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Fourteen Ganoderma lucidum strains from different geographic regions were identified using ITS region sequencing. Based on the sequences obtained, the genomic relationship between the analyzed strains was determined. All G. lucidum strains were also genetically characterized using the AFLP technique. G. lucidum strains included in the analysis displayed an AFLP profile similarity level in the range from 9.6 to 33.9%. Biolog FF MicroPlates were applied to obtain data on utilization of 95 carbon sources and mitochondrial activity. The analysis allowed comparison of functional diversity of the fungal strains. The substrate utilization profiles for the isolates tested revealed a broad variability within the analyzed G. lucidum species and proved to be a good profiling technology for studying the diversity in fungi. Significant differences have been demonstrated in substrate richness values. Interestingly, the analysis of growth and biomass production also differentiated the strains based on the growth rate on the agar and sawdust substrate. In general, the mycelial growth on the sawdust substrate was more balanced and the fastest fungal growth was observed for GRE3 and FCL192. [ABSTRACT FROM AUTHOR]

Cancer is a complex disease residing in various tissues of human body, accompanied with many abnormalities and mutations in genomes, transcriptome, and epigenome. Early detection plays a crucial role in extending survival time of all major cancer types. Recent advances in microarray and sequencing techniques have given more support to identifying effective biomarkers for early detection of cancer. MicroRNAs (miRNAs) are more and more frequently used as candidates for biomarkers in cancer related studies due to their regulation of target gene expression. In this paper, the comparative analysis is used to discover miRNA expression patterns in cancer versus normal samples on early stage of eight prevalent cancer types. Our work focuses on the specific miRNAs biomarkers identification and function analysis. Several identified miRNA biomarkers in this paper are matched well with those reported in existing researches, and most of them could serve as potential candidate indicators for clinical early diagnosis applications. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition. [ABSTRACT FROM AUTHOR]

Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition. [ABSTRACT FROM AUTHOR]

Essential oil of Eucalyptus globulus presents several pharmacological properties. However, their therapeutic efficacy may be affected by limitations due to several conditions, rendering it difficult to obtain stable and effective pharmaceutical formulations. The use of nanotechnology is an alternative to improve their characteristics aiming to ensure their stability and effectiveness. Furthermore, studies about the possible toxic effects of nanostructures are necessary to evaluate safety when the formulation comes into contact with human cells. Hence, in this paper, we evaluate for the first time the stability and in vitro cytogenotoxicity of nanoemulsions containing Eucalyptus globulus in peripheral blood mononuclear cells. As a result, the stability study found that the best condition for storage up to 90 days was refrigeration (4°C); it was the condition that best preserved the nanometric features. The content of the major compounds of oil was maintained after nanoencapsulation and preserved over time. In tests to evaluate the safety of this formulation, we can conclude that, at a low concentration (approximately 0.1%), Eucalyptus globulus nanoemulsion did not cause toxicity in peripheral blood mononuclear cells and also showed a protective effect in cells against possible damage when compared to oil in free form. [ABSTRACT FROM AUTHOR]

Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work. [ABSTRACT FROM AUTHOR]

Nicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide. Our previous works indicate that NNMT is involved in the body mass index and energy metabolism, and recently the association between a SNP (rs694539) of NNMT and a variety of cardiovascular diseases was reported. At present, more than 200 NNMT single nucleotide polymorphisms (SNPs) have been identified in the databases of the human genome projects; however, the association between rs694539 variation and hyperlipidemia has not been reported yet, and whether there are any SNPs in NNMT significantly associated with hyperlipidemia is still unclear. In this paper, we selected 19 SNPs in NNMT as the tagSNPs using Haploview software (Haploview 4.2) first and then performed a case-control study to observe the association between these tagSNPs and hyperlipidemia and finally applied physiological approaches to explore the possible mechanisms through which the NNMT polymorphism induces hyperlipidemia. The results show that a SNP (rs1941404) in NNMT is significantly associated with hyperlipidemia, and the influence of rs1941404 variation on the resting energy expenditure may be the possible mechanism for rs1941404 variation to induce hyperlipidemia. [ABSTRACT FROM AUTHOR]

