Differential gene expression of the healthy conjunctiva during the day

PurposeTo determine if there is diurnal variation in gene expression in normal healthy conjunctival cells.MethodsBulbar conjunctival swab samples were collected from four healthy subjects in the morning and evening of the same day. The two swab samples were taken from one eye of each participant, with a minimum of five hours gap between the two samples. RNA was extracted and analysed using RNA sequencing (RNA-Seq).ResultsA total of 121 genes were differentially expressed between the morning and the evening conjunctival samples, of which 94 genes were upregulated in the morning, and 27 genes were upregulated in the evening. Many of the genes that were upregulated in the morning were involved in defence, cell turnover and regulation of gene expression, while the genes upregulated in the evening were involved in signalling and mucin production.ConclusionsThis study has identified several genes whose expression changes over the course of the day. Knowledge of diurnal variations of conjunctival gene expression provides an insight into the regulatory status of the healthy eye and provides a baseline for examining changes during ocular surface disease.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

基因芯片法在食源性疾病中诊断效果及影响多因素Logistic分析研究

目的对比基因芯片法在食源性疾病诊断中的效果,并对影响多因素进行logistic分析。方法选择180例临床表现符合食源性疾病诊断标准的患者作为研究对象,随机均分为实验组和对照组各90例,对照组采用传统的常规培养检测方法,实验组采用基因芯片法进行检测,对比2种方法的检出率、检测耗时以及检测灵敏度。结果实验组的检出率和检测灵敏度均高于对照组的检出率和检测灵敏度,对照组的检测耗时大约是实验组检测耗时的8.24倍。结论相比常规方法,应用基因芯片法的诊断速度更快、准确率更高,在诊断食源性疾病中的应用效果更佳。通过对单因素的χ~2和t检验,确定对食源性疾病有直接影响的多个因素。对影响食源性疾病的多个因素进行Logistic分析,分析结果表明在本次研究分析中,影响较大的因素是人们的饮食卫生以及进食规律。     

Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.     

Prospects for polymer therapeutics in Parkinson's disease and other neurodegenerative disorders

Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons and represents a growing health burden to western societies. Like many neurodegenerative disorders the cause is unknown, however, as the pathogenesis becomes ever more elucidated, it is becoming clear that effective delivery is a key issue for new therapeutics. The versatility of today's polymerization techniques allows the synthesis of a wide range of polymer materials which hold great potential to aid in the delivery of small molecules, proteins, genetic material or cells. In this review, we capture the recent advances in polymer based therapeutics of the central nervous system (CNS). We place the advances in historical context and, furthermore, provide future prospects in line with newly discovered advancements in the understanding of PD and other neurodegenerative disorders. This review provides researchers in the field of polymer chemistry and materials science an up-to-date understanding of the requirements placed upon materials designed for use in the CNS aiding the focus of polymer therapeutic design.     

Microarray analysis of high light intensity stress on hydrogen production metabolism of Rhodobacter capsulatus

Biohydrogen obtained from purple non sulfur bacteria (PNSB) is an environmentally friendly alternative for hydrogen production. PNSB can be employed in large scale outdoor photobioreactors to produce hydrogen by photofermentation with sunlight as the light source. In external environmental conditions, however, bacteria can experience stress due to high light intensities, which can inhibit or slow down hydrogen production. Previous studies with other PNSB showed varying responses to light intensities (above 4000 lux), in some cases improving, and in others adversely affecting hydrogen production.In this study, Rhodobacter capsulatus, a PNSB species that produce hydrogen efficiently from dark fermenter effluents containing acetate, was used to investigate the effects of high light intensity stress on the hydrogen production metabolism at the gene expression level. A microarray analysis was carried out using a custom-design Affymetrix GeneChip TR_RCH2a520699F. R. capsulatus DSM1710 was grown under a cyclic illumination of 2000 and 7000 lux (12 h light/12 h dark) in a hydrogen production medium having 30 mM acetate and 2 mM glutamate, and was exposed to a high light intensity (10,000 lux) for 1 h in the middle of a light period. The results reveal that photosynthetic reaction center genes were down-regulated in order to protect the photosynthetic membrane from damage. On the other hand, the expression of nitrogenase and electron transport system genes were enhanced by high light intensity. These results show that a high light intensity stress drives R. capsulatus to direct gene expression towards hydrogen production, which supports the hypothesis that hydrogen production is a way for the disposal of excess reducing equivalents to maintain the internal redox balance.     

