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.

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 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 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 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 investigate the metabolic outcomes of primary aldosteronism(PA)patients receiving adrenalectomy(ADX)or spironolactone treatment and the contributing factors to the metabolic outcomes.Methods The clinical data of 70 patients with aldosterone-producing adenoma(APA)and 86 patients with idiopathic hyperaldosteronism(IHA)were retrospectively analyzed.All subjects received confirmatory diagnosis of APA or IHA at the Department of Endocrinology and Metabolism,West China Hospital between March 2018 and October 2020.APA patients underwent ADX,while IHA patients were given spironolactone,a mineralocorticoid receptor antagonist(MRA).After ADX or spironolactone treatment,the outcomes of the metabolic indicators and the inter-group differences between the APA patients and IHA patients were studied.Results There was no significant difference between the baseline data of the APA group and those of the IHA group in terms of age,sex,duration of hypertension,maximum systolic blood pressure(SBP-max),maximum diastolic blood pressure(DBP-max),body mass index(BMI),fasting blood glucose(FBG),lipid parameters,and renal function.IHA patients had higher waist circumference,serum potassium,and plasma renin activity(PRA)than those of the APA patients(all P＜0.05).All patients showed significant improvement in blood pressure,blood potassium,and plasma aldosterone at follow-up.However,they also showed increased triglycerides(TG)accompanied by deterioration in renal function(P≤0.001).Multiple regression showed that TG levels were associated with spironolactone treatment for IHA patients and post-treatment BMI and creatinine levels.Furthermore,APA patients showed improvement in their FBG after ADX(P=0.041),while IHA patients showed elevated levels of FBG after spironolactone treatment(P=0.037).Conclusion After treatment,PA patients still may experience abnormal lipid metabolism and deteriorating renal function.Spironolactone therapy may give rise to worse glucolipid metabolism than ADX therapy does.

Objective: This study used cardiac magnetic resonance imaging (MRI) to compare the characteristics of left ventricular remodeling in patients with primary aldosteronism (PA) with those of patients with essential hypertension (EH) and healthy controls (HCs). Materials and Methods: This prospective study enrolled 35 patients with PA, in addition to 35 age- and sex-matched patients with EH, and 35 age- and sex-matched HCs, all of whom underwent comprehensive clinical and cardiac MRI examinations. The analysis of variance was used to detect the differences in the characteristics of left ventricular remodeling among the three groups. Univariable and multivariable linear regression analyses were used to determine the relationships between left ventricular remodeling and the physiological variables. Results: The left ventricular end-diastolic volume index (EDVi) (mean ± standard deviation [SD]: 85.1 ± 13.0 mL/m2 for PA, 75.9 ± 14.3 mL/m2 for EH, and 77.3 ± 12.8 mL/m2 for HC; p = 0.010), left ventricular end-systolic volume index (ESVi) (mean ± SD: 35.2 ± 9.8 mL/m2 for PA, 30.7 ± 8.1 mL/m2 for EH, and 29.5 ± 7.0 mL/m2 for HC; p = 0.013), left ventricular mass index (mean ± SD: 65.8 ± 16.5 g/m2 for PA, 56.9 ± 12.1 g/m2 for EH, and 44.1 ± 8.9 g/m2 for HC; p < 0.001), and native T1 (mean ± SD: 1224 ± 39 ms for PA, 1201 ± 47 ms for EH, and 1200 ± 44 ms for HC; p = 0.041) values were higher in the PA group compared to the EH and HC groups. Multivariable linear regression demonstrated that log (plasma aldosteroneto-renin ratio) was independently correlated with EDVi and ESVi. Plasma aldosterone was independently correlated with native T1. Conclusion Patients with PA showed a greater degree of ventricular hypertrophy and enlargement, as well as myocardial fibrosis, compared to those with EH. Cardiac MRI T1 mapping can detect left ventricular myocardial fibrosis in patients with PA.

