Template-Type: ReDIF-Chapter 1.0 Author-Name: Jaanvi Dayal Author-Name: Velamanchi Vaishnavi Author-Name: Sushma Patkar Title: Callus Induction and Characterization in Oryza Sativa Indica Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: In rice tissue culture, optimizing plant growth regulators (PGRs) is crucial for effective callus induction. The present study investigates the induction of rice callus in Murashige and Skoog (MS) media supplemented with varying concentrations of 6-Benzylaminopurine (BAP) and Indole-3-acetic acid (IAA), namely, in concentrations of 3:1 ratio, 2:1 ratio and 1:1 ratio respectively. The goal was to identify the most effective PGR combination and concentration that promotes optimal callus growth. Callus fresh and dry weight were measured to assess the growth response under different conditions. Highest mean fresh weight of callus was obtained for 3:1 ratio of BAP to IAA (0.55 ± 0.38 gram). The present study provides valuable insights into optimizing rice tissue culture protocols for improved callus induction, offering potential applications in plant propagation and genetic improvement. Pages: 1-7 Chapter: 1 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch001.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:1-7 Template-Type: ReDIF-Chapter 1.0 Author-Name: M. Bhargavi Author-Name: Sri Gayathri Bhargavi Author-Name: Esha Sripada Title: Computational Approach for HDAC1 Predicting Protein-Ligand Interactions for Cancer through Homology Modelling, Virtual Screening and Molecular Docking Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Histone Deacetylase 1 (HDAC1) is vital for controlling gene expression, chromatin remodelling, and biological functions like distinction and cellular proliferation by acetylation of histone tail residues. Its abnormal expression is of high significance in inflammatory diseases, cancers and allergic diseases. Thus, considering the structure and function of HDAC1 is very crucial because it may be used as a therapeutic target for cancer, neurologicalillnesses, and other disorders. In this study, the structure of HDAC1 was predicted using homology modelling. First, based on the known crystal structures of template proteins, a high-quality 3D model of HDAC1 was generated using the concepts of homology modelling by Modeller 10.6 software. The correctness and reliability of this model were enhanced using a GalaxyWEB refine tool and for energy minimization YASARA software was put to use.The model obtained after refinement and energy minimization was put for validation on the ProSA-WEB and SAVES server where the results of z-score, ERRAT, VERIFY-3D and Ramachandran plot were obtained and analysed. The analysed model was then employed to investigate the binding interactions with ligands that may inhibit HDAC1 activity, in molecular docking studies. The steps involved were performed using Schrodinger suites Maestro 13.5 included: protein preparation using protein preparation wizard, grid generation using GLIDE and virtual screening employing molecular docking using virtual screening workflow wizard. Therefore, through this study HDAC1's structure was predicted, validated and molecular docking studies were performed on it. Pages: 8-21 Chapter: 2 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch002.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:8-21 Template-Type: ReDIF-Chapter 1.0 Author-Name: Naga Santhosh Reddy Vootukuri Author-Name: R. Harichandana Author-Name: K. Sai Prasanna Author-Name: V. Jeevana Jyothi Title: From Code to Cure: The Impact of Artificial Intelligence in Pharma Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Artificial Intelligence (AI) is revolutionizing the field of drug discovery and development by making it faster, more accurate, and cost-effective. By analysing vast amounts of biomedical data, AI can quickly identify potential drug candidates, predict how drugs will interact with targets, and assist in designing new molecules. This speeds up the process of bringing new drugs to market. AI also helps in optimizing clinical trials and improving drug formulations. However, there are challenges such as data bias, regulatory issues, and ethical concerns. Despite these, AI holds great promise for the future, including personalized medicine and advanced research using quantum computing and AI-powered labs. This paper highlights the latest advancements and the transformative impact of AI on the pharmaceutical industry. Pages: 22-31 Chapter: 3 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch003.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:22-31 Template-Type: ReDIF-Chapter 1.0 Author-Name: Bachu Melissa Judith Author-Name: V. Jeevana Jyothi Title: Nanotechnology in Green Chemistry and Life Sciences: A Sustainable Approach Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Nanotechnology has emerged as a transformative tool in Green Chemistry and Life Sciences, offering innovative solutions to environmental, industrial, and healthcare challenges. This paper reviews recent advancements in nanotechnology, focusing on sustainable applications such as nano-catalysts for energy-efficient processes, nano-adsorbents for biomedical waste management, and biodegradable nanomaterials for drug delivery. Furthermore, the study explores the role of metal and non-metal oxide nanoparticles in optimizing catalyst reactions for green biomedicine. Despite the promising prospects, concerns regarding nanoparticle toxicity, environmental impact, and sustainability management remain critical challenges that necessitate regulatory and ethical considerations. Pages: 32-40 Chapter: 4 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch004.