Investigation of the association between habitual dietary FODMAP intake, metabolic parameters, glycemic status, and anthropometric features among apparently healthy overweight and obese individuals.

BACKGROUND: The predisposition of humans to metabolic syndrome is affected by many factors, including diet and lifestyle. Fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) are a set of carbohydrates that are fermented by gut microbiota. In animal studies, supplementation with FODMAP-rich diets as prebiotics can alter body composition and gut microbiota. This study evaluates any relationship between FODMAP and metabolic syndrome risk factors among adults with metabolic syndrome in Iran. METHODS: This cross-sectional study is based on sociodemographic information from 347 overweight and obese participants selected from outpatient clinics through public declaration. Participants body composition and anthropometric measures were also determined. A validated Food Frequency Questionnaire (FFQ) with 168 questions was used to collect dietary data. Biochemical parameters, including serum total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting serum glucose (FSG), and insulin levels, were determined by enzymatic methods. In addition, the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Quantitative Insulin Sensitivity Check Index (QUICKI) were calculated. RESULTS: In moderate FODMAP and low FODMAP groups, lower waist-to-hip ratio (WHR) and higher fat-free mass (FFM) were achieved in higher tertiles. In high FODMAP groups, higher systolic blood pressure (SBP) was shown in the higher tertile (P < 0.05). Higher insulin, HOMA-IR, and lower QUICKI in the second tertile of the high FODMAP group were also observed. CONCLUSION: Findings of this study highlight the potential role of FODMAP in managing metabolic syndrome and open a new field of research.

In silico engineering and simulation of RNA interferences nanoplatforms for osteoporosis treating and bone healing promoting.

Osteoporosis is a bone condition characterized by reduced bone mineral density (BMD), poor bone microarchitecture/mineralization, and/or diminished bone strength. This asymptomatic disorder typically goes untreated until it presents as a low-trauma fracture of the hip, spine, proximal humerus, pelvis, and/or wrist, requiring surgery. Utilizing RNA interference (RNAi) may be accomplished in a number of ways, one of which is by the use of very tiny RNA molecules called microRNAs (miRNAs) and small interfering RNAs (siRNAs). Several kinds of antagomirs and siRNAs are now being developed to prevent the detrimental effects of miRNAs. The goal of this study is to find new antagonists for miRNAs and siRNAs that target multiple genes in order to reduce osteoporosis and promote bone repair. Also, choosing the optimum nanocarriers to deliver these RNAis appropriately to the body could lighten up the research road. In this context, we employed gene ontology analysis to search across multiple datasets. Following data analysis, a systems biology approach was used to process it. A molecular dynamics (MD) simulation was used to explore the possibility of incorporating the suggested siRNAs and miRNA antagonists into polymeric bioresponsive nanocarriers for delivery purposes. Among the three nanocarriers tested [polyethylene glycol (PEG), polyethylenimine (PEI), and PEG-PEI copolymer], MD simulations show that the integration of PEG-PEI with has-mIR-146a-5p is the most stable (total energy = -372.84 kJ/mol, Gyration radius = 2.1084 nm), whereas PEI is an appropriate delivery carrier for has-mIR-7155. The findings of the systems biology and MD simulations indicate that the proposed RNAis might be given through bioresponsive nanocarriers to accelerate bone repair and osteoporosis treatment.

Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm.

PURPOSE: Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. METHODS: For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. RESULTS: The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. CONCLUSION: Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.