连续性血液净化治疗患儿静脉留置导管感染风险预测体系的构建及应用

目的:探讨连续性血液净化治疗患儿静脉留置导管感染风险因素,据此构建风险预测体系,并检验其实际应用效果,以期为临床预防护理提供依据。方法:选取医院2018年4月—2020年4月收治的400例连续性血液净化治疗患儿,按两组基础资料具有匹配性原则将其分为构建组300例、验证组100例,统计构建组中静脉留置导管感染患儿例数,通过单因素分析、多因素Logistic回归分析筛选静脉留置导管感染的独立危险因素,据此构建风险预测体系,并检验其在验证组中的应用效果。结果:经统计得到,构建组中静脉留置导管感染患儿共66例,感染发生率为22.00%;单因素分析得到,连续性血液净化治疗患儿静脉留置导管感染风险因素有穿刺部位、导管留置时间、插管次数、血流速度、血红蛋白、遵医依从性、抗生素使用时间、操作人员手卫生(P<0.05);多因素Logistic回归分析得到,连续性血液净化治疗患儿静脉留置导管感染独立风险因素有股静脉置管、导管留置时间>7 d、血流速度>180 mL/min、血红蛋白<100 g/L、遵医依从性差、抗生素使用时间>7 d(P<0.05);构建得到连续性血液...     

我国城乡基层“2 + X”家庭医生团队现状与预测研究

目的 预测“十四五”期间我国城乡人口和基层医疗卫生机构“2 + X”家庭医生团队数量,探究我国城乡家庭医生服务覆盖情况,为家庭医生健康发展提供科学依据。方法 依据2013—2019年相关数据,采用GM(1,1)灰色预测模型预测2020—2025年我国城乡人口数和“2 + X”家庭医生团队数量和相关卫技人员数。观察每 2000 服务人口标准下,2018—2025年我国城乡“2 + X”家庭医生团队服务人数和服务覆盖率。结果 2020—2025年我国城镇人口数逐年增长,到2025年增至98 831万人;城市基层医疗卫生机构全科医生数、注册护士数、家庭医生团队数量逐年增长,到2025年分别增至171 712人、848 324人、171 712个;全科医生与护士比逐年上升,到2025年升至1∶4.94;2018—2025年我国城市“2 + X”家庭医生团队服务人口覆盖率逐年上升,到2025年升至35%。2020—2025年我国乡村人口数逐年下降,到2025年降至48 053万人;乡镇基层医疗卫生机构全科医生数、注册护士数、家庭医生团队数量逐年增长,到2025年分别增至447 672人、854 976人、447 672个;全科医生与护士比逐年下降,到2025年降至1:1.91;2018—2025年我国乡村“2 + X”家庭医生团队服务人口覆盖率逐年上升,理论上2022年实现全面覆盖。结论 2018—2025年我国城乡“2 + X”家庭医生团队数量逐年增加,但城市家庭医生缺口较大,应加强全科医学人才培养,增强家庭医生职业吸引力,缩小城乡全科医生薪资差距,优化家庭医生服务覆盖均衡性。     

Numerical Simulation and Experimental Validation of Squeeze Casting of AlSi9Mg Aluminum Alloy Component with a Large Size

The squeeze casting process for an AlSi9Mg aluminum alloy flywheel housing component was numerically simulated using the ProCAST software, and orthogonal simulation tests were designed according to the L16 (4) ⁵ orthogonal test table to investigate the alloy melt flow rule under four factors and four levels each of the pouring temperature, mold temperature, pressure holding time and specific pressure, as well as the distributions of the temperature fields, stress fields and defects. The results showed that the flywheel housing castings in all 16 test groups were fully filled, and the thinner regions solidified more quickly than the thicker regions. Hot spots were predicted at the mounting ports and the convex platform, which could be relieved by adding a local loading device. Due to the different constraints on the cylinder surface and the lower end surface, the solidification was inconsistent, the equivalent stress at the corner junction was larger, and the castings with longer pressure holding time and lower mold temperature had larger average equivalent stress. Shrinkage cavities were mainly predicted at mounting ports, the cylindrical convex platform, the peripheral overflow groove and the corner junctions, and there was also a small defect region at the edge of the upper end face in some test groups.     

Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants

PurposeThe aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.Materials and MethodsOf 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model.ResultsThe XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth.ConclusionWe have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.     

