ObjectiveBased on literature data mining， this study explores the modeling elements of diarrhea-predominant irritable bowel syndrome （IBS-D） animal models in China and abroad， providing references and suggestions for improving modeling methods and evaluation indicators. MethodsRelevant literature on IBS-D animal experiments from 2014 to 2024 was retrieved through computer searches in databases such as China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， Chinese Medical Journals Full-text Database， and PubMed. Information on experimental animal species， gender， body weight， modeling methods， modeling periods， intervention controls， modeling standards， and detection indicators was organized. Microsoft Excel 2021 software was used to establish a database and perform statistical analysis to examine the characteristics of IBS-D animal models. ResultsA total of 398 articles that met the inclusion criteria were reviewed. The IBS-D animal models were predominantly established using SD rats， Wistar rats， and C57BL/6 mice. Male animals were more commonly used， with rats typically aged 6-8 weeks and mice aged 4-6 weeks. In terms of interventions， piverium bromide was the main Western medicine， Tongxieyaofang was the primary Chinese medicine， and electroacupuncture was the primary acupuncture method. Among the modeling methods， the multi-factor combined composite modeling approach was the most common. Modeling periods were mainly concentrated between 1-14 days and 15-30 days. The success criteria for modeling were mainly evaluated based on the animal's general condition， fecal appearance， visceral sensitivity， gastrointestinal motility， behavior， and pathology. Detection indicators included apparent indexes， pathological markers， biochemical indicators， oxidative stress， brain-gut peptides， neurotransmitters， inflammatory factors， immune function， intestinal permeability， autophagy， apoptosis， proteins related to relevant signaling pathways， intestinal microbiota and its metabolites， etc. ConclusionThere are various methods for establishing IBS-D animal models， but no unified and universally accepted method has been established. The operation of the same modeling methods and the evaluation standards of the models vary across studies. Based on the results of data mining， the authors suggest that the multi-factor combined composite modeling approach most closely reflects the pathophysiological processes of IBS-D， better simulating the complex clinical symptoms of IBS-D patients， such as abdominal pain and diarrhea， and has a high degree of clinical relevance. This method is relatively recommended. While animal models in general align with Western medicine standards， models incorporating traditional Chinese medicine （TCM） syndromes are relatively few. Therefore， one of the future directions for research is to establish IBS-D animal models that meet the combined clinical disease and syndrome requirements of both Western and Chinese medicine.

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective To systematic evaluate the impact of applying the clinical nursing pathway(CNP)on epidural anesthetic analgesia natural delivery.Methods The randomized controlled trial(RCT)and quasi-experimental researches on the application of CNP in epidural anesthetic analgesia natural delivery were retrieved from PubMed,Cochrane Library,Embase,Web of Science,China Knowledge Network database,Wanfang database,VIP and the Chinese Biomedical Literature Database.The retrieval time limit was from January 1,2014,to July 31,2024 with no language limitation.The meta analysis on the included studies was performed by applying RevMan5.4.1.Results A total of 5 RCTs and 2 quasi-experimental studies were in-cluded,involving 979 parturients with deliveries.The meta analysis showed that compared with the conven-tional nursing,CNP could shorten the duration of the first stage of labor(MD=—1.06,95％CI:—1.95——0.17,P=0.02)and the duration of the second stage of labor(MD=—0.12,95％CI:—0.21——0.03,P=0.006);decreased the rate of perineal lateral incision(RR=0.73,95％CI:0.65-0.83,P＜0.001)and inci-dence rate of postpartum urinary retention(RR=0.35,95％CI:0.20-0.63,P＜0.001);and shortened the time to lactation initiation(SMD=—1.52,95％CI:—2.38——0.66,P＜0.001).There was no influence on reducing postpartum 24 h hemorrhage amount(SMD=—0.51,95％CI:—1.23-0.21,P=0.16).The study subjects were divided into the primipara women subgroup and unclassified parturients subgroup.Compared with the conventional nursing group,compared with the conventional nursing,CNP had no impact on the dura-tion of the first stage of labor(MD=—0.32,95％CI:—0.61-0.98,P=0.63)and the duration of the second stage of labor(MD=—0.11,95％CI:—0.25-0.04,P=0.15)in the primipara women subgroup.CNP could reduce the postpartum 24 h hemorrhage volume in the unclassified parturients subgroup(SMD=—1.47,95％CI:—1.72——1.21,P＜0.001).Conclusion Application of CNP in parturients labor analgesia could reduce the perineal lateral incision rate and incidence rate of postpartum urinary retention and shorten the time to lac-tation initiation.Due to the heterogeneity among studies,the impact of CNP on the labor duration and the bleeding amount within postpartum 24 h still requires more high-quality studies to be conducted in the future for verification.

