Abstract:Objective To evaluate the effects of modified uhrafihration （MUF）on blood rheology in infants after open cardiac surgery. Methods According to admission number, 22 infants of body weight less than 10 kilograms with ventricular septal defect （VSD） and pulmonary hypertension （PH） were divided into control group （10 infants, the mantissa of their admission number was odd number） and experimental group （12 infants, the mantissa of their admission number was even number）. Cases in control group didn＇t undergo MUF at the end of cardiopulmonary bypass （CPB）, while cases in experimental group underwent MUF; the flow rate of MUF ranged from 10 ml/min · kg to 15 ml/min · kg. MUF lasting for 10-15 minutes. Blood samples were repeatedly collected as following time： before operation, at the end of CPB, 15 minutes after CPB or the end of MUF, 2, 24 h after operation. Blood sample of 2. 5 ml was collected from the radial artery with hepathrom test-tube. The changes of relative indexes of the blood rheology were observed by MDK-3200 completely automatic dual pathways blood rheology testing analysator at 37±1 C. Results Hemoglobin, hematocrit, red cell count, blood yielding stress, plasma viscosity, the whole blood viscosity at high shear rate, the whole blood viscosity at middle shear rate and low shear rate, the whole blood reduction viscosity at high shear rate and middle shear rate, the whole blood reduction viscosity at low shear rate and Casson viscosity in experimental group at the end of MUF were significantly higher than those in control group at 15 minutes after CPB （P〈0. 05）. There was no significant difference in red cell aggregation index and red cell deformity between two groups at each moment （P 〉 0.05 ）. Conclusion Hemoglobin, hematocrit and red cell count are significantly elevated through MUF after CPB. Whole blood viscosity in infants undergone open cardiac surgery after CPB with MUF is higher than those who didn＇t undergo MUF.
Hemispheric asymmetry is a fundamental organizing principle of the human brain. Answering the genetic effects of the asymmetry is a prerequisite for elucidating developmental mechanisms of brain asymmetries. Multi-modal magnetic resonance imaging (MRI) has provided an important tool for comprehensively interpreting human brain asymmetry and its genetic mechanism. By combining MRI data, individual differences in brain structural asymmetry have been investigated with quantitative genetic brain mapping using gene-heritability. Twins provide a useful natural model for studying the effects of genetics and environment on the brain. Studies based on MRI have found that the asymmetry of human brain structure has a genetic basis. From the perspective of quantitative genetic analysis, this article reviews recent findings on the genetic effects of asymmetry and genetic covariance between hemispheres from three aspects: the asymmetry of heritability, the heritability of asymmetry and the genetic correlation. At last, the article shows the limitations and future research directions in this field. The purpose of this systematic review is to quickly guide researchers to understand the origins and genetic mechanism of interhemispheric differences, and provide a genetic basis for further understanding and exploring individual differences in laterized cognitive behavior.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.
Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it’s difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.