Biology subjects

Rasool, A.

Publications and source records attributed to Rasool, A..

5 recordsLinked to original sources

bioRxiv2024

A Novel Combinatorial Approach Integrating Experimental and Computational Analysis of Antioxidant Activity: Evaluating Catechin and L-Ascorbic Acid in Serum

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality globally, with oxidative stress playing a pivotal role in its progression. Free radicals produced via oxidative stress contribute to lipid peroxidation, leading to subsequent inflammatory responses, which then result in atherosclerosis. Antioxidants inhibit these harmful effects through their reducing ability, thereby preventing oxidative damage. In this study, we introduce a with computational models simulating hydrophilic and hydrophobic serum environments. We optimized the Ferric Reducing Ability of Plasma (FRAP) assay at a microscale level to evaluate the antioxidant activity of L-ascorbic acid (vitamin C) and catechin, a phytochemical found in green tea, in normal and hypertriglyceridemic serum. Hypertriglyceridemic serum, characterized by increased hydrophobic lipid content, provides a model to examine the impact of serum triglycerides on antioxidant activity. Additionally, we employed computational models using the Gaussian software to simulate the hydrogen atom transfer (HAT) mechanism, calculating free energy changes and bond dissociation energy (BDE) to assess the antioxidant potency of the studied compounds in both hydrophilic and hydrophobic environments. The computational results align with the experimental finding offering a unique combinatorial approach to assess antioxidant activity in both normal and hypertriglyceridemic serum, with potential implications for clinical interventions.

biochemistry↗

bioRxiv2024

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation

Continual learning, the ability of a model to learn over time without forgetting previous knowledge and, therefore, be adaptive to new data, is paramount in dynamic fields such as disease outbreak prediction. Deep neural networks, i.e., LSTM, are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for continual learning by leveraging domain adaptation via Elastic Weight Consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher Information Matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to important parameters, namely, the important previous knowledge. CELs performance is evaluated on three distinct diseases, Influenza, Mpox, and Measles, with different metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts, indicating that CEL adapts to incremental data well. CELs robustness and reliability are underscored by its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CELs versatility in disease outbreak prediction, addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate, timely predictions.

bioinformatics↗

bioRxiv2023

Unravelling the anticancer and enzyme inhibition potential of different classes of compounds including a new steroid, isolated from Cassia mimosoïdes

The genus Cassia is a significant source of secondary metabolites that are physiologically active and come from several chemical classes. The current research deals with the isolation, spectroscopic elucidation (1D and 2D NMR spectroscopy) and enzymatic activity of fifteen known compounds as well as a new unidentified avenasterol derivative namely 21-methylene-24-ethylidene lophenol. The urease and {beta}-glucosidase inhibitory effects of these compounds were studied for the first time, and molecular docking studies were also performed to verify the structure-activity relationships. All the compounds evaluated towards urease showed higher inhibitory activity (1.224{+/-}0.43 < IC50 > 6.678{+/-}0.11 M) compared to standard thiourea (IC50 = 18.61{+/-}0.11 M). Molecular docking results revealed that compound 7 strongly inhibits urease due to the formation of a stable ligand-urease complex via hydrogen bonding, van der Waal and hydrophobic interactions. Formation of a favourable complex of 7 with the target enzyme gave a more negative docking score (-6.95 kcal/mol) than that of thiourea (-3.13 kcal/mol). Regarding the {beta}-glucosidase enzyme, all the compounds evaluated did not show activity except compound 1 which inhibited the latter with a percentage of inhibition of 82.6. These findings imply that this plant may be a contender for developing novel treatments for infectious disorders brought on by urease-producing bacteria.

biochemistry↗

bioRxiv2021

Sex Identification in Cattle, based on Amelogenin Gene

Sex identification is considered an important step in the field of forensic sciences, wildlife and livestock breeding management. In the current experiment we used Amelogenin gene as a biological marker for polymerase chain reaction test to identify the sex of cattle from blood remnants, collected at slaughter house. Due to the conserved region of the gene on both sex chromosomes (X and Y) a single primers pair was employed to amplify the gene in a single polymerase chain reaction. In case of band patterns, a 178 base pair fragment for AMELY and a 241 base pair fragment for AMELX genes were produced. The primers competence and exactness was initially checked on known gender cattle samples and then applied to unknown cattle samples for the validation of the experiment. PCR amplicons of unknown gender showed only one band (241-bp) for female DNA and two bands (241-bp, 178-bp) for male DNA, on the platform of agarose gel upon electrophoresis. Our findings showed that the PCR protocol based on AMELX/Y gene is a reliable technique for the identification of cattle sex.

zoology↗

bioRxiv2019

BIN overlap confirms transcontinental distribution of pest aphids (Hemiptera: Aphididae)

DNA barcoding is highly effective for identifying specimens once a reference sequence library is available for the species assemblage targeted for analysis. Despite the great need for an improved capacity to identify the insect pests of crops, the use of DNA barcoding is constrained by the lack of a well-parameterized reference library. The current study begins to address this limitation by developing a DNA barcode reference library for the pest aphids of Pakistan. It also examines the affinities of these taxa with conspecific taxa from other geographic regions based on both conventional taxonomy and Barcode Index Numbers (BINs). A total of 809 aphids were collected from 123 plant species at 87 sites across Pakistan. Morphological study and DNA barcoding allowed 774 specimens to be identified to one of 42 species while the others were placed to a genus or subfamily. The 801 sequences obtained from these specimens were assigned to 52 BINs whose monophyly were supported by neighbor-joining (NJ) clustering and Bayesian inference. The 42 species were assigned to 41 BINs with 38 showing BIN concordance; one species (Rhopalosiphum padi) was assigned to two BINs, while two others (Aphis affinis, Aphis gossypii) were assigned to the same BIN, while one species (Aphis astragalina) lacked a qualifying sequence. The 42 Linnaean species were represented on BOLD by 7,870 records from 69 countries. Combining these records with those from Pakistan produced to 60 BINs with 12 species showing a BIN split and three a BIN merger. Geo-distance correlations showed that intraspecific divergence values for 18 of 37 species were not affected by the distance between populations. Forty four of the 52 BINs from Pakistan had counterparts in 73 countries across six continents, documenting the broad distributions of pest aphids.

ecology↗