Comprehensive honey authentication in Bangladesh: Profiling physicochemical and bioactive compounds to distinguish floral sources and detect adulteration.

This study aims to advance honey authentication in Bangladesh by developing a reliable, cost-effective, and user-friendly system capable of distinguishing floral sources and purity. We analyzed various physicochemical parameters and bioactive compounds in honey samples from diverse floral sources across Bangladesh during different floral seasons, including deliberately adulterated samples. Results showed clear distinctions among the tested parameters.

Genome-Wide Identification and Characterization of Major RNAi Genes Highlighting Their Associated Factors in Cowpea ( (L.) Walp.).

In different regions of the world, cowpea (Vigna unguiculata (L.) Walp.) is an important vegetable and an excellent source of protein.

N-acetylcysteine reduces severity and mortality in COVID-19 patients: A systematic review and meta-analysis.

Objectives: Recent clinical studies suggest that oxidative stress is one of the key players in the pathogenesis of coronavirus disease 2019 (COVID-19), and N-acetylcysteine (NAC), a potent antioxidant, has been shown to improve clinical outcomes in COVID-19 patients. We conducted a systematic review and meta-analysis of the literature published on the therapeutic intervention of NAC on COVID-19 infection.

Methods: We searched PubMed, Google Scholar, and Science Direct.

A Comparison on Some Interval Mapping Approaches for QTL Detection.

Quantitative trait locus (QTL) analysis is a statistical method that links two types of information such as phenotypic data (trait measurements) and genotypic data (usually molecular markers). There a number of QTL tools have been developed for gene linkage mapping. Standard Interval Mapping (SIM) or Simple Interval Mapping or Interval Mapping (IM), Haley Knott, Extended Haley Knott and Multiple Imputation (IMP) method when the single-QTL is unlinked and Composite Interval Mapping (CIM) is designed to map the genetic linkage for both linked and unlinked genes in the chromosome.

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering.

Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches.

Classification of Functional Metagenomes Recovered from Different Environmental Samples.

Classification of functional metagenomes from the microbial community plays the vital role in the metagenomics research. In this paper, an investigation was made to study the performance of beta-t random forest classifier for classification of metagenomics data. Nine key functional meta-genomic variables were selected using the beta-t test statistic from the 10 different microbial community using p-value at 5% level of significance.

Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart.

Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds.

Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model.

Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs.

Toxic Dose prediction of Chemical Compounds to Biomarkers using an ANOVA based Gene Expression Analysis.

The aim of toxicogenomic studies is to optimize the toxic dose levels of chemical compounds (CCs) and their regulated biomarker genes. This is also crucial in drug discovery and development. There are popular online computational tools such as ToxDB and Toxygates to identify toxicogenomic biomarkers using t-test.