Showing posts with label PCA. Show all posts
Showing posts with label PCA. Show all posts

Monday, 28 April 2025

A Comparative Study on The Efficiency of Genomic DNA Extraction Protocols for P53 Gene Polymorphism in Prostate Cancer | Chapter 8 | Microbiology and Biotechnology Research: An Overview Vol. 2

The physical, chemical, and physiological makeup of the prostate varies greatly between species. The prostate's job is to emit a milky or white fluid that is slightly alkaline and accounts for around 30% of the amount of semen in humans, together with sperm cells and seminal vesicle fluid. Prostate cancer (PC), a type of cancer that develops in the prostate gland in the male reproductive system, is one of the most prevalent cancers influencing older men in the developed world and a major inducer of mortality for elderly men. In recent decades, genetic techniques have emerged as a powerful resource across various life-related applications. DNA-based technologies, such as PCR, are increasingly utilized in research focused on demographic genetic diversity, QTL detection, marker-assisted selection, and food traceability. These methodologies necessitate extraction processes that ensure effective nucleic acid retrieval and the removal of PCR inhibitors. The initial and most critical step in molecular biology is the extraction of DNA from cells. For molecular scientists, the quality and integrity of the isolated DNA, along with the extraction method's user-friendliness and cost-effectiveness, are essential considerations. This study aimed to develop a straightforward, rapid, and cost-effective technique for extracting DNA from human peripheral blood samples, specifically comparing two normal male subjects aged 24 years (n=2) and two male patients with prostate cancer aged 65 years (n=2). The objective was to standardize a DNA extraction protocol utilizing five different extraction methods. The first method involved a modified organic approach that substituted sodium perchlorate for traditional organic solvents like phenol and chloroform, highlighting the advantages of sodium perchlorate due to its affordability and minimal storage and shipping requirements. The second method employed an enzymatic approach using proteinase K, while the third method utilized a detergent. The fourth method incorporated phenol chloroform, and the final method was based on the salting out technique. The findings indicated that the organic extraction method produces a satisfactory yield of DNA in a relatively brief period, while the enzymatic extraction method results in superior DNA purity, making it more appropriate for PCR applications. The purpose of the present study was to find a suitable procedure for DNA extraction with low cost, time, and hazards as well as high yield, purity, and excitability for PCR amplification. Among the primary strategies for obtaining DNA is from whole blood specimens, and there are a variety of protocols known to extract nucleic acids from such specimens. The five proposed methods for DNA extraction significantly lower costs by utilizing only basic and easily obtainable laboratory supplies and equipment, eliminating the need for expensive components such as K and RNase proteins. This conclusion was drawn from a comparison of five different protocols utilizing spectrophotometry, Nanodrop technology, and electrophoresis. The PCR amplification of the P53 gene using the isolated DNA was successfully achieved through these five methods. This suggests that, apart from the detergent-based method, there were no significant inhibitors present for Taq polymerase in the final solution.

 

Author (s) Details

Zaizafoon Nabeel Nasif
Department of Chemistry, College of Science, University of Mustansiriyah, Baghdad, Iraq.

Please see the book here:- https://doi.org/10.9734/bpi/mbrao/v2/5158

Tuesday, 7 January 2025

Analysis of Genetic and Molecular Diversity in Green Gram (Vigna radiata (L.) Wilczek) Genotypes Using SSR Markers | Chapter 1 | Innovations in Biological Science Vol. 5

 