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:32-40 Template-Type: ReDIF-Chapter 1.0 Author-Name: K. Swathi Author-Name: Sanganabattla Supraja Title: Isolation, Production, Extraction, Optimization and Fortification of PHB using Silver Nanoparticles from Lactobacillus Casei Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: In this study, bacteria were first isolated to produce the biopolymer PHB (poly - 3 hydroxybutyrate) as a substitute for plastic, which is a major contributor to soil pollution and excess plastic wastage worldwide. Plastic lacks the property to degrade in soil but is used in large forms. To overcome this problem, bioplastics are synthesized which are biodegradable compounds that are completely sustainable and can substitute fossil fuels and non-degradable plastic. In this study, the ideal organism for the production of PHB was first isolated from soil samples and then grown on suitable media. After identifying the isolate as Lactobacillus by performing various biochemical tests. The organism was then massproduced to extract PHB by differential digestion treatment to extract the bacterial pellet containing PHB. The estimation of PHB was done by using a double-beam UV-VIS spectrophotometer, the readings were taken and maximum absorbance was noted. Optimization is performed to increase the polymer production in PHB-producing organisms, in optimization we check for the optimal conditions of different concentrations of carbon and nitrogen sources where we estimate the dry weight and check for the source showing the maximum dry weight. The post-synthesis of PHB nanoparticles was done to enhance its properties and increase the strength of the polymer. It is done by introducing silver nitrate to the bacterial pellet containing PHB. The presence of silver PHB nanoparticles was estimated using a double-beam UV-VIS spectrophotometer, the readings were taken and maximum absorbance was noted. Pages: 41-52 Chapter: 5 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch005.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:41-52 Template-Type: ReDIF-Chapter 1.0 Author-Name: Turaga Radha Prasannam Author-Name: Gouda Manaswi Author-Name: Sushma Patkar Title: Preparation of Cellulose-Based Degradable Biopolymer using Pineapple Waste Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: The growing environmental challenges posed by plastic waste has prompted the search for sustainable alternatives that can reduce ecological impact. The present study explores the development of a biodegradable plastic from pineapple peel, an agricultural waste product that is often discarded. By utilizing the natural polysaccharides present in the peel, a strong and completely degradable biopolymer is created, offering a sustainable alternative to conventional plastics. The procedure incorporates glycerol as a plasticizer, enhancing the material's flexibility and durability. This approach not only addresses plastic pollution but also contributes to waste valorization, turning pineapple peel into a high-value product. The use of agricultural byproducts for bioplastic production offers a promising solution for environmental applications, mitigating plastic pollution and promoting sustainable materials for a healthier planet. Pages: 53-58 Chapter: 6 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch006.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:53-58 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Vanitha Author-Name: A. Vijaya Lakshmi Author-Name: Sagar Dakua Title: Artificial Intelligence in Life Sciences - The Next Frontier, Decoding the Secrets of Life Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: The scientific landscape is being reshaped by emerging technologies and their cutting-edge applications superseded by the State-of-the-art techniques. One such technology revolutionizing the world is Artificial Intelligence (AI). It is a field that encompasses a wide array of computational approaches and algorithms capable of mimicking complex cognitive skills and functions of human. AI encompasses Machine Learning (ML), and ML encompasses Deep Learning (DL), whereas Next Generation Sequencing (NGS) gets benefitted from AI/ML/DL for data analysis. Machine learning has applications in both personalized medicine and gene editing. It can predict patient-specific treatment responses based on genomic data. Moreover, they are instrumental in optimizing gene editing technologies such as CRISPR-Cas9 by predicting on- and off-target effects in test datasets, thereby facilitating the design of guide RNAs (gRNAs) that minimize off-target activity. ML can also be applied to organoid technology. Organoids are three-dimensional structures, cultivated from stem cells, that aim to recreate the intricate cellular arrangements and 3D architectures characteristic of natural organs, there by simplifying the study of organ complexity. Next-generation sequencing (NGS), facilitates the simultaneous sequencing of millions of DNA fragments, as it plays a crucial role in deciphering the genetic heterogeneity of diseases. DL leverages AI to analyse healthcare data and drive improvements in patient care. By leveraging realworld data (RWD), AI can also streamline clinical research and bridge the gap between research and real-world practice. Deep learning models learn from real-world data, enabling them to recognize patterns and predict outcomes in similar real-world situations. AI, encompassing machine learning and deep learning, is transforming various fields, including healthcare, where it complements powerful tools like next-generation sequencing (NGS) to advance disease understanding, personalized medicine, and drug discovery, improving early detection and streamlining clinical research with real-world data. Pages: 59-65 Chapter: 7 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch007.