Transcranial random noise stimulation (tRNS) over prefrontal cortex does not influence the evaluation of facial emotions

ABSTRACT

Cerebral asymmetries for emotion processing are controversial, the right hemisphere being considered either superior in the recognition of all emotions, or superior in the recognition of negative emotions (together with the left-hemispheric superiority for positive emotions). In a number of previous studies, tDCS was applied on the left/right prefrontal cortex (PFC) in order to disentangle this issue, but the results remain controversial. We applied hf-tRNS/sham stimulation over the left/right PFC, during the presentation of neutral, angry and happy faces presented as broadband images (supraliminal condition), and as “hybrid” stimuli in which an emotional face in low spatial frequency is superimposed to the neutral expression of the same individual in high spatial frequency (subliminal condition), during a friendliness evaluation task. The results showed that angry and happy unfiltered stimuli were judged as the most unfriendly and friendly, respectively. Importantly, we found that hf-tRNS applied over the left/right PFC did not influence friendliness evaluations for emotional faces.     

How can self-efficacy be increased? Meta-analysis of dietary interventions

Targeting individuals' beliefs that they are able to eat healthily can improve dietary-related behaviours. However, the most effective behaviour change techniques (BCTs) to promote dietary self-efficacy have not been systematically reviewed. This research addressed this gap. Studies testing the effect of interventions on healthy eating and underlying dietary-related self-efficacy, within randomised controlled trials, were systematically reviewed in MEDLINE, EMBASE and PSYCINFO. Two reviewers independently coded intervention content in both intervention and comparison groups. Data pertaining to study quality were also extracted. Random effects meta-analysis was used to calculate an overall effect size on dietary self-efficacy for each study. The associations between 26 BCTs and self-efficacy effects were calculated using meta-regression. In some of the analyses, interventions that incorporated self-monitoring (tracking one's own food-related behaviour), provided feedback on performance, prompted review of behavioural goals, provided contingent rewards (rewarding diet success), or planned for social support/social change increased dietary self-efficacy significantly more than interventions that did not. Stress management was consistently associated with self-efficacy effects across all analyses. There was strong evidence for stress management and weaker evidence for a number of other BCTs. The findings can be used to develop more effective, theory- and evidence-based behavioural interventions.     

Molecular epidemiology of cutaneous leishmaniasis and heterogeneity of Leishmania major strains in Iran

Objective To determine the geographical distribution of Leishmania species causing cutaneous leishmaniasis (CL) and to study the genetic heterogeneity of Leishmania major isolates from different endemic areas of Iran. Methods A total of 341 isolates from lesions of patients living in 11 provinces of Iran were grown in culture medium and inoculated to BALB/c mice to detect possible visceralisation. The species were identified by isoenzyme analysis using a battery of six enzymes and kinetoplast (k) DNA‐PCR technique. Genetic variation among L. major isolates was analysed by random amplified polymorphic DNA (RAPD) technique. Results Of the total 341 isolates, 283 isolates were L. major and 58 isolates were Leishmania tropica. In rural areas, the causative agent of CL was mainly L. major (95%L. major vs. 5%L. tropica), in urban areas it was L. tropica (65%L. tropica vs. 35%L. major). All isolates of L. major and 8.6% of L. tropica isolates showed visceralisation in BALB/c mice. There is considerable genetic diversity between L. major strains from different endemic areas and even between some isolates of the same endemic area. Conclusion Leishmania major is the most frequent species in the endemic areas of CL in eleven provinces of Iran, and genetic diversity is a common feature of L. major in the country.     

Stochastic processes in the aetiopathogenesis of scleroderma

We review the aetiology of scleroderma from an epidemiological perspective examining genetic, environmental and stochastic risk factors. The presence of familial clustering (but with low twin concordance) suggests a genetic contribution, and this has been confirmed with recent candidate gene and genome-wide association screening demonstrating both major histocompatibility complex and non-major histocompatibility complex genetic linkage. In contrast, environmental associations are weak or inconsistent. An examination of the age-adjusted incidence curve of scleroderma is consistent with a stochastic process involving five to eight random events. In pathogenesis, scleroderma is best considered as an autoimmune disorder where genetic and environmental factors are both important variables, but random events are also likely to play a pivotal role. We suggest that these random events might result in acquired somatic mutations or epigenetic alterations involving genes coding for immune receptors, tolerogenic gates or proteins involved in immune regulation, inflammation and/or repair that, over time, might summate to form a requisite cassette (of genetic changes), which allows the initiation and progression of the autoimmune process.     