This paper investigates the protective effect of interleukin-1 receptor antagonist (IL-1Ra) released from hyaluronic acid chitosan (HA-CS) microspheres in a controlled manner on IL-1β-induced inflammation and apoptosis in chondrocytes. The IL-1Ra release kinetics was characterized by an initial burst release, which was reduced to a linear release over eight days. Chondrocytes were stimulated with 10 ng/ml IL-1β and subsequently incubated with HA-CS-IL-1Ra microspheres. The cell viability was decreased by IL-1β, which was attenuated by HA-CS-IL-1Ra microspheres as indicated by an MTT assay. ELISA showed that HA-CS-IL-1Ra microspheres inhibited IL-1β-induced inflammation by attenuating increases in NO2- and prostaglandin E2 levels as well as increase in glycosaminoglycan release. A terminal deoxyribonucleotide transferase deoxyuridine triphosphate nick-end labeling assay revealed that the IL-1β-induced chondrocyte apoptosis was decreased by HA-CS-IL-1Ra microspheres. Moreover, HA-CS-IL-1Ra microspheres blocked IL-1β-induced chondrocyte apoptosis by increasing B-cell lymphoma 2 (Bcl-2) and decreasing Bcl-2-associated X protein and caspase-3 expressions at mRNA and protein levels, as indicated by reverse-transcription quantitative polymerase chain reaction and western blot analysis, respectively. The results of the present study indicated that HA-CS-IL-1Ra microspheres as a controlled release system of IL-1Ra possess potential anti-inflammatory and antiapoptotic properties in rat chondrocytes due to their ability to regulate inflammatory factors and apoptosis associated genes. [ABSTRACT FROM AUTHOR]

Sublethal concentrations (sub-MICs) of certain disinfectants are no longer effective in removing biofilms from abiotic surfaces and can even promote the formation of biofilms. Bacterial cells can probably adapt to these low concentrations of disinfectants and defend themselves by way of biofilm formation. In this paper, we report on three Staphylococcus aureus biofilm formers (strong B+++, moderate B++, and weak B+) that were cultivated with sub-MICs of commonly used disinfectants, ethanol or chloramine T, and quantified using Syto9 green fluorogenic nucleic acid stain. We demonstrate that 1.25–2.5% ethanol and 2500 μg/mL chloramine T significantly enhanced S. aureus biofilm formation. To visualize differences in biofilm compactness between S. aureus biofilms in control medium, 1.25% ethanol, or 2500 μg/mL chloramine T, scanning electron microscopy was used. To describe changes in abundance of surface-exposed proteins in ethanol- or chloramine T-treated biofilms, surface proteins were prepared using a novel trypsin shaving approach and quantified after dimethyl labeling by LC-LTQ/Orbitrap MS. Our data show that some proteins with adhesive functions and others with cell maintenance functions and virulence factor EsxA were significantly upregulated by both treatments. In contrast, immunoglobulin-binding protein A was significantly downregulated for both disinfectants. Significant differences were observed in the effect of the two disinfectants on the expression of surface proteins including some adhesins, foldase protein PrsA, and two virulence factors. [ABSTRACT FROM AUTHOR]

With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively. The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon. These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance. In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance. Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively. [ABSTRACT FROM AUTHOR]

Sparse representation is a powerful tool in signal denoising, and visual evoked potentials (VEPs) have been proven to have strong sparsity over an appropriate dictionary. Inspired by this idea, we present in this paper a novel sparse representation-based approach to solving the VEP extraction problem. The extraction process is performed in three stages. First, instead of using the mixed signals containing the electroencephalogram (EEG) and VEPs, we utilise an EEG from a previous trial, which did not contain VEPs, to identify the parameters of the EEG autoregressive (AR) model. Second, instead of the moving average (MA) model, sparse representation is used to model the VEPs in the autoregressive-moving average (ARMA) model. Finally, we calculate the sparse coefficients and derive VEPs by using the AR model. Next, we tested the performance of the proposed algorithm with synthetic and real data, after which we compared the results with that of an AR model with exogenous input modelling and a mixed overcomplete dictionary-based sparse component decomposition method. Utilising the synthetic data, the algorithms are then employed to estimate the latencies of P100 of the VEPs corrupted by added simulated EEG at different signal-to-noise ratio (SNR) values. The validations demonstrate that our method can well preserve the details of the VEPs for latency estimation, even in low SNR environments. [ABSTRACT FROM AUTHOR]