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:59-65 Template-Type: ReDIF-Chapter 1.0 Author-Name: Mini Fernandez Author-Name: ANVSL Sarayu Title: In-Silico Studies of MTNB, TM14A, LEG2, S10A3 and ITC4S Proteins Involved in Lung Cancer Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Lung cancer has the highest mortality rates among both men and women and is the leading cause of cancer-related deaths worldwide. Research has shown that five proteins—MTNB, TM14A, LEG2, S10A3, and LTC4S—can accelerate the development of lung cancer. These proteins' target sequences, template sequences, and alignments were obtained from UniProtKB, PDB-RCSB, and ClustalOmega. Protein modeling and validation were performed using SWISS-MODEL and the SAVES v6.0 and ProSA tools. Surface pockets were identified using CASTpFold. Each protein was then docked with three selected ligands—sorafenib, distamycin, and sunitinib—using Vina in "PyRx." AutoDock Vina facilitated the separation of ligand conformations. Finally, BIOVIA Discovery Studio was used to visualize the interactions between the proteins and the drugs. Understanding these interactions highlights the potential of these medications as targeted therapies that could reduce tumor growth and improve patient outcomes. Pages: 66-77 Chapter: 8 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch008.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:66-77 Template-Type: ReDIF-Chapter 1.0 Author-Name: Mini Fernandez Author-Name: P. Shravani Shriya Title: In-Silico Studies of MIF, MIEN 1, NAA40, RALB, and PTGES Proteins Involved in Colorectal Cancer Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Colorectal cancer is the second leading cause of cancer-related deaths, emerging as one of the major causes of morbidity and mortality worldwide. Our study intends to focus on 5 proteins; MIF, MIEN 1, NAA40, RALB, and PTGES that have been experimentally proven to play a key role in the progression of colorectal cancer. The selected proteins and their template sequences along with structures were retrieved from NCBI, UniProt, BLAST, and RCSB PDB followed by alignment analysis in Clustal Omega. The models of the proteins were generated and validated using Swiss-Model, SAVES v6.0, and ProSA. The active site was predicted with the employment of CASTp. Docking of the protein with the selected ligands was performed using Vina in PyRx and the most suitable confirmation of the ligand was selected via splitting the ligand using AutoDock vina. Further details of protein-ligand interactions were visualized in BioVia Discovery Studio. In silico analysis revealed the topological features of various binding mechanisms of the drug with respective proteins, reflecting the possible interactions that can reduce the progression of CRCs acting as targeted agents for suppressing tumor growth. However, further experimental analysis including in vivo, in vitro, and clinical trials are required to validate the possible outcome and determine its success rate. Pages: 78-91 Chapter: 9 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch009.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:78-91 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Parijatham Kanchana Author-Name: Prasannan Pooja Author-Name: Rajashekar Chikati Title: Evaluating the Potential of SHOC2, CDK3, and EGFR as Drug Target in Lung Cancer through In-Silico Studies Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Cancer is listed as one of the main public health concerns. Statistics currently claim that new cancer cases number in India will go up from approximately 1.46 million in 2022 to 1.57 million by 2025. Lung cancer is among the most severe causes of death due to its aggressive nature, late detection, and resistance to standard treatments. Targeting the key molecular pathwaysthat promotethe growth of cancerisrequired forthe generation of successful curative medicines. This study focuses on the drug ability of proteins SHOC2 leucine-rich repeat scaffold protein, cyclin-dependent kinase3 (CDK3), and epidermal growth factor receptor (EGFR) for lung cancer through molecular docking studies. Using ‘PyMOL and HDOCK software’, molecular docking was performed to assess interactions between selected anticancer drugs Osimertinib, Gefitinib, Afatinib, Sorafenib, and Ceritinib. These drugs were selected due to their proven inhibitory effects on cancer-related signalling pathways. The docking results demonstrated varying binding affinities for the three Protein selected, which has strongest binding (-176.45) with Osimertinib, while Gefitinib, Afatinib, Sorafenib, and Ceritinib have less binding affinity (-116.37 to – 165.59). Docking data revealed different binding strengths for the selected drugs, which shows the strong binding interaction with certain target proteins, thus demonstrating a prospect for further development as targeted agents. These findings suggest that SHOC2, CDK3, and EGFR may be viable drug targets for lung cancer treatment. This study provides a computational framework for further in vitro and in vivo validation for drug discovery. Pages: 92-103 Chapter: 10 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch010.