screened by LASSO regression.Six different machine learning models were established according to the feature variables,thenthe optimal model was determined by comparison,and anonlinerisk predictor for DKD occurrence was constructed in patients with T2DM.Results Taking the occurrence of DKD as the outcome variable in the training set,the results of LASSO regression analysis showed that the optimal value of the model was 10-fold cross validation lambda.1se=0.01662473,and 15 characteristic variables with nonzero coefficient were screened out to be related to the occurrence of DKD.The data included sex,age,family history of DM,DM duration,LDL-C,HbA1c,WBC,PDW,Scr,urine α1-microglobulin,urine β2-microglobulin,urine microalbumin,hypertension,hypokalemia,and DR.In the training set and validation set,the prediction performance of XGBoost model was better than that of other models(AUC=0.872,0.893,95%CI 0.853～0.891,0.865～0.921),the sensitivity was 0.779,0.863,and the specificity was 0.721,0.758,respectively.The F1 scores were 0.774 and 0.787.DCA analysis showed that the XGBoost model had a greater net benefit and threshold probability.According to the XGBoost model,the online predictor of DKD risk in T2DM patients was laid out,and two patients were selected for application,the results showed that the predictive value of the model was 0.185 in non-DKD patients,and the predictive value was 0.510 in DKD patients.Conclusions The XGBoost model is the best model for predicting the occurrence of DKD in T2DM patients,and an online predictor was successfully built.

Objective:To analyze the mediating effect of cardiovascular health status (CVH) on the association between educational level and cardiovascular disease (CVD).Methods:The participants were from Shanghai Suburban Adult Cohort and Biobank, and questionnaire survey, physical examination, blood biochemistry were conducted from 2016 to 2020 for baseline information collection, and follow up was conducted until March 31, 2024 based on the medical data, CVD incidence data and death surveillance data at different levels. The associations of educational level, CVH and time to CVD onset of the study population were analyzed using the accelerated failure time model to analyze the mediating effects of CVH, health behaviors, and health factors in the association of educational level and time to CVD onset. The mediating effects of educational level, gender, and age moderated associations were also analyzed.Results:A total of 57 312 participants were included, with 2 780 new cases of CVD during a median follow-up of 6.71 (6.71-6.72) years, and a mean incidence density of 7.77/1 000 person-years (95% CI: 7.48/1 000 person-years -8.06/1 000 person-years). In total, the less educational level and the lower CVH, the higher CVD incidence density ( P<0.05). The results of accelerated failure time models showed that the time ratio for CVD-free survival was 1.15 (95% CI: 1.06-1.24) and 1.33 (95% CI: 1.10-1.60) for moderate and high educational level, respectively. The results of the mediation effect analysis showed that the association between moderate and high educational level and time to CVD onset was 29.60% (20.50%-50.00%) and 36.10% (23.80%-59.00%), 9.97% (5.07%-20.00%) and 13.84% (6.84%-29.00%), 15.24% (9.64%-27.00%) and 17.55% (11.58%-33.00%) of mediators mediated by CVH, health behaviors, health factors, respectively. Among them, there was an exposure-mediated interaction of educational level and a positive moderating effect of age. Conclusion:CVH, health behaviors and health factors had a proportionate mediating effect in the association between educational level and risk of CVD development.