Objective:To construct univariate and multivariate relapse free survival (RFS) prediction models for breast cancer patients with diabetes mellitus type 2 (T2DM) and to compare and select the model with higher predictive performance.Methods:A total of 912 breast cancer patients treated at the First Affiliated Hospital of Dalian Medical University from January 2010 to December 2016 were included, of which 202 patients had T2DM and 710 patients did not. Kaplan-Meier survival curve was drawn based on whether patients had T2DM, and log-rank test was performed based on whether patients had T2DM. All patients were randomly divided into a training set ( n=640) and a validation set ( n=272) at a ratio of 7∶3. Univariate and multivariate Cox proportional risk regression models were used to analyze RFS in breast cancer patients with the survival package. The "rms" package was employed to construct univariate and multivariate RFS prediction models for breast cancer patients with T2DM. Clinical decision curves and calibration curves were used to validate the models. The receiver operator characteristic (ROC) curve was used to compare and analyze the prediction performance of the two models. Results:There were no statistically significant differences between the training set and the validation set patients in terms of age, T2DM, surgical approach, axillary management methods, T stage, N stage, molecular sub-type, estrogen receptor (ER) 1, ER2, progesterone receptor (PR) , ER and PR consistency, Ki67, human epidermal growth factor receptor 2 (HER2) (all P>0.05) . There was a statistically significant difference in histological grade ( χ2=7.59, P=0.022) . Survival analysis showed that the 5-year RFS rate was 83.7% in patients with T2DM and 92.3% in patients without T2DM ( χ2=16.61, P<0.001) . Univariate analysis revealed that age ( HR=1.04, 95% CI: 1.03-1.06, P<0.001) , T2DM ( HR=2.31, 95% CI: 1.49-3.55, P<0.001) , surgical approach ( HR=2.39, 95% CI: 1.20-4.77, P=0.013) , axillary management methods ( HR=2.62, 95% CI: 1.72-3.98, P<0.001) , T stage (T 2: HR=2.13, 95% CI: 1.36-3.31, P<0.001; T 3: HR=6.90, 95% CI: 3.35-14.22, P<0.001) , N stage (N 2: HR=3.87, 95% CI: 2.12-7.07, P<0.001; N 3: HR=8.61, 95% CI: 4.71-15.75, P<0.001) , molecular sub-type (Luminal B: HR=2.74, 95% CI: 1.17-6.36, P=0.019; HER2 +: HR=3.64, 95% CI: 1.38-9.58, P=0.009; TNBC: HR=4.40, 95% CI: 1.71-11.34, P=0.002) , ER1 (>10%: HR=0.57, 95% CI: 0.37-0.90, P=0.016) , ER2 ( HR=0.57, 95% CI: 0.37-0.89, P=0.015) , and PR ( HR=0.56, 95% CI: 0.37-0.86, P=0.008) were all factors influencing RFS in breast cancer patients. Multivariate analysis demonstrated that age ( HR=1.04, 95% CI: 1.02-1.06, P<0.001) , T2DM ( HR=1.82, 95% CI: 1.16-2.85, P=0.009) , T stage (T 2: HR=1.60, 95% CI: 1.01-2.54, P=0.046; T 3: HR=2.64, 95% CI: 1.22-5.72, P=0.014) , N stage (N 2: HR=3.72, 95% CI: 2.01-6.88, P<0.001; N 3: HR=5.34, 95% CI: 2.78-10.25, P<0.001) , and ER1 (>10%: HR=0.63, 95% CI: 0.39-0.99, P=0.046) were independent factors influencing RFS in breast cancer patients. Based on the 10 and 5 variables with P<0.05 in the univariate and multivariate analyses respectively, the nomograms of the univariate and multivariate prediction models were constructed to evaluate the influence of factors such as T2DM on the postoperative RFS of breast cancer patients. Clinical decision curves and calibration curves indicated that both models had high predictive value for RFS in breast cancer patients, and the predictive results were highly consistent with the actual observed results. ROC curve analysis showed that there was no statistically significant difference in the area under the curve (AUC) of the two models for predicting the RFS rates of breast cancer patients in the training set and validation set at 36, 60, and 84 months (all P>0.05) , indicating that the predictive efficacy of the two models was comparable. The multivariate model is more suitable for clinical application because it uses fewer variables. Conclusions:Breast cancer patients with T2DM have poorer prognosis. Age, T2DM, T stage, N stage, and ER1 are independent factors influencing postoperative RFS in breast cancer patients. The multi-factor prediction model of RFS in breast cancer patients based on T2DM is more suitable for clinical application due to its higher predictive efficacy and fewer variables.

