炒粳米粉预防能全力引起的脑卒中患者腹泻108例临床观察

目的 观察炒粳米粉对减少脑卒中患者由肠内营养制剂能全力引起腹泻并发症的治疗效果。方法 从2009—2011年入住我院神经内科进行肠内营养治疗的脑卒中患者中选取108例符合条件的病人,随机平分为两组,试验组在能全力中加入炒粳米粉,对照组仅仅使用能全力,观察病人服用能全力后腹泻发生情况。结果 试验组腹泻发生率明显低于对照组（P<0.01）。结论 在能全力中添加炒粳米粉可有效减少脑卒中患者由能全力引起的腹泻。     

The Empiric Use of Palm Kernel Oil in Neonatal Skin Care： Justifiable or Not？

Palm kernel oil is a vegetable oil derived from Elaeis guineensis and widely used in neonatal settings for skin care.It is a good emollient with a beneficial effect in moisturizing the skin and preventing transdermal heat and water loss.However,it’s putative roles in the prevention or treatment of fatty acid deficiency is still controversial.So is its function in the improvement of neurological development from its long-chain polyunsaturated fatty acids? We set out in this review to verify whether its empiric use in this context has any scientific justification and is recommendable.Although there is evidence that it has emollient and moisturizing properties necessary for softening the skin and restoring elasticity,controversy subsists on its other nutritional and neurodevelopmental properties.     

针刀松解配合中药内服治疗冻结肩60例临床疗效的分析

目的 观察针刀松解配合中药内服治疗冻结肩的临床疗效,并探讨针刀松解治疗冻结肩理论依据。方法 60例患者行小针刀粘连松解术,术后给予桃红四物汤加减治疗,并指导患者进行主动和被动的功能锻炼。随访6~12个月,平均7.3个月,对治疗前后的肩关节分别以ASES评分、VAS疼痛评分法、Constant肩关节功能及活动度(ROM)评分法进行对比分析。结果 (1)术后3个月,本组患肩活动明显改善,外展(90.66±14.34)°,前屈(124.21±16.44)°,内收(75±12.31)°;(2)术后3个月VAS疼痛评分(1.81±1.02)分较术前(7.86±1.35)分显著降低;(3)术后未出现肩关节不稳症状;(4)术后3个月Constant评分(52.2±13.2)分较术前(22.4±19.4)分明显改善。结论针刀松解配合中药内服治疗冻结肩,可显著改善患者的疼痛与功能障碍程度,创伤小,疗程短,见效快,疗效可靠,且中远期随访显示疗效稳定。     

Kernel machine SNP-set analysis for censored survival outcomes in genome-wide association studies

In this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)-sets that are predictive of survival with data from genome-wide association studies. We first group typed SNPs into SNP-sets based on genomic features and then apply a score test to assess the overall effect of each SNP-set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP-set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP-SNP interactions (epistasis), and the joint effects of multiple causal variants. By simulating SNP data based on the LD structure of real genes from the HapMap project, we demonstrate that our proposed test is more powerful than the standard single SNP minimum P-value-based test for association studies with censored survival outcomes. We illustrate the proposed test with a real data application.     

Investigation on Amari’s dynamical neural field with global constant inhibition

In this paper, the properties of Amari’s dynamical neural field with global constant inhibition induced by its kernel are investigated. Amari’s dynamical neural field illustrates many neurophysiological phenomena successfully and has been applied to unsupervised learning like data clustering in recent years. In its applications, the stationary solution to Amari’s dynamical neural field plays an important role that the underlying patterns being perceived are usually presented as the excited region in it. However, the type of stationary solution to dynamical neural field with typical kernel is often sensitive to parameters of its kernel that limits its range of application. Different from dynamical neural field with typical kernel that have been discussed a lot, there are few theoretical results on dynamical neural field with global constant inhibitory kernel that has already shown better performance in practice. In this paper, some important results on existence and stability of stationary solution to dynamical neural field with global constant inhibitory kernel are obtained. All of these results show that such kind of dynamical neural field has better potential for missions like data clustering than those with typical kernels, which provide a theoretical basis of its further extensive application.     

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried–dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.     

A unified method for rare variant analysis of gene-environment interactions

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Several region-based tests have been developed to jointly model the marginal effect of rare variants, but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a method to detect GE interactions for rare variants using generalized linear mixed effect model. The proposed method can accommodate either binary or continuous traits in related or unrelated samples. Under this model, genetic main effects, GE interactions, and sample relatedness are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation studies of continuous and binary traits show that the proposed method maintains correct type I error rates and appropriate power under various scenarios, such as genotype main effects and GE interaction effects in opposite directions and varying the proportion of causal variants in the model. We apply our method in the Framingham Heart Study to test GE interaction of smoking on body mass index or overweight status and replicate the Cholinergic Receptor Nicotinic Beta 4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.     

An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.     

紫苏叶饮片与其免煎颗粒治疗风寒感冒初起的临床效果对比分析

目的探讨紫苏叶饮片与其免煎颗粒治疗风寒感冒初起的临床效果。方法选取2018年5月15日～9月20日接诊的114例风寒感冒初起患者,随机分为观察组57例(紫苏叶免煎颗粒)、对照组57例(紫苏叶饮片)。治疗结束后对比两组风寒感冒初起患者各项情况。结果观察组疗效总有效率为94.74%,头痛中医症状积分降幅分数为(3.20±0.48)分,喷嚏为(3.18±0.39)分,咳嗽为(3.23±0.36)分、流涕为(3.16±0.42)分、鼻塞为(2.96±0.36)分,发热为(3.33±0.26)分,生活质量评分为(92.53±5.12)分,各项指标均与对照组无较大差异(P0.05)。结论在风寒感冒初起患者的临床治疗中应用紫苏叶饮片及其免煎颗粒效果均较佳,但紫苏叶免煎颗粒服用方式较为方便,临床价值更高。     

最小二乘支持向量机算法在中医临床脉图参数-血压预测模型的应用

目的 提出基于最小二乘支持向量机（LSSVM）算法的学习模型,以提高中医临床血压数据预测的准确度和效率。方法 将LSSVM学习模型应用于中医临床血压数据预测。用LSSVM等式约束代替支持向量机不等式约束,将二次规划问题转化为线性方程求解问题,降低计算复杂性,加快算法收敛速度。收集320例患者的临床脉图参数及血压数据,以其中300例样本作为训练样本,训练得到LSSVM学习模型,以其余20例样本作为测试数据,用得到的LSSVM学习模型根据患者的脉图参数预测血压数据。结果 实验证明,LSSVM学习模型对血压数据有较好的预测准确度。其中基于多项式核函数的LSSVM学习模型较基于径向基核函数LSSVM学习模型表现出更好的学习和预测能力,基于多项式核函数的LSSVM学习模型中收缩压、舒张压、平均动脉压预测结果的平均预测误差分别为7.88%、8.40%、6.67%,低于基于径向基核函数的LSSVM学习模型的预测误差（分别为7.95%、9.70%、7.48%）。结论 本实验提出的基于LSSVM的学习模型仅通过患者的临床脉图参数就可预测患者血压数据,对中医学临床诊断有一定的参考价值。