Objective To investigate the predictive value of early fluid balance combined with daily average fluid intake on the prognosis of patients who received hematoma evacuation for spontaneous intracerebral hemorrhage(sICH).Methods This study retrospectively and consecutively enrolled patients who underwent hematoma evacuation for sICH admitted to the Neurosurgical Intensive Care Unit of North China University of Science and Technology Affiliated Hospital between January 2023 and January 2025.Patients'baseline and clinical data were collected,including gender,age,admission vital signs(body temperature,respiratory rate,heart rate,systolic and diastolic blood pressure),medical history(hypertension,diabetes mellitus,coronary heart disease),admission Glasgow coma scale(GCS)score,admission laboratory parameters(including white blood cell count,red blood cell count,platelet count,urine bacterial levels,urine ketones,urine pH,homocysteine),hemorrhage location,hemorrhage volume,time from ictus to surgery,and hematoma evacuation rate(hematoma evacuation rate[％]=[preoperative hematoma volume-postoperative hematoma volume]/preoperative hematoma volume × 100％).Fluid intake,output,balance,daily average intake,and cumulative fluid balance during the early postoperative period(the first to seventh 24-hour periods)were assessed and recorded.Fluid intake comprised the sum of intravenously administered fluids(crystalloids,hypertonic fluids,colloids,blood products,and parenteral nutrition),enteral nutrition(nasogastric feeding solutions and water),and renal replacement therapy fluids.Fluid output comprised the sum of urine volume,vomitus,drainage fluid,and dialysis fluid losses.Fluid balance was defined as the difference between fluid intake and output.Daily average intake was calculated as the average of the fluid intake over the first seven postoperative 24-hour periods.Cumulative fluid balance was the sum of the fluid balances over the first seven postoperative 24-hour periods.Patient outcomes were assessed at 30 days postoperatively.Discharged patients underwent outpatient review or telephone follow-up,while inpatients were evaluated by neurosurgeons.Prognosis was evaluated using the 30-day modified Rankin scale(mRS)score:mRS scores 0-3 were classified as favorable prognosis,and mRS scores 4-6 as poor prognosis.Patients were subsequently divided into favorable prognosis and poor prognosis groups based on mRS scores.Factors with P＜0.05 in univariate comparisons and without collinearity(multicollinearity was defined by:tolerance＜0.1 and variance inflation factor＞10)were included in multivariate Logistic regression analysis to identify independent risk factors for poor 30-day prognosis after hematoma evacuation in sICH patients.Receiver operating characteristic(ROC)curve analysis was employed to evaluate the predictive value of relevant factors for poor 30-day prognosis after hematoma evacuation in sICH patients.Results A total of 320 patients who underwent hematoma evacuation for sICH were enrolled,including 192 males and 128 females,with an age range of 20-91 years(median age 62[53,68]years).Among them,144 patients were assigned to the favorable prognosis group and 176 to the poor prognosis group.The poor prognosis group exhibited significantly higher hemorrhage volume,homocysteine level,fluid intake at the 3rd,5th,6th,and 7th 24-hour periods,fluid balance at the 3rd,5th,6th,and 7th 24-hour periods,daily average intake,and cumulative fluid balance compared to the favorable prognosis group(all P＜0.05).The favorable prognosis group had a significantly higher hematoma evacuation rate,GCS score,proportion of supratentorial hemorrhage and negative urine ketones than the poor prognosis group(all P＜0.05).No significant differences were found in other clinical data(all P＞0.05).Fluid balance over the first seven postoperative 24-hour periods displayed a rise-decline-rise pattern in the poor prognosis group and a rise-decline pattern in the favorable prognosis group.Both groups peaked at the 3rd 24-hour period.Multivariate Logistic regression analysis identified fluid balance at the 3rd 24-hour period(OR,2.013,95％CI 1.386-2.922,P＜0.01)and daily average intake(OR,3.583,95％CI 1.793-7.161,P＜0.01)as independent influencing factors for poor 30-day prognosis after hematoma evacuation in sICH patients.ROC curve analysis revealed that the area under the curve(AUC)for fluid balance at the 3rd 24-hour period and daily average intake in predicting poor 30-day prognosis were 0.699(95％CI 0.642-0.757)and 0.765(95％CI 0.712-0.819),respectively.The combined model of fluid balance at the 3rd 24-hour period and daily average intake achieved an AUC of 0.804(95％CI 0.756-0.853),demonstrating significantly superior predictive performance compared to either variable alone(both P＜0.05).Conclusions The combination of fluid balance at the 3rd 24-hour period and daily average intake demonstrates modest predictive value for poor 30-day prognosis after hematoma evacuation in sICH patients.These findings require validation through multicenter,large-scale,prospective studies.