Objective:To explore the impact of applying micro-video tracing training model to emerging infectious diseases (EIDs) knowledge training on the autonomous learning ability and learning outcomes among emergency department nurses.Methods:In this study, the training content was developed from the perspective of the historical development of EIDs. The training program was constructed in the form of a micro-video classification module and periodic WeChat group push-based autonomous learning. Convenience sampling was used to select 85 emergency nurses ( n=85) from a grade A tertiary general hospital in Shanghai for micro-video tracing training. The nurses were provided with EIDs knowledge training using a module consisting of 11 episodes, each of which included 5 sessions, i.e., enlightening thinking, micro-video learning, key point consolidation, key point analysis, and extension and expansion. After the intervention, the nurses were compared for their autonomous learning ability and their knowledge, belief, and action levels of EIDs, and their degree of satisfaction with the training model was evaluated. SPSS 20.0 and GraphPad Prism 9.3.0 were used for non-parametric Wilcoxon paired tests of quartiles ( Q25, M, and Q75). Results:After the training, the emergency department nurses had significantly higher scores in overall autonomous learning ability ( Z=-4.69, P<0.001) and in each dimension: concept of self-motivation ( Z=-4.48, P<0.001), task analysis ( Z=-4.06, P<0.001), self-monitoring and regulation ( Z=-4.50, P<0.001), and self-evaluation ( Z=-4.82, P<0.001); they had significantly higher scores in overall knowledge, belief, and action levels of EIDs ( Z=-4.65, P<0.001) and in each dimension: knowledge ( Z=-4.04, P<0.001), belief ( Z=-3.24, P=0.001), and action ( Z=-4.05, P<0.001). Emergency department nurses had a high degree of satisfaction with the training model (degree of satisfaction for each item >90%). Conclusions:The micro-video tracing training model can effectively improve the autonomous learning ability of emergency department nurses and their knowledge, belief, and action levels of EIDs, with a high degree of satisfaction among the nurses, making it promising for wide use.

Cocaine,as a widely abused and highly addictive drug,has a serious impact on the physical and mental health of individuals and carries a certain degree of social harm and economic burden.Acupuncture can assist in the treatment of cocaine addiction with fewer side effects.However,a well-defined mode of stimulation is an important factor in elucidating the various mechanisms by which acupuncture treats disease.This paper summarizes the problems in the mechanism of cocaine addiction,such as different parameters of stimulation,unstable depth of acupuncture,different acupoint selection,and different lengths of acupuncture time.To standardize the intervention measures of acupuncture experiments,it is suggested that in future research,the stimulation method should explore the best parameters,the selection of acupoints should be based on clinical practice,the timing of acupuncture should be objective,and the treatment course should consider the effects of acupuncture.

