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 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 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 This study sought to establish a diarrhea-predominant irritable bowel syndrome(IBS-D)mouse model by gavage different mass concentrations sennae folium combined with chronic restraint stress,and to determine the appropriate mass concentration of sennae folium to establish IBS-D mouse model.Methods The mass concentration of sennae folium used for the IBS-D mouse model followed suggested amounts in the literature and on that basis,the mass concentration gradient was established prior to conducting the experiment.Female C57BL/6 mice were divided into a normal group(Group N),a low-dose group(Group L;0.25 g/mL sennae solution),a medium-dose group(Group M;0.50 g/mL sennae solution),and a high-dose group(Group H;1.0 g/mL sennae solution),with 10 mice per group.After 14 days,the defecation,diarrhea index,visceral sensitivity,and morphological changes in the colonic tissue in each group were observed and recorded to compare the differences among models established with varying mass concentrations of sennae folium.Results Compared with Group N(42.90±11.90)%,Group L(80.30±5.77)%,Group M(80.50±3.44)%,and Group H(81.90±2.68)%had significantly higher 6 h fecal water content(P＜0.01).Compared with Group N(0.00±0.00),the diarrhea index of mice in Group L(0.57±0.16),Group M(0.62±0.23),and Group H(0.60,0.23)also increased significantly(P＜0.01).Compared with Group N(0.65(0.60,0.65)),Group M(0.32(0.24,0.39))and Group H(0.34(0.27,0.47))had significantly lower visceral pain threshold and higher visceral sensitivity(P＜0.01).Additionally,the first blue stool time in Group M(98.15(93.41,100.44)min)was significantly shorter than that in Group N(186.81(109.28,192.05)min)(P＜0.01),and the total number of stools in Group M(22.4±3.73)was significantly higher than that in Group N(17.90±4.48)(P＜0.05).Conclusions Compared with 0.25 and 1.0 g/mL,0.50 g/mL sennae folium gavage,combined with chronic restraint stress,can better simulate the clinical symptoms of IBS-D.

Parkinson disease (PD) is a complex neurodegenerative disorder characterized by a variety of motor and non-motor symptoms. Many studies have shown that the transmembrane protein 175 (TMEM175) gene may be a potential target for the treatment of PD and other neurodegenerative disorders, but the specific pathogenic mechanism remains unclear. TMEM175 is a lysosomal protein-coding gene that encodes a lysosomal proton channel protein. This article reviews the research advances in the characterization of the TMEM175 gene and its encoded proteins, the clinical features of mutant PD, and related pathogenic mechanism. It is shown that the TMEM175 gene has an impact on the pathogenesis of PD, and patients with different mutation sites tend to have different ages of onset and clinical features. Compared with the patients without TMEM175 mutations, the patients with TMEM175 mutations tend to have an earlier age of onset, more severe motor symptoms, and more susceptibility to cognitive impairment and non-motor symptoms. This article systematically reviews the TMEM175 gene, in order to assist in the early diagnosis of PD and the discovery of new disease-modifying therapies and treatment strategies.
Parkinson Disease

Objective To investigate the protective effect of epigallocatechin gallate(EGCG)on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced Parkinson's disease model mice.Methods Twenty-eight male C57BL/6J mice aged 6～8 weeks were randomly divided into four groups:the control group,the model group,the low-dose EGCG group[25 mg/(kg·d)],and the high-dose EGCG group[50 mg/(kg·d)].A Parkin-son's disease(PD)mouse model was established by intraperitoneal injection of MPTP at a dose of 30 mg/(kg·d)for 7 consecutive days.The protective effect of EGCG on MPTP-induced Parkinson's model mice was analyzed through behavioral index detection and Western blot method.Results(1)In the behavioral tests,compared with the model group,the movement distance and speed of mice treated with low-and high-dose EGCG were significantly improved(both P values<0.001).The mice in the high-dose EGCG treatment group also showed a significant advantage in the percentage of the central path distance(P<0.001).(2)Compared with the control group,the deposition of α-synuclein in the model group increased significantly(P<0.001).Compared with the model group,both the low-and high-dose EGCG groups reduced the deposition of α-synuclein(both P<0.001).(3)Compared with the control group,the expression levels of Beclin 1 and LC3 proteins in the substantia nigra region of mice in the model group decreased significantly(both P<0.001),while the expression level of p62 protein increased significantly(P<0.001).After treatment with EGCG,compared with the model group,the expression levels of Beclin 1 and LC3 proteins in mice of the low-dose EGCG group increased to varying degrees(P<0.01;P<0.001),and the expression level of p62 protein decreased significantly(P<0.001).In the high-dose EGCG group,the expression levels of Beclin 1 and LC3 proteins increased significantly(both P<0.001),and the expression level of p62 protein decreased significantly(P<0.001).Conclusion EGCG reduces alpha-synuclein deposition via the autophagy-lysosomal pathway and protects against MPTP-induced Parkinson's disease model mice.

