Putative therapeutic impacts of cardiac CTRP9 in ischaemia/reperfusion injury

Recently, cytokines belonging to C1q/tumour necrosis factor‐related proteins (CTRPs) superfamily have attracted increasing attention due to multiple metabolic functions and desirable anti‐inflammatory effects. These various molecular effectors exhibit key roles upon the onset of cardiovascular diseases, making them novel adipo/cardiokines. This review article aimed to highlight recent findings correlated with therapeutic effects and additional mechanisms specific to the CTRP9, particularly in cardiac ischaemia/reperfusion injury (IRI). Besides, the network of the CTPR9 signalling pathway and its possible relationship with IRI were discussed. Together, the discovery of all involved underlying mechanisms could shed light to alleviate the pathological sequelae after the occurrence of IRI.     

Construction of miRNA‐lncRNA‐mRNA co‐expression network affecting EMT‐mediated cisplatin resistance in ovarian cancer

Platinum resistance is one of the major concerns in ovarian cancer treatment. Recent evidence shows the critical role of epithelial–mesenchymal transition (EMT) in this resistance. Epithelial‐like ovarian cancer cells show decreased sensitivity to cisplatin after cisplatin treatment. Our study prospected the association between epithelial phenotype and response to cisplatin in ovarian cancer. Microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE47856","term_id":"47856"}}GSE47856 was acquired from the GEO database. After identifying differentially expressed genes (DEGs) between epithelial‐like and mesenchymal‐like cells, the module identification analysis was performed using weighted gene co‐expression network analysis (WGCNA). The gene ontology (GO) and pathway analyses of the most considerable modules were performed. The protein–protein interaction network was also constructed. The hub genes were specified using Cytoscape plugins MCODE and cytoHubba, followed by the survival analysis and data validation. Finally, the co‐expression of miRNA‐lncRNA‐TF with the hub genes was reconstructed. The co‐expression network analysis suggests 20 modules relating to the Epithelial phenotype. The antiquewhite4, brown and darkmagenta modules are the most significant non‐preserved modules in the Epithelial phenotype and contain the most differentially expressed genes. GO, and KEGG pathway enrichment analyses on these modules divulge that these genes were primarily enriched in the focal adhesion, DNA replication pathways and stress response processes. ROC curve and overall survival rate analysis show that the co‐expression pattern of the brown module''s hub genes could be a potential prognostic biomarker for ovarian cancer cisplatin resistance.     

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.Graphical abstract      

Thermostable extracellular protease of Bacillus stearothermophilus: factors affecting its production

A strain of protease-producing Bacillus stearothermophilus has been isolated. Glycerol was the best carbon source for production whereas yeast extract was the best nitrogen source. The bacterium could grow up to 70°C but optimum protease production was at 60°C. Best initial pH for protease production was 5. Alkaline pH inhibited production. The enzyme was stable at 60°C for 18 h and was inhibited by EDTA, PMSF and HgCl₂.The authors are with the Enzyme and Microbial Technology Group, Faculty of Science and Environmental Studies, Universiti Pertanian Malaysia, 43400 UPM Serdang, Selangor, Malaysia     

Insulin adsorption on functionalized silica surfaces: an accelerated molecular dynamics study

We study the influence of surface functionalization of a silica surface on insulin adsorption using accelerated molecular dynamics simulation. Three different functional groups are studied, CH₃, OH, and COOH. Due to the partial charges of these groups, the surface polarity of silica is strongly altered. We find that the adsorption energies of insulin change in agreement with the decreasing surface polarity. Conformational changes in the adsorbed protein and the magnitude of the molecular dipole moment in the adsorbed state are consistent with this result. We conclude that protein adsorption on functionalized polar surfaces is governed by the induced changes in surface polarity.     

Genetic and Chemical Diversity in Perovskia abrotanoides Kar. (Lamiaceae) Populations Based on ISSRs Markers and Essential Oils Profile

Genetic and the essential oil composition variability among twelve Perovskia abrotanoides populations (PAbPs) growing wild in Iran were assessed by ISSR markers, GC‐FID and GC/MS, respectively. Nine selected ISSR primers produced 119 discernible bands, of them 96 (80.7%) being polymorphic. Genetic similarity values among populations ranged between 0.07 and 0.79 which indicated a high level of genetic variation. Polymorphic information content, resolving power and marker index generated by ISSR primers were, 0.31, 6.14, and 3.32, respectively. UPGMA grouped PAbPs into four main clusters. Altogether, 38 chemical compounds were identified in the oils, and a relatively high variation in their contents was found. Camphor (11.9 – 27.5%), 1,8‐cineole (11.3 – 21.3%), α‐bisabolol (0.0 – 13.1%), α‐pinene (5.9 – 10.8%), and δ‐3‐carene (0.1 – 10.5%) were the major compounds. Oxygenated monoterpenes (32.1 – 35.8%) and monoterpene hydrocarbons (25.7 – 30.4%) were the main groups of compounds in the oils studied. Cluster analysis and principal‐component analysis were used to characterize the samples according to oil components. Four main chemotypes were found to be Chemotype I (camphor/1,8‐cineol), Chemotype II (1,8‐cineole/camphor), Chemotype III (camphor/1,8‐cineol/α‐bisabolol), and Chemotype IV (camphor/δ‐3‐carene/α‐bisabolol). The information, provided here on P. abrotanoides populations, will be useful to introduce this plant into agricultural systems.