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:92-103 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Parijatham Kanchana Author-Name: Prathi Satya Yasaswini Author-Name: Rajashekar Chikati Title: In Silico Insights into CLEC4g, TD26, and NRF2 as Therapeutic targets in Hepatocellular Carcinoma Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Hepatocellular carcinoma is the 6th most occurring cancer in the world it causes approximately 8,30,000 deaths every year making it one of the leading reasons for cancer related deaths. In this project, we performed molecular docking to investigate the binding affinities of anti-HCC agents with three selected proteins: C-type lectin domain family 4 member G (CLEC4G), TD26, and Nuclear factor erythroid 2-related factor 2 (NRF2). CLEC4G is involved in immune modulation within the liver, TD26 is involved in tumor progression, and NRF2 plays a crucial role in cellular defense mechanisms. Using HDock and PyMOL, we performed molecular docking simulations to evaluate interactions between selected anticancer agents like Sorafenib, Lenvatinib, Regororafenib, Cabozantinib, Doxorubicin with these proteins. The selected ligands showed different binding affinities wherein Doxorubicin (-153.87, -137.42 and -122.77) showed the strongest binding affinity with all the three proteins followed by Cabozantinib, Regorafenib, Lenvatinib and sorafenib respectively. Our findings focus on the therapeutic potential of targeting CLEC4G, TD26, and NRF2 in HCC treatment. Additional lab experiments are needed to verify these results and study their Practical applications. This study provides valuable insights into the development of targeted therapies for HCC, paving the way for precision medicine approaches to improve patient outcomes. Pages: 104-115 Chapter: 11 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch011.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:104-115 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Parijatham Kanchana Author-Name: Devasani Vaishnavi Author-Name: Y. Sabitha Title: Antibiofilm and Antimicrobial Activity of Indigofera Tirunelvelica and Basella Alba Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: The objective of this research is to compare the anti-biofilm and antimicrobial activity of Indigofera tirunelvelica and Basella alba. This study helps us to understand the anti-biofilm activity & antimicrobial of extracts from Indigofera tirunelvelica and Basella alba against common biofilm-forming bacteria, such as Escherichia coli, Streptococcus pneumonia, Klebsiella pneumoniae, and Bacillus thuringiensis and biofilmforming fungi, such as Aspergillus Niger and Canadian. For this experimentation, leaf extract (methanol, chloroform & aqueous) of Indigofera tirunelvelica and Basella alba were used. Bacteria and fungi stains are cultured in tryptic soy broth and sabouraud dextrose broth respectively. The bacterium and fungi were cultured in well plates and incubated for 24 to 48 hours. The treatment with Indigofera tirunelvelica and Basella alba varying degrees of biofilm inhibition in bacteria and fungi respectively. For klebsiella and streptococcus higher biofilm inhibition of 18mm was observed. Similar with aspergillus and Canadian 12mm was observed. Aqueous showed more inhibition activity than methanol, chloroform taken in Indigofera tirunelvelica and Basella. In basella the inhibition activity is more promising. Pages: 116-126 Chapter: 12 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch012.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:116-126 Template-Type: ReDIF-Chapter 1.0 Author-Name: Mini Fernandez Author-Name: D. Keerthana Goud Title: In-Silico Studies of RBP7, CALML3, C35, LGALSL,S100A3 Proteins Involved in Breast Cancer Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: The most important reason for death caused by cancer in females around the globe is breast cancer. Early diagnosis, proper treatment are fundamental in order to enhance the outcomes of patients. This study aims to investigate various proteins that contribute to the development and progression of breast cancer. Proteins that were chosen for this work are: Retinoid-binding protein 7, Calmodulin-like protein 3, C35 protein, Galectinrelated protein and S100-A3 protein. In addition, three drugs: Raloxifene, Tamoxifen, and Fulvestrant were chosen to check if they have therapeutic effects on the above-mentioned proteins. By studying how these proteins interact with drugs, we can gain important information that could help create effective treatment. The protein sequences were retrieved from biological databases. Homology modelling was done to produce models for the selected proteins using SWISS-MODEL, then validated with help of ‘SAVES v6.0’ and ‘ProSA’. To identify places where drug would bind to the protein, surface pockets were characterized with the employment of ‘CASTp’. Each protein was then docked to three drugs using Vina in ‘PyRx’ and the most suitable conformation of the ligand was selected via splitting the ligand using ‘AutoDock Vina’. The protein-drug interactions were then visualised in ‘Biovia Discovery Studio’. The models were generated for proteins and were docked to the ligands. As predicted, the ligands successfully attached to amino acid residues in active site of the proteins by Hydrophobic bond, Hydrogen bond, Electrostatic Bond. By studying interactions between the proteins and the drugs, it is analysed about how the drug help to slow the progression of breast cancer. This highlights their potential as targeted treatments that could help stop tumor growth. This study also interprets which drug has more or less therapeutic effect for each protein that help in better treatment. Pages: 127-142 Chapter: 13 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch013.