Inflammatory breast cancer(IBC)is a rare but highly aggressive subtype of breast cancer characterized by rapid clinical progression and poor prognosis.Although it accounts for only 2%-4%of all breast cancer cases,it is responsible for 8%-10%of breast cancer-related mortality.The etiology of IBC is multifactorial,involving genetic,hormonal,environmental,and socioeconomic factors.Pathologically,IBC is marked by the presence of dermal lymphatic tumor emboli,and molecular subtypes are predominantly HER2-positive and triple-negative,indicating high tumor invasiveness.Diagnosis relies on characteristic clinical manifestations and histopathological confirmation,while imaging techniques such as MRI and PET/CT play important roles in evaluating disease extent and metastasis.Given that IBC is often diagnosed at a locally advanced or metastatic stage,there is currently no specific treatment protocol.Instead,management generally follows the treatment paradigm of non-IBC,emphasizing systemic therapy within a multidisciplinary framework.HER2-positive IBC benefits from chemotherapy combined with dual-targeted anti-HER2 therapy;triple-negative IBC may respond to immune checkpoint inhibitors;and CDK4/6 inhibitors show potential efficacy in hormone receptor-positive subtypes.Despite advancements,the prognosis remains poor,with a high risk of early recurrence and distant metastasis.Prognostic factors include lymph node involvement,molecular subtype,and response to neoadjuvant therapy.As research into the tumor microenvironment and molecular mechanisms deepens,targeted and individualized therapies hold promise for improving outcomes.This review summarizes the epidemiology,pathology,diagnostic criteria,treatment strategies,and prognostic factors of IBC,aiming to inform clinical practice and future research.

Inflammatory breast cancer(IBC)is a rare but highly aggressive subtype of breast cancer characterized by rapid clinical progression and poor prognosis.Although it accounts for only 2%-4%of all breast cancer cases,it is responsible for 8%-10%of breast cancer-related mortality.The etiology of IBC is multifactorial,involving genetic,hormonal,environmental,and socioeconomic factors.Pathologically,IBC is marked by the presence of dermal lymphatic tumor emboli,and molecular subtypes are predominantly HER2-positive and triple-negative,indicating high tumor invasiveness.Diagnosis relies on characteristic clinical manifestations and histopathological confirmation,while imaging techniques such as MRI and PET/CT play important roles in evaluating disease extent and metastasis.Given that IBC is often diagnosed at a locally advanced or metastatic stage,there is currently no specific treatment protocol.Instead,management generally follows the treatment paradigm of non-IBC,emphasizing systemic therapy within a multidisciplinary framework.HER2-positive IBC benefits from chemotherapy combined with dual-targeted anti-HER2 therapy;triple-negative IBC may respond to immune checkpoint inhibitors;and CDK4/6 inhibitors show potential efficacy in hormone receptor-positive subtypes.Despite advancements,the prognosis remains poor,with a high risk of early recurrence and distant metastasis.Prognostic factors include lymph node involvement,molecular subtype,and response to neoadjuvant therapy.As research into the tumor microenvironment and molecular mechanisms deepens,targeted and individualized therapies hold promise for improving outcomes.This review summarizes the epidemiology,pathology,diagnostic criteria,treatment strategies,and prognostic factors of IBC,aiming to inform clinical practice and future research.

