Objective To analyze the effect of psychological intervention and intraoperative operation cooperation on cesarean de-livery outcome of pregnant women with heart disease .Methods 60 cases of pregnant women complicating heart disease in this hos-pital from July 2010 to July 2012 were taken as the research subjects and divided into the control group by the conventional inter-vention and the observation group(30 cases) by the psychological intervention and intraoperative cooperation according to the differ-ent intervention measures .The differences of delivery outcomes and the negative mood scores were compared between the two groups .Results The delivery outcome after receiving psychological intervention and intraoperative cooperation in the observation group was significantly better than that in the control group(P<0 .05);the negative mood scores after the systematic nursing in the observation group were significantly lower than those in the control group patients (P<0 .05) .Conclusion Using the whole course psychological intervention and intraoperative cooperation can effectively improve the maternal and fetal negative outcomes ,reduce anxiety ,depression and other negative moods .

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.