Objective. To establish and validate a nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with nasopharyngeal carcinoma (NPC) receiving radiotherapy by integrating multiple independent prognostic factors. Materials and Methods. Data from 5663 patients with NPC who received definite radiotherapy between 2004 and 2018 were included and divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors of patients with NPC after radiotherapy. Thereafter, the predictive accuracy of the nomogram model was evaluated. Results. Age, race, marital status, pathological type, tumor size, T stage, N stage, M stage, American Joint Committee on Cancer stage, and chemotherapy were independent factors affecting the prognosis of patients with NPC receiving radiotherapy. Nomograms with a concordance index of 0.726 (95% confidence interval (CI): 0.675–0.777) and 0.732 (95% CI: 0.680–0.785) were able to predict OS and CSS, respectively. The area under the curve showed excellent predictive performance. Additionally, the calibration curve indicated that the predicted survival rate was consistent with the actual survival rate, and the decision curve indicated its clinical value. The established risk stratification system was able to accurately stratify patients receiving radiotherapy for NPC into three risk subgroups with significant differences in prognosis (P < 0.05). Conclusions. The constructed nomogram had good prognostic performance and could be used as an effective tool to evaluate the prognosis of patients with NPC after radiotherapy. This nomogram could be further used to guide clinical decisions and personalized treatment plans. [ABSTRACT FROM AUTHOR]

A new fungus Cladosporium oxysporum GQ-3 producing extracellular xylanase was isolated from decaying agricultural waste and identified based on the morphology and comparison of internal transcribed spacer (ITS) rDNA gene sequence. C. oxysporum produced maximum xylanase activity of 55.92 U/mL with wheat bran as a substrate and NH4Cl as a nitrogen source. Mg2+ improved C. oxysporum xylanase production. Partially purified xylanase exhibited maximum activity at 50°C and pH 8.0, respectively, and showed the stable activity after 2-h treatment in pH 7.0–8.5 or below 55°C. Mg2+ enhanced the xylanase activity by 2% while Cu2+ had the highest inhibition ratio of 57.9%. Furthermore, C. oxysporum xylanase was resistant to most of tested neutral and alkaline proteases. Our findings indicated that Cladosporium oxysporum GQ-3 was a novel xylanase producer, which could be used in the textile processes or paper/feed industries. [ABSTRACT FROM AUTHOR]

In genome-wide association study (GWAS), robust genetic association tests such as maximum of three CATTs (MAX3), each corresponding to recessive, additive, and dominant genetic models, the minimum p value of Pearson’s Chi-square test with 2 degrees of freedom, and CATT based on additive genetic model (MIN2), genetic model selection (GMS), and genetic model exclusion (GME) methods have been shown to provide better power performance under wide range of underlying genetic models. In this paper, we demonstrate how these robust tests can be applied to the replication study of GWAS and how the overall statistical significance can be evaluated using the combined test formed by p values of the discovery and replication studies. [ABSTRACT FROM AUTHOR]

Inflammatory disorders affect many people worldwide, and medicinal plants are used to ameliorate these health problems. This paper reports the antiedematogenic and analgesic evaluation of Copaifera langsdorffii Desf. leaves hydroethanolic extract (Cop) and two of its isolated compounds: quercetin-3-O-α-L-rhamnopyranosyl (quercitrin) and kaempferol-3-O-α-L-rhamnopyranosyl (afzelin). For that, the following experimental protocols were undertaken locomotor performance, writhing induced by acetic acid, antinociceptivity induced by formalin, hot plate latency, paw oedema induced by carrageenan and dextran, and cell migration induced by lipopolysaccharide (LPS), as well as the measurement of nitric oxide (NO), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interleukin 10 (IL-10) in macrophages. Neither the extract nor the isolated compounds displayed analgesic activity. The obtained results showed that C. langsdorffii extract possesses antiedematogenic properties acting on peripheral sites, whereas quercitrin and afzelin are not involved. Moreover, these properties are not associated with cell migration inhibition, TNF-α, IL-6, or IL-10 regulation. [ABSTRACT FROM AUTHOR]