Objective To investigate the protective effect of epigallocatechin gallate(EGCG)on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced Parkinson's disease model mice.Methods Twenty-eight male C57BL/6J mice aged 6～8 weeks were randomly divided into four groups:the control group,the model group,the low-dose EGCG group[25 mg/(kg·d)],and the high-dose EGCG group[50 mg/(kg·d)].A Parkin-son's disease(PD)mouse model was established by intraperitoneal injection of MPTP at a dose of 30 mg/(kg·d)for 7 consecutive days.The protective effect of EGCG on MPTP-induced Parkinson's model mice was analyzed through behavioral index detection and Western blot method.Results(1)In the behavioral tests,compared with the model group,the movement distance and speed of mice treated with low-and high-dose EGCG were significantly improved(both P values<0.001).The mice in the high-dose EGCG treatment group also showed a significant advantage in the percentage of the central path distance(P<0.001).(2)Compared with the control group,the deposition of α-synuclein in the model group increased significantly(P<0.001).Compared with the model group,both the low-and high-dose EGCG groups reduced the deposition of α-synuclein(both P<0.001).(3)Compared with the control group,the expression levels of Beclin 1 and LC3 proteins in the substantia nigra region of mice in the model group decreased significantly(both P<0.001),while the expression level of p62 protein increased significantly(P<0.001).After treatment with EGCG,compared with the model group,the expression levels of Beclin 1 and LC3 proteins in mice of the low-dose EGCG group increased to varying degrees(P<0.01;P<0.001),and the expression level of p62 protein decreased significantly(P<0.001).In the high-dose EGCG group,the expression levels of Beclin 1 and LC3 proteins increased significantly(both P<0.001),and the expression level of p62 protein decreased significantly(P<0.001).Conclusion EGCG reduces alpha-synuclein deposition via the autophagy-lysosomal pathway and protects against MPTP-induced Parkinson's disease model mice.

Objective This study sought to establish a diarrhea-predominant irritable bowel syndrome(IBS-D)mouse model by gavage different mass concentrations sennae folium combined with chronic restraint stress,and to determine the appropriate mass concentration of sennae folium to establish IBS-D mouse model.Methods The mass concentration of sennae folium used for the IBS-D mouse model followed suggested amounts in the literature and on that basis,the mass concentration gradient was established prior to conducting the experiment.Female C57BL/6 mice were divided into a normal group(Group N),a low-dose group(Group L;0.25 g/mL sennae solution),a medium-dose group(Group M;0.50 g/mL sennae solution),and a high-dose group(Group H;1.0 g/mL sennae solution),with 10 mice per group.After 14 days,the defecation,diarrhea index,visceral sensitivity,and morphological changes in the colonic tissue in each group were observed and recorded to compare the differences among models established with varying mass concentrations of sennae folium.Results Compared with Group N(42.90±11.90)%,Group L(80.30±5.77)%,Group M(80.50±3.44)%,and Group H(81.90±2.68)%had significantly higher 6 h fecal water content(P＜0.01).Compared with Group N(0.00±0.00),the diarrhea index of mice in Group L(0.57±0.16),Group M(0.62±0.23),and Group H(0.60,0.23)also increased significantly(P＜0.01).Compared with Group N(0.65(0.60,0.65)),Group M(0.32(0.24,0.39))and Group H(0.34(0.27,0.47))had significantly lower visceral pain threshold and higher visceral sensitivity(P＜0.01).Additionally,the first blue stool time in Group M(98.15(93.41,100.44)min)was significantly shorter than that in Group N(186.81(109.28,192.05)min)(P＜0.01),and the total number of stools in Group M(22.4±3.73)was significantly higher than that in Group N(17.90±4.48)(P＜0.05).Conclusions Compared with 0.25 and 1.0 g/mL,0.50 g/mL sennae folium gavage,combined with chronic restraint stress,can better simulate the clinical symptoms of IBS-D.