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:127-142 Template-Type: ReDIF-Chapter 1.0 Author-Name: Y. Aparna Author-Name: Gundlapalli Charanya Author-Name: Shankargari Sai Abhilash Author-Name: Tallam Yashaswini Title: Analysis of Influenza Datasets for Disease Prediction using AI and ML Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Influenza remains a significant global health concern, necessitating accurate predictive models for disease surveillance and management. This study analyzes influenza datasets collected from the CDC and WHO to predict disease trends using artificial intelligence (AI) and machine learning (ML) techniques. Data preprocessing was conducted to refine and structure the datasets for effective analysis. Various machine learning models, including Linear Regression, K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest, were tested to evaluate their predictive capabilities. Descriptive statistics, ANOVA, ARIMA modeling, and box plot analysis were performed to gain insights into the dataset's characteristics. Model performance was assessed using R² mean values and accuracy metrics. The SVM model demonstrated the highest predictive accuracy and was identified as the most effective model for forecasting influenza trends. Future disease predictions for the next four years were generated using the ML approach, providing valuable insights for public health planning. This study highlights the potential of AI-driven analytics in disease prediction and prevention. Pages: 143-152 Chapter: 14 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch014.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:143-152 Template-Type: ReDIF-Chapter 1.0 Author-Name: G. Sony Author-Name: M. Aishwarya Mole Author-Name: Y. Sabitha Title: Network Pharmacology based Approach on Potential Targets of Ocimum Sanctum Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Holy basil, or Ocimum sanctum, is a herb widely utilized in traditional medicine for various health advantages, such as a potential reduction of cholesterol. The single most important risk factor for heart disease is high cholesterol, and although statins and other medications similar to them are widely used, they have unwanted side effects. Consequently, the interest in natural remedies has increased. It is still not known precisely how Ocimum sanctum contributes to cholesterol management, however Holy basil, or Ocimum sanctum, is a herb widely utilized in traditional medicine for various health advantages, such as a potential reduction of cholesterol. In this research, we examined the interactions between the bioactive compounds of Ocimum sanctum and key proteins that play a role in cholesterol metabolism through network pharmacology and molecular docking. With the aid of bioinformatics tools such as IMPPAT, STITCH, STRING, ADMETLab, AutoDock Vina (molecular docking), and Discovery Studio, we examined the compounds of the plant and their effect on cholesterol-related target proteins. Based on our Studies, we have six key target proteins that are linked to oxidative stress, inflammation, and lipid metabolism: AMPK, MAPK, PPARα, PPARγ, CYP2C9, and ALOX12.Through molecular docking studies, it was discovered that Ocimum sanctum compounds were binding well to these proteins, suggesting potential cholesterol regulation. The ancient use of Ocimum sanctum in cholesterol control is complemented by computational data provided in this work. By the combination of molecular docking and network pharmacology, we highlight its possible multi-target activity. These findings extend our understanding of plant medicine and pave the way for further research in the laboratory to study the plant's possible role as a natural cholesterol-reducing agent. Pages: 153-165 Chapter: 15 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch015.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:153-165 Template-Type: ReDIF-Chapter 1.0 Author-Name: Deepaswitha Vishnubhotla Author-Name: R. Ramya Krithi Author-Name: A. Alekhya Author-Name: D. Sriya Author-Name: R. Deepika Title: Isolation and Characterization of Agrobacterium Tumefaciens from Crown Gall and Soil Samples Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Agrobacterium tumefaciens is popularly known as nature’s genetic engineer. It is a Gram-negative pathogenic bacterium which is generally found in soil. It causes tumour formation in plants due to its ability to perform inter-kingdom DNA transfer. Due to this characteristic of A. tumefaciens to act as a gene vector it is used as a gene jockeying tool. Its virulence is due to presence of Ti plasmid, T-DNA and vir regions of the plasmid. This paper focuses on the isolation and characterization of Agrobacterium tumefaciens isolated from two samples- crown gall present on Azadirachta indica (neem tree) and soil present around Phaseolus vulgaris (bean) plant. The tumour sample was surface sterilized using Tween-20, 70% ethanol and mercuric chloride solution while the soil sample was serially diluted. Isolation was done by streaking the samples onto MacConkey agar plates that is selective for Agrobacterium. On performing Gram staining it was determined that bacterium was Gram-negative. Antibiotic Sensitivity test performed using Kirby-Bauer method revealed that the isolate was sensitive to kanamycin (1.5 cm) and tetracycline (2.4 cm), visualized as zone of inhibition, while being resistant to cefuroxime and rifampin (0 cm). Biochemical tests revealed that the isolate was positive for motility test (highly motile), oxidase test (disc turns blue), catalase test (effervescence produced), citrate utilization test (Simmon’s citrate agar turned blue) and H2S production test (Kligler’s iron agar turned black). Positive result was obtained for the pathogenicity test in carrot, which confirmed tumour forming ability of the isolate. Leaves of brinjal plant which were infected with the isolate were then tested for GUS activity and blue coloured spots were observed which are positive for GUS assay. From the obtained results, it was concluded that the organism isolated was indeed Agrobacterium tumefaciens. However, the strain can be confirmed only by using advanced techniques such as DNA sequencing and polymerase chain reaction (PCR). Pages: 166-179 Chapter: 16 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch016.