Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy.

Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.

Study on galactosylated sodium alginate for enhancing HepG2 Cells adhesion and 3D printability.

Sodium alginate is a polyanionic natural polysaccharide polymer widely used in tissue engineering. However, the lack of binding domains for interaction with cells limits its application in regenerative medicine. This study designed a kind of galactosylated sodium alginate (G-SA) material with improved galactose grafting rate by EDC/NHS activation of carboxyl groups in MES buffer and subsequently cross-linking by Ca2+ aims to enhance the adherence behavior of HepG2 cells on alginate substrate. The synthesized G-SA was characterized by Fourier transform infrared spectra and nuclear magnetic resonance spectroscopy. G-SA exhibited good biocompatibility and significantly enhanced the adhesion behavior of HepG2 cells on its surface. Furthermore, we demonstrated that the effect of G-SA concentration in enhancing cell adhesion was diminished at higher than 2% w/v. Finally, the suitability of G-SA material is investigated for 3D printing, demonstrating that HepG2 cells could maintain high viability and excellent printability in the interior of the gel. In addition, cells could multiply and grow into cell spheroids with an average size of 200 µm in G-SA scaffolds. These results indicated that galactosylated sodium alginate material could be used as a 3D culture system that could be effective for engineering liver cancer models.

Dynamic gestational week prediction model for pre-eclampsia based on ID3 algorithm.

Pre-eclampsia (PE) is a type of hypertensive disorder during pregnancy, which is a serious threat to the life of mother and fetus. It is a placenta-derived disease that results in placental damage and necrosis due to systemic small vessel spasms that cause pathological changes such as ischemia and hypoxia and oxidative stress, which leads to fetal and maternal damage. In this study, four types of risk factors, namely, clinical epidemiology, hemodynamics, basic biochemistry, and biomarkers, were used for the initial selection of model parameters related to PE, and factors that were easily available and clinically recognized as being associated with a higher risk of PE were selected based on hospital medical record data. The model parameters were then further analyzed and screened in two subgroups: early-onset pre-eclampsia (EOPE) and late-onset pre-eclampsia (LOPE). Dynamic gestational week prediction model for PE using decision tree ID3 algorithm in machine learning. Performance of the model was: macro average (precision = 76%, recall = 73%, F1-score = 75%), weighted average (precision = 88%, recall = 89%, F1-score = 89%) and overall accuracy is 86%. In this study, the addition of the dynamic timeline parameter "gestational week" made the model more convenient for clinical application and achieved effective PE subgroup prediction.

Dynamic risk prediction models for different subtypes of hypertensive disorders in pregnancy.

Background: Hypertensive disorders in pregnancy (HDP) are diseases that coexist with pregnancy and hypertension. The pathogenesis of this disease is complex, and different physiological and pathological states can develop different subtypes of HDP. Objective: To investigate the predictive effects of different variable selection and modeling methods on four HDP subtypes: gestational hypertension, early-onset preeclampsia, late-onset preeclampsia, and chronic hypertension complicated with preeclampsia. Methods: This research was a retrospective study of pregnant women who attended antenatal care and labored at Beijing Maternity Hospital, Beijing Haidian District Maternal and Child Health Hospital, and Peking University People's Hospital. We extracted maternal demographic data and clinical characteristics for risk factor analysis and included gestational week as a parameter in this study. Finally, we developed a dynamic prediction model for HDP subtypes by nonlinear regression, support vector machine, stepwise regression, and Lasso regression methods. Results: The AUCs of the Lasso regression dynamic prediction model for each subtype were 0.910, 0.962, 0.859, and 0.955, respectively. The AUC of the Lasso regression dynamic prediction model was higher than those of the other three prediction models. The accuracy of the Lasso regression dynamic prediction model was above 85%, and the highest was close to 92%. For the four subgroups, the Lasso regression dynamic prediction model had the best comprehensive performance in clinical application. The placental growth factor was tested significant (P < 0.05) only in the stepwise regression dynamic prediction model for early-onset preeclampsia. Conclusion: The Lasso regression dynamic prediction model could accurately predict the risk of four HDP subtypes, which provided the appropriate guidance and basis for targeted prevention of adverse outcomes and improved clinical care.

Dynamic prediction model of fetal growth restriction based on support vector machine and logistic regression algorithm.

Background: This study analyzed the influencing factors of fetal growth restriction (FGR), and selected epidemiological and fetal parameters as risk factors for FGR. Objective: To establish a dynamic prediction model of FGR. Methods: This study used two methods, support vector machine (SVM) and multivariate logistic regression, to establish the prediction model of FGR at different gestational weeks. Results: At 20-24 weeks and 25-29 weeks of gestation, the effect of the multivariate Logistic method on model prediction was better. At 30-34 weeks of gestation, the prediction effect of FGR model using the SVM method is better. The ROC curve area was above 85%. Conclusions: The dynamic prediction model of FGR based on SVM and logistic regression is helpful to improve the sensitivity of FGR in pregnant women during prenatal screening. The establishment of prediction models at different gestational ages can effectively predict whether the fetus has FGR, and significantly improve the clinical treatment effect.