免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

Evaluation of environmentally conscious manufacturing programs using a three-hybrid multi-criteria decision analysis method

Environmental management, being an important component in strategies for achieving sustainable development of processes and products, has emerged as a proactive approach in majority of the manufacturing organizations. From the strategic perspective environmentally conscious manufacturing (ECM) programs lead to better environmental management practice. The objective of the current paper is to present an integrated and holistic framework to evaluate ECM programs. This framework combines three multi-criteria decision analysis (MCDA) methods to consider eight major environmentally conscious manufacturing indicators (ECMI) in order to identify the efficiency of each ECM program. First the interdependence relationship among the ECMIs is established using decision-making trail and evaluation laboratory (DEMATEL). Then a range of weightage (i.e. upper and lower bounds) is created for each ECMI using analytic network process (ANP) to include managerial preferences. Finally, this range of weightage for each indicator is applied to perform restricted multiplier data envelopment analysis (RMDEA). Results show that the technical efficiency of the inefficient ECM programs for integrated RMDEA, on average, is calculated as 53.2% whereas traditional input oriented DEA provides the same score as 72.3%. This clearly indicates that integrated RMDEA is better than the input oriented DEA because same level of output could be produced with lesser resources if the ECM programs perform on the frontier. Hence, the advantage of this methodology is that the managerial preferences are successfully implemented through this newly developed hybrid methodology that will help to reduce less resource consumption and lead to better environmental policy.     

A single-spring model predicts the majority of variance in impact force during a fall onto the outstretched hand

The objective of this study was to validate a single-spring model in predicting measured impact forces during an outstretched arm falling scenario. Using an integrated force plate, impact forces were assessed from 10 young adults (5 males; 5 females), falling from planted knees onto outstretched arms, from a random order of drop heights: 3, 5, 7, 10, 15, 20, and 25 cm. A single-spring model incorporating body mass, drop height plus the estimated linear stiffness of the upper extremity (hand, wrist and arm) was used to predict impact force on the hand. We used an analysis of variance linearity test to test the validity of using a linear stiffness coefficient in the model. We used linear regression to assess variance (R²) in experimental impact force predicted by the single-spring model. We derived optimum linear stiffness coefficients for male, female and sex-combined. Our results indicated that the association between experimental and predicted impact forces was linear (P < 0.05). Explain variance in experimental impact force was R² = 0.82 for sex-combined, R² = 0.88 for males and R² = 0.84 for females. Optimum stiffness coefficients were 7436 N/m for sex-combined, 8989 N/m for males and 4527 N/m for females. In conclusion, a linear spring coefficient used in the single-spring model proved valid for predicting impact forces from fall heights up to 25 cm. Results also suggest the use of sex-specific spring coefficients when estimating impact force using the single-spring model. This model may improve impact force to bone strength ratios (factor-of-risk) and prediction of forearm and wrist fracture.     

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.     

Synthesis and in vitro antiproliferative evaluation of novel N-alkylated 6-isobutyl- and propyl pyrimidine derivatives

A series of novel N-alkylated C-6-isobutyl- or -propyl pyrimidine derivatives were synthesized and their antiproliferative effect was evaluated on a panel of tumor cell lines including leukemia cell line K562 and normal diploid human fibroblasts. N-methoxymethylated 5-methylpyrimidin-2,4-dione with di(benzyloxy)isobutyl at C-6 (14b) showed the strongest effect on the cell growth at micromolar concentrations. Mechanisms of action for the lipophilic compound 14b predicted in silico, pointed to its anticancer and antimetastatic potential exerted through inhibition of DNA or RNA polymerases and adhesion molecules. The latter mechanism has been supported in vitro for adherent tumor cell lines.     

Can transposable element copy number distribution parameters be estimated from natural populations of Drosophila melanogaster?

The copy frequency distribution of a transposable element family in a Drosophila melanogaster natural population is generally characterised by the values of the Charlesworths' model parameters α and β (Charlesworth & Charlesworth, 1983). The estimation of these parameters is made using the observed distribution of the occupied sites in a population sample. Several results have been interpreted as due either to the influence of stochastic factors or to deterministic factors (transposition, excision, selection…). The accuracy of this method was tested by estimations performed on samples from simulated populations. The results show that with the sample size usually used for natural population studies, the confidence intervals are too large to reasonably deduce either the element copy number distribution or the values of transposition and excision rate and selective coefficients.