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:166-179 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Parijatham Kanchana Author-Name: Poojita Pabbu Author-Name: Y. Sabitha Title: In Bitro Studies on Alpha Glucosidase and Alpha Amylase Inhibitory Activities of Indigofera Tirunelvelica and Basella Alba Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Diabetes is a chronic, metabolic disorder characterized by Hyperglycaemia (elevated levels of blood glucose), which leads over time to serious damage to the heart, blood vessels, eyes etc. India is referred to as the “diabetes capital of the world” because. Around 77 million people are diagnosed with diabetes which makes it the second most affected country after China. Since antidiabetic drugs prescribed today have frequent side effects, research on plant-based alternatives is needed. This study aims to find out the antidiabetic activity of Indigofera tirunelvelica and Basella alba by evaluating their in vitro inhibitory enzyme activities. Treatment may involve inhibition of enzymes responsible for carbohydrate degradation, such as Alpha glucosidase and alpha amylase, in order to maintain blood sugar levels. For this experiment, the plant extract was made using methanol, chloroform, and distilled water. The activity of amylase and glucosidase was measured spectrophotometrically. Inhibitory potential was measured using regression equation. IC₅₀ values were calculated for all the samples including Acarbose. The IC₅₀ value of Acarbose(standard) was 13.91μg/ml. The IC₅₀ value of Indigofera tirunelvelica (36.83μg/ml) was less compared to Basella alba (53.96μg/ml) This indicates a stronger inhibitory effect of Indigofera tirunelvelica as it has the nearest value to that of the standard (Acarbose). Significant inhibition was seen in Indigofera tirunelvelica compared to Basella alba which suggests that, these plants can contribute for future research in the treatment of diabetes. Pages: 180-190 Chapter: 17 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch017.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:180-190 Template-Type: ReDIF-Chapter 1.0 Author-Name: Mini Fernandez Author-Name: G. Archana Title: Exploring Microbial Approaches for the Bioremediation of Heavy Metal-Contaminated Soils Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Bioremediation functions as a sustainable microbial-based approach to remove heavy metals from environments. The study examines how bacteria extracted from soil contamination areas of Balanagar (Hyderabad) and Eloor (Kerala) perform in bioremediation processes. Different biochemistry tests using IMViC procedures together with catalase activity tests helped identify multiple bacterial strains. This study examined metal absorption and microbial biomass growth along with heavy metal and Indole-3 acetic acid interrelations to assess microbial efficiency. Spectrophotometric measurements detected heavy metals to track their removal efficiency in the studied system. The laboratory data showed Gram-positive bacteria predominated in Sample 1 from Balanagar while Sample 2 from Eloor was characterized by Gram-negative bacteria. The analyzed samples proved to have catalase enzyme capabilities and presented equivalent outcomes from the IMViC testing method. The biomass quantity in Sample 2 grew significantly higher than Sample 1 particularly when Nickel was present yet Lead diminished biomass levels in both cases. The research demonstrates substantial nickel bioremediation capabilities of Gram-negative bacteria collected from Eloor yet more study needs to be done to optimize their performance. The study demonstrates how bacterial bioremediation handles heavy metal pollution while stressing the need to understand microbial metal uptake processes better to create stronger remediation solutions. Pages: 191-202 Chapter: 18 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch018.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:191-202 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Malathi Varma Author-Name: J. Nilaya Author-Name: V. Arpita Title: Revolutionizing Cancer Care: The Future of AI in Detection and Treatment Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: The integration of Artificial Intelligence (AI) in oncology is transforming cancer detection, diagnosis, and treatment, leading to more precise and personalized care. AI-powered algorithms are enhancing early cancer detection through advanced imaging analysis, reducing false positives and enabling faster diagnosis. Machine learning models trained on vast datasets are improving tumor classification, predicting disease progression, and identifying optimal treatment strategies tailored to individual patients. AI is also revolutionizing drug discovery by accelerating the identification of potential therapeutic compounds and optimizing clinical trial designs. In radiation therapy and radioisotope-based treatments, AI is refining dose planning and real-time monitoring, minimizing damage to healthy tissues while maximizing treatment efficacy. Additionally, AIdriven genomics and biomarker analysis are advancing personalized medicine, allowing for targeted therapies that improve patient outcomes. Despite these advancements, challenges such as data privacy, ethical concerns, and the need for regulatory approval must be addressed to ensure AI’s seamless integration into clinical practice. The collaboration between AI developers, oncologists, and regulatory bodies will be crucial in overcoming these hurdles. As AI continues to evolve, its role in oncology will expand, leading to more efficient, cost-effective, and patient-centric cancer care. This paper explores the latest innovations, challenges, and future directions of AI in cancer detection and treatment, highlighting it’s potential to revolutionize oncology and improve survival rates worldwide. Pages: 203-211 Chapter: 19 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch019.