Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia.

Hyperhomocysteinemia (HHcy) is a condition closely associated with cardiovascular and cerebrovascular diseases. Detecting its risk factors and taking some relevant interventions still represent the top priority to lower its prevalence. Yet, in discussing risk factors, Logistic regression model is usually adopted but accompanied by some defects. In this study, a Tabu Search-based BNs was first constructed for HHcy and its risk factors, and the conditional probability between nodes was calculated using Maximum Likelihood Estimation. Besides, we tried to compare its performance with Hill Climbing-based BNs and Logistic regression model in risk factor detection and discuss its prospect in clinical practice. Our study found that Age, sex, α1-microgloblobumin to creatinine ratio, fasting plasma glucose, diet and systolic blood pressure represent direct risk factors for HHcy, and smoking, glycosylated hemoglobin and BMI constitute indirect risk factors for HHcy. Besides, the performance of Tabu Search-based BNs is better than Hill Climbing-based BNs. Accordingly, BNs with Tabu Search algorithm could be a supplement for Logistic regression, allowing for exploring the complex network relationship and the overall linkage between HHcy and its risk factors. Besides, Bayesian reasoning allows for risk prediction of HHcy, which is more reasonable in clinical practice and thus should be promoted.

VHL syndrome without clear family history: A rare case report and literature review of Chinese patients.

Objective: To analyze the clinical manifestations and imaging features of a hospitalized patient with intermittent headache who was finally diagnosed with von Hippel-Lindau (VHL) syndrome and to perform whole-exon gene detection to improve the understanding of the diagnosis and treatment strategies of the disease. Methods: A case of suspected VHL syndrome in Shanxi Provincial People's Hospital was analyzed. Proband DNA was also extracted for whole exome sequencing and screened for causative mutation sites, which were validated by Sanger sequencing. The literature about VHL gene mutations in Chinese patients in the past 10 years were also reviewed. Results: There is a heterozygous mutation site c.499C > G on the VHL gene on the short arm of chromosome 3 of the patient, which is a missense mutation. The mutation results in the substitution of arginine with glycine at amino acid 167 of the encoded protein, which may be primarily responsible for the disease in the patient with VHL syndrome. However, the mutation did not occur in other family members. Conclusion: Early recognition and treatment of VHL syndrome can be available with genetic testing technology. Strengthening the understanding of this complex genetic disease and improving the diagnostic rate of VHL syndrome are helpful for the precise treatment of patients with this disease, which may help prolong the survival time of patients to a certain extent and improve their quality of life.

Using random forest algorithm for glomerular and tubular injury diagnosis.

Objectives: Chronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD. Methods: Demographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively. Results: A total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively. Conclusion: RF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice.

Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province.

Introduction: Chronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China's rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients' quality of life. Methods: From April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively. Results: 12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD. Conclusion: ML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate.

SENP3 regulates high glucose-induced endothelial dysfunction via ROS dependent signaling.

BACKGROUND: The current study aimed to explore the role of SENP3 in endothelial cell dysfunction in a high-glucose setting. METHODS: The gene and protein expressions of SENP3 in high-glucose cultured HAECs were examined using quantitative PCR and western blotting. The effects of SENP3 on HAEC viability, apoptosis, migration, and endothelial-monocyte adhesion were evaluated in vitro by knockdown. Moreover, a mouse streptozotocin-induced type I diabetes model was established for SENP3 expression assessment. In addition, the effects of SENP3 on ROS-related signaling pathways were investigated in high-glucose cultured HAECs. RESULTS: Significantly increased levels of SENP3 mRNA and protein were found in high-glucose cultured HAECs in a time-dependent manner. SENP3 knockdown reversed high glucose-induced HAEC viability, apoptosis, and migration reduction. SENP3 knockdown attenuated the high glucose-induced intercellular adhesion of THP-1 monocytic cells and HAECs via downregulation of ICAM-1 and VCAM-1 expression. Increased levels of SENP3, ICAM-1, and VCAM-1 expression were observed in the aorta tissue of mice with type I diabetes. Downregulation of SENP3 expression was observed in HAECs cultured with high glucose levels using the free radical scavenger N-acetyl-L-cysteine or NOX4 siRNA. CONCLUSIONS: SENP3 was involved in high glucose-induced endothelial dysfunction, and ROS-dependent signaling served as the mechanism.