<i>In Silico</i> analysis and linking of metabolism-related genes with the immune landscape in head and neck squamous cell carcinoma

Metabolic reprogramming and immunologic suppression are two critical characteristics promoting the progression of head and neck squamous cell carcinoma (HNSCC). The integrative analysis of all the metabolism-related genes (MRGs) in HNSCC is lacking and the interaction between the metabolism and the immune characteristics also requires more exploration to uncover the potential mechanisms. Therefore, this study was designed to establish a prognostic signature based on all the MRGs in HNSCC. Genes of HNSCC samples were available from the TCGA and GEO databases while the MRGs were retrieved from a previous study. Ultimately 4 prognostic MRGs were selected to construct a model possessing robust prognostic value and accuracy in TCGA cohorts. The favorable reproducibility of this model was confirmed in validation cohorts from GEO databases. The risk score calculated by this model was an independent prognostic factor that further classified these HNSCC patients into high-/low-risk groups. GSEA analyses and somatic mutations indicated the low-risk group could activate several anti-tumor pathways and possessed lower TP53 mutation. The results of ESTIMATE, single-sample GSEA, CIBERSORT, and some immune-related molecules analyses suggested the low-risk group exhibited lower metabolic activities and higher immune characteristics. The Spearman correlation test implied most metabolic pathways with tumor-promoting function were negatively correlated with the immune activity, indicating a plausible approach of combining the anti-metabolism and the immunotherapy drugs in the high-risk group to enhance therapeutic effects than applied separately. In conclusion, this prognostic signature linking MRGs with the immune landscape could promote the individualized treatment for HNSCC patients.     

重组人淋巴细胞活化基因-3蛋白胞外段的真核表达及鉴定

目的真核表达重组人淋巴细胞活化基因-3(lymphocyte activation gene-3,LAG-3)蛋白胞外段,并进行鉴定。方法用植物血球凝集素(phytohaemagg lutinin,PHA)刺激Jurkat细胞,流式细胞术检测Jurkat细胞中LAG-3蛋白的表达;提取Jurkat细胞总mRNA,RT-PCR法扩增人LAG-3蛋白胞外段基因片段,同时在蛋白C-末端引入His标签,将其克隆入载体pcDNA3. 1+,构建重组质粒,转染Expi293F真核细胞,当细胞活率低于50%时收获细胞上清,经镍柱亲合层析纯化。纯化产物进行4%~20%SDS-PAGE、HPLC及Western blot分析,BCA法测定浓度。结果经菌液PCR、双酶切及测序鉴定,表明质粒构建正确。重组表达蛋白的相对分子质量约60 000,纯化后纯度达95%以上,与鼠抗LAG-3单克隆抗体可发生特异性结合,浓度为2. 4 mg/mL。结论成功构建了重组真核表达质粒LAG-3/pcDNA3. 1+,并于Expi293F细胞中表达,纯化获得了纯度较高的LAG-3蛋白,为后期LAG-3蛋白的相关研究及其单抗的制备奠定了基础。     

Peanut FAD2 Genotype and Growing Location Interactions Significantly Affect the Level of Oleic Acid in Seeds

The level of oleic acid is an important parameter in determining seed nutritional quality and oil stability. The level of oleic acid in peanut is genetically controlled by a pair of fatty acid desaturase genes (FAD2A and FAD2B), but the environmental conditions of the production sites can also have a significant effect. To investigate the effect of gene and environment interaction, 45 accessions were grown at three locations for 2 years. Environmental data were collected; individual plants were genotyped with functional SNP markers from FAD2A and FAD2B; and seed level of oleic acid was determined by gas chromatography. Three FAD2A/FAD2B genotypes (448G/no insertion 442A, 448A/no insertion 442A, and 448A/insertion 442A) were identified and designated as G/W, A/W, and A/A, respectively. A/A genotype averaged the highest level of oleic acid (80.0%), followed by A/W (56.0%), and then G/W (40.7%). Analysis of gene and environment interaction revealed that oleic acid phenotype plasticity could be explained by the interaction of FAD2 genotype and photothermal time, which quantified environmental conditions. The A/W genotype was the most sensitive to photothermal time changes. The oleic acid plasticity revealed in this study would be useful for breeders, farmers, and product processors.