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:203-211 Template-Type: ReDIF-Chapter 1.0 Author-Name: K. Suman Author-Name: Lathika Das Title: Isolation of Yeast and Extraction of Superoxide Dismutase and ß Glucanase and Its Application in Agriculture Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Enzymes are highly efficient biocatalysts researched for industrial-scale catalysis because of their several distinct advantages. The present research includes the extraction of enzymes that have agricultural applications from yeast. The yeast cells were used to produce enzymes that had agricultural applications. The yeast cells were first isolated from two different curd sources. Isolation was done by serial dilution followed by the spread plate technique was employed for the collected curd samples. The isolates were grown on specific media like Yeast Potato Dextrose (YPD) medium. Cultural, and morphological study was performed for the selected colonies. The isolated yeast cells were screened for the production of enzymes like superoxide dismutase and β Glucanase. These enzymes are Significant and have extensive applications in agriculture. The yeast cells were then grown on YPD broth and incubated at 30°C for 24 h in an orbital shaking incubator at 180 rpm. The cells were centrifuged and lysed with cell-specific buffer for extraction of enzymes. Qualitative and Quantitative analysis was performed on the crude enzyme suspension to understand the activity rate of superoxide dismutase and β glucanase. The main study involves the action of the enzymes, superoxide dismutase and β glucanase on seed germination and plant growth. These enzymes can be used in modern agriculture to gain tolerance to stress conditions and reduce reliance on chemical inputs, making agriculture more eco-friendly. Pages: 212-228 Chapter: 20 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch020.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:212-228 Template-Type: ReDIF-Chapter 1.0 Author-Name: Anirban Dash Author-Name: Mirza Fareedulla Baig Title: Revolutionizing Health─Care: In-Silico Vaccine Designing Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: In-Silico Vaccine Designing represents a paradigm shift in vaccine research, using computational tools to accelerate and refine the development process. By integrating bioinformatics, immunoinformatics, and structural biology, this approach enables researchers to identify and predict antigenic regions with high precision, reducing the time and cost associated with traditional vaccine development. Tuberculosis (TB) is a global health threat, making it necessary to develop innovative vaccine strategies. This study focuses on the insilico design of a multi-epitope vaccine targeting the integral transmembrane protein kefB of Mycobacterium tuberculosis. The amino acid sequence of kefB was retrieved from UniProt, and antigenic epitopes were identified using computational tools. B-cell epitopes were predicted via LB-tope - ABCpred, while T - cell epitopes (MHCI and MHC-II) were derived from IEDB. The best antigenic epitopes were selected based on their VaxiJen scores, followed by allergenicity prediction using AllerTOP. A vaccine construct was designed by linking the epitopes to a 50s ribosomal protein adjuvant using linkers. The construct was modified in 3D using SwissModel, visualized with Chimera, and validated through structural assessment tools, including the Ramachandran Plot via MOLProbity and refinement by GalaxyWEB. The final construct was docked with the Toll-like Receptor 2 (TLR2) of Homo sapiens using PatchDock to ensure receptor-ligand compatibility. Codon optimization was performed using JCat for improved expression, and a pET vector was designed for potential cloning. Host immune response simulation (CImmSim) and population coverage analysis (IEDB) confirmed the vaccine’s immunogenic potential. This in-silico approach highlights a promising strategy for vaccine development against tuberculosis and emphasizes the potential of computational biology in accelerating vaccine research thus revolutionizing health care. Pages: 229-241 Chapter: 21 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch021.