Mungbean, often known as Greengram, is a member of the Leguminosae family. A study on the genetic and molecular diversity of 40 genotypes of Greengram was conducted using SSR markers. The study of molecular markers is essential to genomic research. Because of their repeatability, multiallelic nature, codominant inheritance, relative abundance, and good genomic coverage, SSRs stand out among other marker systems like restriction fragment length polymorphism (RFLP), RAPD, sequence tagged sites (STSs), and AFLP. The experimental material contained significant genetic diversity; nevertheless, for all yield-related and yield-attributing features, phenotypic coefficient of variation exceeded genotypic coefficient of variation. The genotypes LGG 574 (8.80), PDM 139 (8.34), and Pant Mung 6 had the highest seed yields (7.78). The highest observed cluster distances were between clusters 5 and 6 (472.88) and clusters 4 and 5 (432.89). Among all the factors PC 1 to PC 10, PC 1 (19.99) accounted maximum proportion of variability in the set of all variables and the remaining components accounted for progressively lesser and lesser amounts of variation. The first six principal components (PC-1 to PC-6) with eigenvalues of 2.94, 2.62, 2.18, 1.40, 1.28, and 0.86, respectively, accounted for 86.95% of the total variance for all the qualities, according to principal component analysis. Genotypes 23, 32, 33, and 21 were spread out relatively far from other genotypes in the scatter plot, suggesting that they might be different from other genotypes. PIC values of 10 SSR loci, where the VR 86 marker produced the greatest PIC Value percentage and highest heterozygosity percent. The fixation index ranges from 1.000 to -0.076. In comparison to Cluster II, III, IV, and V, Cluster I has the most genotypes (25), and the use of SSR markers in this work to differentiate between genotypes was made possible by the high polymorphism information richness of this cluster. This study shows that SSR analysis can be used to evaluate the molecular diversity of various Greengram genotypes. For the purpose of marker-assisted breeding programmes, plant breeders highly value the information generated on marker data.

 

Author(s)details:-

 

Mr. S. Shanmukha Saikumar
Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.

 

Dr. P. S. Shanmugavadivel
Department of Plant Biotechnology, Indian Institute of Pulses Research, Kanpur- 208024, Uttar Pradesh, India.

 

Dr. Meenal Rathore
Department of Plant Biotechnology, Indian Institute of Pulses Research, Kanpur- 208024, Uttar Pradesh, India.

 

Dr. Meenal Rathore
Department of Plant Biotechnology, Indian Institute of Pulses Research, Kanpur- 208024, Uttar Pradesh, India.

 

Miss G. Gayathri
Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj - 211007, Uttar Pradesh, India.

 

 

Please See the book here :-  https://doi.org/10.9734/bpi/ibs/v5/3498G

Saturday, 13 July 2024

Optimizing Quinoline Derivatives for ABCB1 Inhibition: A Machine Learning Approach to Combat Multidrug Resistance in Cancer | Chapter 10 | Current Innovations in Chemical and Materials Sciences Vol. 9

 

A vast array of human tumors contain multidrug resistance (MDR) proteins linked to the ATP-binding cassette family, which lead to treatment failure. One of the mechanisms of multiple drug resistance is the overexpression of efflux pumps, like ABCB1. In order to predict the inhibitory biological activity towards ABCB1, the goal of this paper is to develop a robust quantitative structure-activity relationship (QSAR) model that best describes the correlation between the activity and the molecular structures. Using various linear and non-linear machine learning (ML) regression techniques, such as k-nearest neighbors (KNN), decision trees (DT), back propagation neural networks (BPNN), and gradient boosting-based (GB) methods, a series of quinoline derivatives of eighteen compounds were examined in this regard. Their goal is to identify the source of these compounds' activity in order to create new quinoline derivatives that have a stronger effect on ABCB1. A total of sixteen machine learning (ML) predictive models were created using varying numbers of 2D and 3D descriptors. The statistical metrics root mean square error (RMSE) and coefficient of determination (R2) were used to assess the models. With one descriptor, represented by R2 and RMSE of 95% and 0.283, respectively, a GB-based model, specifically catboost, achieved the highest predictive quality among all developed models. The outward-facing p-glycoprotein (6C0V) was the target crystal structure for molecular docking studies, and the results showed strong binding affinities via both hydrophobic and H-bond interactions with the relevant compounds. At -9.22 kcal/mol, the 17 has the highest binding energy. As a result, it is possible that structure 17 will prove to be a useful potential lead structure for the synthesis and design of more effective P-glycoprotein inhibitors that can be combined with anti-cancer medications to manage cancer multidrug resistance.

 

Author(s) Details:

Mouad Lahyaoui,
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.


Riham Sghyar
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

 

Yousra Seqqat
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Fouad Ouazzani Chahdi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.


Ahmed Mazzah

University of Lille, CNRS, USR 3290, MSAP, Miniaturization for Synthesis, Analysis and Proteomics, Lille, France.


Amal Haoudi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Taoufiq Saffaj
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.

Youssef Kandri Rodi
Laboratory of Applied Organic Chemistry, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, USMBA, P.O. Box 2626, Fez, Morocco.