Objective:To explore the relationship between normal serum creatinine(Scr) level and diabetic kidney disease(DKD) in patients with type 2 diabetes mellitus(T2DM).Methods:This was a prospective cohort study. Patients with yet not DKD who were regularly followed up at six centers of Li′s United Clinic in Taiwan, China from January 1, 2002 to December 31, 2018 were selected. At baseline, clinic information and lab tests were collected. According to whether the patients developed DKD during the follow-up period, they were divided into DKD group and non-DKD(NDKD) group. The exposure factor was the Scr(μmol/L) value, and it was used as a categorical variable. According to the quartiles of Scr, they were divided into 4 groups: Q1 group(Scr<57.68 μmol/L), Q2 group(57.68 μmol/L≤Scr<68.51 μmol/L), Q3 group(68.51 μmol/L≤Scr<80.44 μmol/L) and Q4 group(Scr≥80.44 μmol/L). The Cox regression model was used to explore the relationship between Scr level and the incidence of DKD. Receiver operating characteristic(ROC) curve was used to analyze the predictive effect of normal level Scr on DKD. Results:A total of 2 202 T2DM patients without DKD at baseline were included. After a follow-up period of(5.2±2.17) years, there were 966 patients in the DKD group and 1 236 patients in the NDKD group. Compared with the NDKD group, the DKD group had older age, longer duration of diabetes, higher BMI, SBP, DBP, LDL-C, Scr, and UACR(all P<0.05). Cox regression analysis results showed that compared with the Q1 group as the reference, the risk of developing DKD in the Q2, Q3, and Q4 groups after adjusting for confounding factors was 1.394, 1.688, and 2.821 times higher, respectively(all P<0.05). ROC curve analysis results showed that the area under the curve(AUC) for predicting DKD occurrence using normal serum creatinine level was 0.70(95% CI 0.678-0.722), with an optimal cutoff value of 74.27 μmol/L, sensitivity of 0.54, and specificity of 0.76. The cumulative risk plot showed that after adjusting for confounding factors, patients in the Q4 group had a higher cumulative risk of developing DKD compared to the Q1, Q2, and Q3 groups(all P<0.05). Conclusion:Scr is an independent risk factor for developing DKD in patients with T2DM. The higher the Scr level, the greater the risk, especially when Scr is greater than 74.27 μmol/L.

Objective:To construct an evidence-based prediction model for early recurrence after surgery of hepatocellular carcinoma (HCC) based on Meta-analysis and to do external validation study.Methods:The literatures in Chinese National Knowledge Infrastructure, Wanfang, VIP, Chinese Science Citation Database (CSCD), Chinese Social Science Citation System (CCSCI), PubMed, Web of Science and IEEE databases between January 2019 and December 2023 were searched based on the subject words. According to the inclusion and exclusion criteria, 9 literatures were included to screen the risk factors affecting the early recurrence of HCC. When the same risk factor was found in ≥5 included literatures, Meta-analysis was performed by using Review Manager 5.4.1 software. External validation data were collected from 401 patients with primary HCC who underwent surgery in Liaoning Cancer Hospital between March 2014 and March 2017. The patients were divided into early recurrence group (176 cases) and early non-recurrence group (225 cases) according to whether they relapsed 2 years after surgery. The OR values of all risk factors obtained in the Meta-analysis were converted into modeling, and postoperative early recurrence rate of HCC in the Meta-analysis was used to calculate β 0, and finally the logistic model was obtained. The OR value was incorporated into the logit (P) model, and the morbidity (P) of the external validation data was calculated. Taking the recurrence 2 years after surgery or not as the dependent variable and P as the independent variable, the receiver operating characteristic (ROC) curve was drawn to calculate the area under the curve (AUC). Results:A total of 8 risk factors for early HCC recurrence were screened out from 9 literatures (x 1: alpha-fetoprotein ≥ 400 ng/ml; x 2: tumor number ≥ 2; x 3: the longest tumor diameter ≥ 5 cm; x 4: Barcelona staging B-C; x 5: microvascular invasion; x 6: moderate to low differentiation; x 7: incomplete capsule; x 8: nonanatomic hepatectomy). The Meta-analysis included 1 757 HCC cases, with 960 postoperative early recurrences and an early recurrence rate of 45.36%, finally the β 0 value was -0.201. The predictive model for 2-year recurrence of HCC was constructed and calculated as logit (P) = -0.201+0.835x 1+0.905x 2+0.783x 3+1.008x 4+0.765x 5+0.831x 6+1.533x 7+0.940x 8. Analysis of variance by external validation data showed that the differences in ascites, alpha-fetoprotein, tumor number, tumor diameter, Barcelona staging, microvascular invasion, tumor differentiation degree, capsule invasion, resection type, and systemic inflammation index were statistically significant between early recurrence group and early non-recurrence group (all P < 0.05). ROC curve analysis showed that AUC of postoperative early recurrence of HCC predicted by the model was 0.718, (95% CI: 0.689-0.753), the optimal cut-off value was 3.11, the Yoden index was 0.288, the sensitivity was 69.32%, and the specificity was 69.56%. Conclusions:The evidence-based prediction model constructed based on Meta-analysis for postoperative early recurrence of HCC has a high predictive value. However, further verification and optimization with big data is still needed.