FTIR-ATR结合膜富集技术测定中药材地龙中微量铜的含量

目的 利用傅里叶变换-衰减全反射红外光谱(FTIR-ATR)结合膜富集技术,建立中药材地龙中微量铜质量分数的快速、经济的测定方法.方法 首先,对30批校正集样品进行湿法消解,利用铜与金属络合剂1-(2-吡啶偶氮)-2-萘酚(PAN)发生络合反应,优化各种反应条件,包括pH 、PAN用量、反应时间.再将络合物抽滤富集在微孔滤膜上,采集其FTIR-ATR光谱,经9点平滑、一阶导数9点平滑等预处理后,利用随机森林回归算法建立其铜质量分数的定量校正模型.校正集按3∶1随机分配为训练集和测试集,对模型进行参数优化和评价.利用优化后模型预测广东、广西和福建3个产地地龙样品中铜的质量分数,并与ICP-MS测定结果进行比较.结果 最优模型预测3个产地地龙样品的质量分数在5.85～6.98 mg/kg之间,与ICP-MS分析结果相比,相对误差均小于20％.结论 本方法利用膜富集技术提高了FTIR-ATR的检测灵敏度,为中药材重金属元素的定量分析提供了一种新方法.     

Subgroup analysis based on prognostic and predictive gene signatures for adjuvant chemotherapy in early-stage non-small-cell lung cancer patients

In treating patients diagnosed with early Stage I non-small-cell lung cancer (NSCLC), doctors must choose surgery alone, Adjuvant Cisplatin-Based Chemotherapy (ACT) alone or both. For patients with resected stages IB to IIIA, clinical trials have shown a survival advantage from 4–15% with the adoption of ACT. However, due to the inherent toxicity of chemotherapy, it is necessary for doctors to identify patients whose chance of success with ACT is sufficient to justify the risks. This research seeks to use gene expression profiling in the development of a statistical decision-making algorithm to identify patients whose survival rates will improve from ACT treatment. Using the data from the National Cancer Institute, the lasso method in the Cox-Proportional-Hazards regression model is used as the main method to determine a feasible number of genes that are strongly associated with the treatment-related patient survival. Considering treatment groups separately, the patients are assigned a risk category based on the estimation of survival times. These risk categories are used to develop a Random Forests classification model to identify patients who are likely to benefit from chemotherapy treatment. This model allows the prediction of a new patient’s prognosis and the likelihood of survival benefit from ACT treatment based on a feasible number of genomic biomarkers. The proposed methods are evaluated using a simulation study.     

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta‐analyses. This type of data can be presented as a series of 2‐by‐2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta‐analysis. We examine the use of 7 models for random‐effects meta‐analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within‐study approximations and a 2‐stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2‐stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.     

Meta‐analysis approaches to combine multiple gene set enrichment studies

In the field of gene set enrichment analysis (GSEA), meta‐analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta‐Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta‐analysis approaches recently developed for genome‐wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size‐adjusted enrichment score based on a one‐sided Kolmogorov‐Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.     

Bartlett‐type corrections and bootstrap adjustments of likelihood‐based inference methods for network meta‐analysis

In network meta‐analyses that synthesize direct and indirect comparison evidence concerning multiple treatments, multivariate random effects models have been routinely used for addressing between‐studies heterogeneities. Although their standard inference methods depend on large sample approximations (eg, restricted maximum likelihood estimation) for the number of trials synthesized, the numbers of trials are often moderate or small. In these situations, standard estimators cannot be expected to behave in accordance with asymptotic theory; in particular, confidence intervals cannot be assumed to exhibit their nominal coverage probabilities (also, the type I error probabilities of the corresponding tests cannot be retained). The invalidity issue may seriously influence the overall conclusions of network meta‐analyses. In this article, we develop several improved inference methods for network meta‐analyses to resolve these problems. We first introduce 2 efficient likelihood‐based inference methods, the likelihood ratio test–based and efficient score test–based methods, in a general framework of network meta‐analysis. Then, to improve the small‐sample inferences, we developed improved higher‐order asymptotic methods using Bartlett‐type corrections and bootstrap adjustment methods. The proposed methods adopt Monte Carlo approaches using parametric bootstraps to effectively circumvent complicated analytical calculations of case‐by‐case analyses and to permit flexible application to various statistical models network meta‐analyses. These methods can also be straightforwardly applied to multivariate meta‐regression analyses and to tests for the evaluation of inconsistency. In numerical evaluations via simulations, the proposed methods generally performed well compared with the ordinary restricted maximum likelihood–based inference method. Applications to 2 network meta‐analysis datasets are provided.