影像组学在磨玻璃结节型肺腺癌的应用研究进展

肺癌作为全世界发病率及病死率最高的恶性肿瘤,其早期诊断及治疗对患者生存率的提高极为重要.早期肺腺癌在计算机断层扫描(Computed Tomography,CT)中常表现为磨玻璃结节(Ground-Glass Nodule,GGN),精确诊断GGN型肺腺癌对患者后期治疗具有重大价值.影像组学通过将传统的影像图像转换为可...     

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。     

Improving GWAS discovery and genomic prediction accuracy in biobank data

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R² was 47% in a UK Biobank holdout sample, which was 76% of the estimated hSNP2. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.

As biobank datasets increase in size, it is important to understand the factors limiting the prediction of phenotype from genotype. Alongside others, we have recently shown that genomic prediction accuracy can be improved through the use of random-effects models that incorporate prior knowledge of genomic annotations and allow for differences in the variance explained by single-nucleotide polymorphism (SNP) markers, depending upon their linkage disequilibrium (LD) and their minor allele frequency (MAF) (1–8). These improvements in prediction accuracy should also translate into greater genome-wide association study (GWAS) discovery power. Mixed-linear models of association (MLMA) are commonly applied in GWASs in a two-step approach, where a random-effects model is first used to estimate leave-one-chromosome-out (LOCO) genetic values, and these are then used in a second marginal regression coefficient estimation step. Theory suggests that the test statistics obtained in the MLMA second step depend upon the accuracy of the LOCO genomic predictors produced from the first step. Current MLMA implementations use a blocked ridge regression model (9), a Restricted Maximum Likelihood (REML) genomic relationship model (10), or a Bayesian spike-and-slab model (11) within the first step.Here, we improve the computational implementation of our recently developed Bayesian grouped mixture of regressions model (GMRM), which estimates genetic marker effects jointly, but with independent marker inclusion probabilities and independent hSNP2 parameters across LD, MAF, and functional annotation groups (Materials and Methods). This allows us to apply the model to 21 traits in the UK Biobank to test for prediction accuracy improvements over existing approaches. We then extend the model to provide MLMA SNP marker association estimates to test whether improved prediction accuracy translates to improved GWAS discovery compared to existing MLMA approaches.     

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。     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.