Effects of low and high intensity interval training exercises on VO2max and components of neuromuscular and vascular system in male volunteers

Objectives:To evaluate the effects of different intensity exercises on maximal oxygen consumption (VO_2max and levels of components, namely brain-derived neurotrophic factor (BDNF), tyrosine kinase receptor B (TrKB), vascular endothelial growth factor (VEGF), peroxisome proliferator activated receptor-gamma coactivator (PGC-1α), and irisin.Methods:Thirty-six male participants were divided into control (CNT), low-intensity (LIIT), and high-intensity interval training (HIIT) groups. LIIT and HIIT groups consisted of 8 exercises (20 s work and rest in each repetition, respectively) conducted for 4 weeks. VO_2max and protein component levels were determined pre- and post-training. VO_2max capacity was also determined using the Yo Yo Intermittent Recovery Test-1 (Yo Yo IR-1). Statistical analysis was conducted to determine significance of the differences observed.Results:According to the YoYo IR-1, VO_2max, serum BDNF, VEGF, PGC1α, irisin, and TrkB data obtained in the study, a statistically significant difference between the groups was observed (p<0.05). While the interaction effect was found to be statistically significant in the study using PGC1α, VEGF, and TrkB data (p<0.05), it was not found to be statistically significant using YoYo IR-1, VO_2max, serum BDNF, or irisin data (p>0.05).Conclusion:HIIT and LIIT improved all study parameters, while HIIT showed a greater effect than LIIT.     

Artificial intelligence in differentiating tropical infections: A step ahead

Background and objectiveDifferentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.MethodologyA cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.ResultsA total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55–60%, whereas a binary classification machine learning algorithms showed an average of 79–84% for one vs other and 69–88% for one vs one disease category.ConclusionThis is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.     

Role of IRAK-M in Alcohol Induced Liver Injury

Increasing evidence suggests that innate immunity plays an important role in alcohol-induced liver injury and most studies have focused on positive regulation of innate immunity. The main objective of this study was to investigate the negative regulator of innate immunity, IL-1/Toll-like receptor (TLR) signaling pathways and interleukin receptor-associated kinase-M (IRAK-M) in alcoholic liver injury. We established an alcohol-induced liver injury model using wild type and IRAK-M deficient B6 mice and investigated the possible mechanisms. We found that in the absence of IRAK-M, liver damage by alcohol was worse with higher alanine transaminase (ALT), more immune cell infiltration and increased numbers of IFNγ producing cells. We also found enhanced phagocytic activity in CD68⁺ cells. Moreover, our results revealed altered gut bacteria after alcohol consumption and this was more striking in the absence of IRAK-M. Our study provides evidence that IRAK-M plays an important role in alcohol-induced liver injury and IRAK-M negatively regulates the innate and possibly the adaptive immune response in the liver reacting to acute insult by alcohol. In the absence of IRAK-M, the hosts developed worse liver injury, enhanced gut permeability and altered gut microbiota.     

Sulphonamides incorporating 1,3,5-triazine structural motifs show antioxidant,acetylcholinesterase, butyrylcholinesterase,and tyrosinase inhibitory profile

Abstract

A series of 16 novel benzenesulfonamides incorporating 1,3,5-triazine moieties substituted with aromatic amines, dimethylamine, morpholine and piperidine were investigated. These compounds were assayed for antioxidant properties by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay, 2,2`-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical decolarisation assay and metal chelating methods. They were also investigated as inhibitors of acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and tyrosinase, which are associated with several diseases such as Alzheimer, Parkinson and pigmentation disorders. These benzenesulfonamides showed moderate DPPH radical scavenging and metal chelating activity, and low ABTS cation radical scavenging activity. Compounds 2?b, 3d and 3?h showed inhibitory potency against AChE with % inhibition values of >90. BChE was also effectively inhibited by most of the synthesised compounds with >90% inhibition potency. Tyrosinase was less inhibited by these compounds.     

Genetic analysis of phenylpropanoid metabolites associated with resistance against Verticillium longisporum in Brassica napus

Verticillium longisporum is a major threat to production of oilseed rape (Brassica napus) in Europe. The aim of the study was to develop new markers and obtain insights into putative mechanisms and pathways involved in the resistance reaction. A genetic approach was used to identify quantitative trait loci (QTL) for V. longisporum resistance and metabolic traits potentially influencing resistance in a B. napus mapping population. Resistance to V. longisporum was mapped in a doubled haploid (DH) population from a cross between the partially resistant winter oilseed rape variety Express 617 and a resistant resynthesized B. napus line, R53. One major resistance QTL contributed by R53 was identified on chromosome C5, while a further, minor QTL contributed by Express 617 was detected on chromosome C1. Markers flanking the QTL also significantly correlated with V. longisporum resistance in four further DH populations derived from crosses between elite oilseed rape cultivars and other resynthesized B. napus lines originating from genetically and geographically diverse brassica A and C genome donors. The tightly-linked markers developed enable the combination of favorable alleles for novel resistance loci from resynthesized B. napus materials with existing resistance loci from commercial breeding lines. HPLC analysis of hypocotyls from infected DH lines revealed that concentrations of a number of phenylpropanoids were correlated with V. longisporum resistance. QTL for some of these phenylpropanoids were also found to co-localize with the QTL for V. longisporum resistance. Genes from the phenylpropanoid pathway are suggested as candidates for V. longisporum resistance.     

Checklist of the springtails (Hexapoda: Collembola) of Turkey

A checklist of the species of springtails (Hexapoda: Collembola) hitherto recorded from Turkey is presented. This list contains 53 species belonging to 39 genera in 13 families. The diversity of Collembola in Turkey is poorly known and it is clear that numerous species wait to be discovered.