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:229-241 Template-Type: ReDIF-Chapter 1.0 Author-Name: G. Sony Author-Name: Md Mehdiya Muskaan Author-Name: Y. Sabitha Title: In-Silico Identification of Structural Changes and Molecular Interactions of Mutations in Multiple Drug Resistant (MDR) Bacteria Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: In-Silico Vaccine Designing represents a paradigm shift in vaccine research, using computational tools to accelerate and refine the development process. By integrating bioinformatics, immunoinformatics, and structural biology, this approach enables researchers to identify and predict antigenic regions with high precision, reducing the time and cost associated with traditional vaccine development. Tuberculosis (TB) is a global health threat, making it necessary to develop innovative vaccine strategies. This study focuses on the insilico design of a multi-epitope vaccine targeting the integral transmembrane protein kefB of Mycobacterium tuberculosis. The amino acid sequence of kefB was retrieved from UniProt, and antigenic epitopes were identified using computational tools. B-cell epitopes were predicted via LB-tope - ABCpred, while T - cell epitopes (MHCI and MHC-II) were derived from IEDB. The best antigenic epitopes were selected based on their VaxiJen scores, followed by allergenicity prediction using AllerTOP. A vaccine construct was designed by linking the epitopes to a 50s ribosomal protein adjuvant using linkers. The construct was modified in 3D using SwissModel, visualized with Chimera, and validated through structural assessment tools, including the Ramachandran Plot via MOLProbity and refinement by GalaxyWEB. The final construct was docked with the Toll-like Receptor 2 (TLR2) of Homo sapiens using PatchDock to ensure receptor-ligand compatibility. Codon optimization was performed using JCat for improved expression, and a pET vector was designed for potential cloning. Host immune response simulation (CImmSim) and population coverage analysis (IEDB) confirmed the vaccine’s immunogenic potential. This in-silico approach highlights a promising strategy for vaccine development against tuberculosis and emphasizes the potential of computational biology in accelerating vaccine research thus revolutionizing health care. Pages: 242-254 Chapter: 22 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch022.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:242-254 Template-Type: ReDIF-Chapter 1.0 Author-Name: D. Manju Bhargavi Author-Name: Ch. Kavyasri Author-Name: V. Jeevana Jyothi Title: Nanotechnology: Pioneering Drug Discovery and Delivery Innovations Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Nanomaterials have revolutionized the field of drug discovery and delivery by enabling targeted, efficient, and controlled release of therapeutics. This review explores the role of nanotechnology in drug development, with a focus on nanocarriers such as liposomes, dendrimers, polymeric nanoparticles, and inorganic nanomaterials. Key advantages include improved bioavailability, reduced side effects, and enhanced therapeutic efficacy. Several case studies highlight the success of nanomaterials in treating diseases like cancer, neurodegenerative disorders, and infectious diseases. The literature survey examines recent advancements and challenges in the clinical translation of nanomedicines. While nanotechnology offers immense potential in pharmaceutical sciences, concerns related to toxicity, scalability, and regulatory approval must be addressed for widespread adoption. This review underscores the need for interdisciplinary research to further harness the benefits of nanotechnology in medicine. Pages: 255-262 Chapter: 23 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch023.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:255-262 Template-Type: ReDIF-Chapter 1.0 Author-Name: S. Vanitha Author-Name: Vamika Anil Author-Name: S. Prerna Title: Nanotechnology and Tissue Engineering: Building the Future of Regenerative Medicine Book-Title: Convergence of Technology & Biology ─ Transforming Life Sciences Editor-Name: Malathi Varma Editor-Name: S.Parijatham Kanchana Editor-Name: G.Sony ISBN: 978-93-6163-763-6 Abstract: Nanotechnology offers groundbreaking methods for medical diagnosis, treatment, tissue regeneration, and biomedical imaging. The small size of nanoparticles (1-100 nanometers) facilitates interaction with biological systems, improving the sensitivity and specificity of disease detection through enhanced imaging. Nanotechnology reshapes dentistry, improving diagnostics, treatment precision, and dental materials. Developing nanocomposites, nano-adhesives, and nano-bone grafts represents a significant advancement in dental materials. Nanorobots offer the potential for pain-free procedures and targeted drug delivery within the oral cavity. Nanostructured scaffolds used in tissue engineering mimic the extracellular matrix. It promotes cell growth and tissue regeneration. This technology plays a key role in improving the effective monitoring of bioelectric signals in cancer treatment using advanced sensors and electrodes. It revolutionizes surgery by enabling high-precision, minimally invasive procedures through nanorobotic technology. Organ-on-a-chip (OoC) systems offer an innovative and versatile micro-physiological platform for replicating the dynamic tissue microenvironment, enabling the study of nanotechnology-biology interactions. Researchers are using models that combine nanotechnology, microfluidics, and tissue engineering to mimic the complex dynamics of musculoskeletal tissues in a controlled manner. This allows them to effectively study disease mechanisms and assess therapeutic approaches, such as nanotechnology-based interventions. Scientists are leveraging the integration of nanotechnology with other technologies like tissue engineering to achieve targeted drug delivery, precise sensing, and versatile manipulation of cellular processes, driving a paradigm shift in research at the cellular level and early disease diagnosis and possible prognosis. Nevertheless, resolving safety and bioaccumulation issues is a prerequisite for its broader clinical translation. Pages: 263-270 Chapter: 24 Year: 2025 Month: March File-URL: https://www.shanlaxpublications.com/p/ctbtls/ch024.pdf File-Format: Application/pdf Handle: RePEc:dax:ctbtls:978-93-6163-763-6:p:263-270