Chitosan is a biopolymer derived from chitin which is naturally occurring in the exoskeleton of crustaceans. This paper reports dissolution and regeneration of chitosan by directly dissolving in an ionic liquid solvent, 1-butyl-3-methylimidazolium chloride (BMIMCl). This will provide an ideal platform to solubilise these kinds of polymers to achieve the dissolution. The current study dissolved chitosan from crab shell utilising BMIMCl as a solvent and characterised the resultant regenerated polymer. The regenerated chitosan showed increased hydrogen bonding when characterised by Fourier transform infrared (FTIR) spectral analysis. In addition, the study also compared the characteristics of regenerated and generic chitosan. The regenerated chitosan was also evaluated for antimicrobial properties and showed to possess antibacterial features similar to the commercial grade. This method can be utilised in future for blending of polymers with chitosan in a dissolved phase. [ABSTRACT FROM AUTHOR]

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources. [ABSTRACT FROM AUTHOR]

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis. [ABSTRACT FROM AUTHOR]

Recently, we have found that a number of microRNAs (miRNAs) and proteins are involved in the response to acupuncture therapy in hypertensive rats. Our bioinformatics study suggests an association between these miRNAs and proteins, which include miR-339 and sirtuin 2 (Sirt2). In this paper, we aimed to investigate whether Sirt2 was a direct target of miR-339 in neurons. In human SH-SY5Y cells, the luciferase assay implied that Sirt2 was likely a target of miRNA-339. Overexpression of miR-339 downregulated Sirt2 expression, while knockdown of miR-339 upregulated Sirt2 expression in human SH-SY5Y cells and rat PC12 cells. In addition, overexpression of miR-399 increased the acetylation of nuclear factor-κB (NF-κB) and forkhead box protein O1 (FOXO1) in SH-SY5Y cells, which are known targets of Sirt2. Our findings demonstrate that miR-339 regulates Sirt2 in human and rat neurons. Since Sirt2 plays a critical role in multiple important cellular functions, our data imply that acupuncture may act through epigenetic changes and subsequent action on their targets, such as miRNA-339/Sirt2/NF-κB/FOXO1 axis. Some physiological level changes of neurons after altering the miR-339 levels are needed to validate the suggested therapeutic role of miR-339/Sirt2/NF-κB/FOXO1 axis in response to acupuncture therapy in the future work. [ABSTRACT FROM AUTHOR]

Background. Solute carrier family 2 member 4- (SLC2A4-) retinol binding protein-4- (RBP4-) phosphoenolpyruvate carboxykinase 1 (PCK1)/phosphoinositide 3-kinase (PI3K) is an adipocyte derived "signalling pathway" that may contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). We explored whether single nucleotide polymorphisms (SNPs) of these "signalling pathway" genes are associated with gestational diabetes mellitus (GDM). Methods. Case-control studies were conducted to compare GDM and control groups. A total of 334 cases and 367 controls were recruited. Seventeen candidate SNPs of the pathway were selected. Chi-square tests, logistic regression, and linear regression were used to estimate the relationships of SNPs with GDM risk and oral glucose tolerance test (OGTT), fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) levels. Model-based multifactor dimensionality reduction was used to estimate the adjusted interactions between genes. Regression and interaction analyses were adjusted by maternal age, prepregnancy BMI, and weekly BMI growth. The Bonferroni correction was applied for multiple comparisons. Results. RBP4 rs7091052 was significantly associated with GDM risk. SLC2A4 rs5435, RBP4 rs7091052, PCK1 rs1042531 and rs2236745, and PIK3R1 (coding gene of the PI3K P85 subunit) rs34309 were associated with OGTT, fasting insulin, and HOMA-IR levels in the linear regression analysis. The gene-gene interaction analysis showed that, compared with pregnant women with other genotype combinations, women with SLC2A4 rs5435 (CC/CT), RBP4 rs7091052 (CC), PCK1 rs1042531 (TT/TG) and rs2236745 (TT), and PIK3R1 rs34309 (AA) had lower GDM risk. Conclusion. SLC2A4, RBP4, PCK1, and PIK3R1 genes may be involved in the pathogenesis of GDM. [ABSTRACT FROM AUTHOR]