Objective To explore the predictive effect of empowerment and theory of planned behavior(TPB)on the four core behaviors of type 2 diabetes mellitus(T2DM)self-management,and analyze the influence path of TPB model and behavioral intention on the four core behaviors.Methods A total of 500 T2DM patients who were hospitalized in the Endocrinology Department of three tertiary general hospitals in different provinces from December 2022 to May 2023 were selected for investigation.Hierarchical multiple regression analysis was used to explore the predictive effects of empowerment and TPB models on self-management behaviors.Bootstrap method was used to analyze the influence path of TPB model and behavior intention on self-management behaviors.Results Empowerment had a predictive effect on self-management behavior,which was improved after the addition of TPB,with medication R2=0.194,blood glucose monitoring R2=0.308,regular diet R2=0.337 and regular exercise R2=0.343,respectively.Mediation effect analyses revealed that attitude,subjective norm,and perceived behavioral control were observed to predict behavior intention across the four behaviors,and the three except through the behavior intention indirectly affect behavior,can also directly affect the behavior.Conclusions Empowerment can predict self-management behavior,and adding TPB to this can improve the prediction effect.Attitude,subjective norms and perceived control can indirectly or directly affect self-management behavior through behavioral intention.

Objective To observe the effects of Angelicae Sinensis Radix-Paeoniae Radix alba combined with bone marrow mesenchymal stem cells(BM-MSCs)transplantation on liver inflammation and hepatocytes regeneration in non-alcoholic steatohepatitis(NASH)associated cirrhosis;To discuss its possible mechanism.Methods West diet combined with carbon tetrachloride was used to prepare the NASH related cirrhosis model.The mice were randomly divided into control group,model group,Angelicae Sinensis Radix-Paeoniae Radix alba group,BM-MSCs group,and Angelicae Sinensis Radix-Paeoniae Radix alba+BM-MSCs group,with 12 mice in each group.After successful modeling,corresponding interventions were given according to grouping for 4 consecutive weeks.HE staining was used to observe the morphology of liver tissue;the contents of serum ALT,AST,TBil,TG,ALB,AFP,as well as the contents of IL-1β,TNF-α and HGF in liver tissue were detected;RT-qPCR was used to detect TLR9,MyD88,TRAF6 and NF-κBp65 mRNA expression in liver cells;primary liver cells were separated,CpG ODN 2216 stimulation was induced in vitro,cells supernatant was extracted,and IL-1β and TNF-α content were detected.Results Compared with the control group,inflammatory cell infiltrated in liver tissue of model group mice,accompanied by hepatocyte necrosis and collagen deposition,forming pseudo lobules;the contents of serum ALT,AST,TBil and TG increased,the content of ALB decreased,the content of HGF in liver tissue decreased,the contents of IL-1β and TNF-α increased,with statistical significance(P<0.05);the mRNA expressions of TLR9,MyD88,TRAF6 and NF-κBp65 in liver cells increased(P<0.05),and the contents of IL-1β and TNF-α in cells supernatant increased after CpG ODN 2216 induction(P<0.05).Compared with the model group,the inflammatory infiltration in liver tissue and necrosis of liver cells were reduced and the degree of liver fibrosis was alleviated in the Angelicae Sinensis Radix-Paeoniae Radix alba+BM-MSCs group,the liver tissue damage in Angelicae Sinensis Radix-Paeoniae Radix alba group and BM-MSCs group were slightly improved;except for the TG content in BM-MSCs group,all detection indicators showed significant improvement in each intervention group(P<0.05).Compared with the Angelicae Sinensis Radix-Paeoniae Radix alba group and BM-MSCs group,the Angelicae Sinensis Radix-Paeoniae Radix alba+BM-MSCs group showed statistical significance in related detection indicators(P<0.05),and demonstrated a synergistic effect.Conclusion Angelicae Sinensis Radix-Paeoniae Radix alba combined with BM-MSCs transplantation could effectively improve the liver function and promote liver cell regeneration.The mechanism is associated with the down-regulation of TLR9/NF-κB signaling pathway expressions and function,the inhibition of inflammatory cytokines secretions,and the improvement of liver inflammatory microenviroment.