Objective: This study used cardiac magnetic resonance imaging (MRI) to compare the characteristics of left ventricular remodeling in patients with primary aldosteronism (PA) with those of patients with essential hypertension (EH) and healthy controls (HCs). Materials and Methods: This prospective study enrolled 35 patients with PA, in addition to 35 age- and sex-matched patients with EH, and 35 age- and sex-matched HCs, all of whom underwent comprehensive clinical and cardiac MRI examinations. The analysis of variance was used to detect the differences in the characteristics of left ventricular remodeling among the three groups. Univariable and multivariable linear regression analyses were used to determine the relationships between left ventricular remodeling and the physiological variables. Results: The left ventricular end-diastolic volume index (EDVi) (mean ± standard deviation [SD]: 85.1 ± 13.0 mL/m2 for PA, 75.9 ± 14.3 mL/m2 for EH, and 77.3 ± 12.8 mL/m2 for HC; p = 0.010), left ventricular end-systolic volume index (ESVi) (mean ± SD: 35.2 ± 9.8 mL/m2 for PA, 30.7 ± 8.1 mL/m2 for EH, and 29.5 ± 7.0 mL/m2 for HC; p = 0.013), left ventricular mass index (mean ± SD: 65.8 ± 16.5 g/m2 for PA, 56.9 ± 12.1 g/m2 for EH, and 44.1 ± 8.9 g/m2 for HC; p < 0.001), and native T1 (mean ± SD: 1224 ± 39 ms for PA, 1201 ± 47 ms for EH, and 1200 ± 44 ms for HC; p = 0.041) values were higher in the PA group compared to the EH and HC groups. Multivariable linear regression demonstrated that log (plasma aldosteroneto-renin ratio) was independently correlated with EDVi and ESVi. Plasma aldosterone was independently correlated with native T1. Conclusion Patients with PA showed a greater degree of ventricular hypertrophy and enlargement, as well as myocardial fibrosis, compared to those with EH. Cardiac MRI T1 mapping can detect left ventricular myocardial fibrosis in patients with PA.

Objective: To discuss the effect of high-density fat-binding SVF-GEL in female facial lipofilling. Methods: This is a retrospective study including 32 female patients, received facial fat transplantation during June 2017 to June 2018 in Yichun College. Each patient underwent high-density fat-binding SVF-GEL transplantation for facial surgery. Patients′satisfaction with the surgery and the rate of secondary surgery was evaluated. Fat was harvested from the inner thigh, centrifuged at 1200 g for 3 min, and the liquid was removed. The upper 2/3 part is prepared for SVF-GEL, for further used in delicate lipofilling in eyelid, tear groove and nasolabial groove. The lower 1/3 high density fat was used for volume restoration, such as forehead, temporal area and cheek. Results: All patients had significant improvements in facial contours with mild swelling and short recovery time. The satisfaction rate was 68.8%(22/32), and the second operation rate was 15.6%(5/32). Conclusions High-density fat-binding SVF-GEL transplantation can achieve good results in correcting facial volume loss.

Objective To summarize the prenatal ultrasonographic characteristics of anomalous origin of one pulmonary artery from the aorta( AOPA ) ,and describe the diagnostic and clinical outcomes of fetal AOPA . Methods Echocardiographic characteristics of 3 fetuses with AOPA were reviewed . The ultrasonographic features were comparatively analyzed with postpartum autopsy findings . The relevant literature were reviewed and the experience of prenatal diagnosis of AOPA were summarized . Results In 3 cases with AOPA ,2 cases were the proximal type and anomalous origin of right pulmonary artery from the aorta ,of them ,one was accompanied with coarctation of the aorta , the other was accompanied with aortopulmonary window . One case was the distal type and anomalous origin of left pulmonary artery from the left innominate aorta ,it was only associated with mirror right aortic arch and right ductus arteriosus . Ultrasound characteristics were no bifurcation in distal pulmonary artery ,main trunk of pulmonary artery extends directly to one branch ,the other branch originated from the ascending aorta or left innominate artery . Three cases were confirmed by postpartum autopsy . Conclusions There are some characteristic signs on ultrasonic features of AOPA . Prenatal ultrasound has important value for diagnosis of AOPA .