Please see the link here: https://stm.bookpi.org/CICMS-V9/article/view/14341

Thursday, 9 December 2021

Discrimination Of Various Brain Pathological Lesions Non-invasively by MRI Utilizing Supervised Machine Learning Manipulating Tissue Refractive Index, T2 Relaxation Values and Tissue Metabolites | Chapter 14 | Recent Developments in Medicine and Medical Research Vol. 9

 For the stability of dental implants and artificial joints, excellent solid bonding between biomaterials and bone tissue (osseointegration and osteo-conductivity) is critical. Much has been learnt about this notion, leading to substantial advances in implant design and surface modification in the fields of implant dentistry and orthopaedic surgery.

We studied whether low-intensity pulsed ultrasound (LIPUS) could accelerate the osseointegration ability of bioactive materials such as bioactive titanium and hydroxyapatite for this issue.

Bio-active pure titanium and hydroxyapatite (HA) were used as materials, and an in vitro simulation test and an animal experiment were conducted.

The production of bone-like hydroxyapatite on the material surface in simulated body fluid (SBF) under LIPUS was evaluated in a simulation test. The bio-active samples implanted to the rabbits' femurs, which underwent LIPUS irradiation, were studied using Scanning Electron Microscope (SEM), X-ray diffraction (XRD), and histological observation in an animal test.

As a result of the crystal growth of bone-like apatite on the surface of the sample materials, LIPUS irradiation showed excellent enhancement of bone-material attachment, implying that LIPUS application has clinical potential to improve osseointegration (osteointegration), bone-bonding ability of bio-active materials.

Author(S) Details

Tapan K. Biswas
Department of Instrumentation and Electronics Engineering, Jadavpur University, India.

Rajib Bandyopadhyay
Department of Instrumentation and Electronics Engineering, Jadavpur University, India.

View Book:- https://stm.bookpi.org/RDMMR-V9/article/view/4596

Sunday, 24 May 2020

Multivariate Analysis of Genetic Diversity among Maize Genotypes and Trait Interrelationships under Drought and Low N Stress | Chapter 3 | New Perspectives in Agriculture and Crop Science Vol. 2

Multivariate analysis is the most popular approach for genetic variability estimation to study the patterns of variation and their genetic relationships among germplasm collections to enhance their use in crop breeding. The objectives of the present study were: (i) to assess the extent of genetic diversity in a collection of Egyptian commercial maize hybrids and populations, through field evaluation under water and N stressed and non-stressed conditions, using morphological data based on Principle Component Analysis (PCA), (ii) to measure the genetic distance among these genotypes using Agglomerative Hierarchical Clustering (AHC) analysis and (iii) to assess the relationship between grain yield and yield-related traits of maize genotypes using genotype × trait (GT)-biplot analysis. A two-year field experiment was conducted in a split-split plot design with 3 replications, where 2 irrigation regimes, three N rates and 19 maize genotypes occupied the main plots, sub plots and sub-sub plots, respectively. The germplasm was assessed for 21 agronomic traits. Highly significant differences (P ≤ 0.01) were observed among the maize hybrids and populations for all measured traits.  Results of the GT biplot in the present study indicated that high values of 100-Kernel weight, ears/plant, kernels/plant, kernels/row, plant height, nitrogen use efficiency, nitrogen utilization efficiency, and grain nitrogen content and short ASI could be considered reliable secondary traits for improving grain yield under stressed and non-stressed conditions. The highest genetic distance was found between G9 (SC-2055) and each of G15 (American Early Dent), G18 (Midland) or G19 (Ried Type). The AHC based on phenotypic data assigned the maize genotypes into five groups. The different groups obtained can be useful for deriving the inbred lines with diverse features and diversifying the heterotic pools.

Author (s) Details

Dr. Ahmed Medhat Mohamed Al-Naggar  
Department of Agronomy, Faculty of Agriculture, Cairo University, Giza, Egypt.

Dr. Magdy Mohamed Shafik  
Department of Agronomy, Faculty of Agriculture, Cairo University, Giza, Egypt.

Rabeh Yousef Mubarak Musa  
Department of Agronomy, Faculty of Agriculture, Upper Nile University, South Sudan.

View Book: - http://bp.bookpi.org/index.php/bpi/catalog/book/172