儿童利用不同特质信息预测他人行为

目的考察与分析5岁、7岁儿童和成人时不同特质的稳定性信念,从而为儿童理解不同特质的可能策戎提供可靠的依据。方法采用传统行为预测研究的情景故事法,并结合“行为-类型-行为”的特质推理研究范式,进行个别施测。结果相关样本t检验表明,尽管儿童对物质属性的稳定性信念的形成早于特质属性,但对两者稳定性的理解都随年龄增长而不断增强。此外,儿童能区别对待不同特质,他们认为内部状态（能力和身体属性）比意图更稳定。结论相对于社会属性,儿童更早理解物质属性且采取了相似性的推理策略;在社会属性中,儿童依据不同的推理来理解意图、身体属性和身体能力这几种特质。     

Structural Response Prediction of Thin-Walled Additively Manufactured Parts Considering Orthotropy,Thickness Dependency and Scatter

Besides the design freedom offered by additive manufacturing, another asset lies within its potential to accelerate product development processes by rapid fabrication of functional prototypes. The premise to fully exploit this benefit for lightweight design is the accurate structural response prediction prior to part production. However, the peculiar material behavior, characterized by anisotropy, thickness dependency and scatter, still constitutes a major challenge. Hence, a modeling approach for finite element analysis that accounts for this inhomogeneous behavior is developed by example of laser-sintered short-fiber-reinforced polyamide 12. Orthotropic and thickness-dependent Young’s moduli and Poisson’s ratios were determined via quasi-static tensile tests. Thereof, material models were generated and implemented in a property mapping routine for finite element models. Additionally, a framework for stochastic finite element analysis was set up for the consideration of scatter in material properties. For validation, thin-walled parts on sub-component level were fabricated and tested in quasi-static three-point bending experiments. Elastic parameters showed considerable anisotropy, thickness dependency and scatter. A comparison of the predicted forces with experimentally evaluated reaction forces disclosed substantially improved accuracy when utilizing the novel inhomogeneous approach instead of conventional homogeneous approaches. Furthermore, the variability observed in the structural response of loaded parts could be reproduced by the stochastic simulations.     

Influence Factors and Prediction Model of Bond Strength of Tunnel Fireproof Coating under Freeze–Thaw Cycles

The freeze–thaw resistant performance of a tunnel fireproof coating (TFC) has an important impact on bonding property and durability. The influence of redispersible emulsion powder, polypropylene fiber and air-entraining agent on TFCs was studied. Transverse fundamental frequency and ultrasonic sound velocity were used to evaluate the damage degree of TFC, and the mechanism was revealed by SEM and pore structure. The results show that the most beneficial effect on bond strength of TFC is redispersible emulsion powder, followed by air-entraining agent, and then polypropylene fiber. After freeze–thaw cycles, the cumulative pore volume of micropores in the TFC increases obviously, while the porosity of macropores does not change significantly. A prediction model was proposed, which can calculate the bond strength from the damage degree of TFC under freeze–thaw cycles. The achievement can promote the application of TFC in cold regions.     

Risk factors related to the severity of COVID-19 in Wuhan

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction.Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity.Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level.Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.     

DVH预测模型在VMAT计划培训中的应用

目的:以宫颈癌为例,探索使用剂量体积直方图（DVH）预测模型培训放疗物理师容积旋转调强（VMAT）计划设计的可行性及其效果。方法:随机选取20例宫颈癌测试病例对3名见习物理师进行两个阶段培训（第一阶段15例,第二阶段5例）。每位物理师分别采用两种方法设计VMAT计划:传统人工计划（MP）和基于DVH预测模型引导的自动计划（KBP）。对比不同阶段两种计划靶区和危及器官的各种剂量学参数,并做相应统计分析。结果:与人工计划相比,自动计划在明显提高PGTV靶区V60覆盖（P<0.001）,改善高剂量控制（V66）（P=0.027）的情况下,显著降低膀胱（P<0.001）、直肠（P<0.001）、左右肾（P=0.001和P<0.001）以及左右侧股骨头（P<0.001和P<0.001）等绝大部分正常组织器官的受照剂量。在提高计划合格率的同时,亦减小了计划者间的质量差异。结论:DVH预测模型有助于深化物理师对VMAT初始优化参数设置的理解,加快培训进度,提升VMAT计划设计水平。