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MOLECULAR DOCKING-BASED EVALUATION OF QUERCETIN AS A MULTI-TARGET THERAPEUTIC AGENT IN ESTROGEN-ASSOCIATED GALLBLADDER CARCINOGENESIS

2026-05-01T00:00:00Z

Title: MOLECULAR DOCKING-BASED EVALUATION OF QUERCETIN AS A MULTI-TARGET THERAPEUTIC AGENT IN ESTROGEN-ASSOCIATED GALLBLADDER CARCINOGENESIS Authors: SIRO, VICTORIA; BHARADVAJA, NAVNEETA (SUPERVISOR) Abstract: Gallbladder carcinoma (GBC) is an intense and highly invasive malignant cancer of the biliary tract associated with unfavourable prognosis and high mortality due to late-stage diagnosis. Chronic inflammation, oxidative stress, hormonal imbalance, and dysregulation of signaling pathways such as PI3K/AKT, MAPK, and NF-κB play major roles in the progression of estrogen-associated gallbladder carcinogenesis. Estrogen-mediated signaling further contributes to tumour development by promoting gallstone formation, abnormal cell proliferation, angiogenesis, and resistance to apoptosis. Considering the complex and multi-factorial nature of gallbladder cancer, there is a growing need for therapeutic agents capable of targeting multiple molecular pathways simultaneously. The present study aimed to evaluate the multi-target therapeutic potential of quercetin, a naturally occurring flavonoid, against estrogen-associated gallbladder carcinogenesis using molecular docking and network pharmacology approaches. Potential protein targets of quercetin were identified using SwissTargetPrediction, while gallbladder disease- associated genes were collected from GeneCards. Key hub proteins including AKT1, PIK3CA, EGFR, SRC, and BRAF were selected for further investigation. Molecular docking studies were performed using PyRx integrated with AutoDock Vina. Among the selected proteins, AKT1 exhibited the strongest binding affinity with quercetin, showing a docking score of approximately −9.7 kcal/mol, indicating stable and favourable interaction. Interaction analysis demonstrated the presence of multiple hydrogen bonds and hydrophobic interactions within the active binding site, confirming structural compatibility between quercetin and the target protein. Visualization studies using PyMOL and Discovery Studio further validated the docking results. The findings of this study suggest that quercetin possesses significant multi-target therapeutic potential by modulating critical signalling pathways involved in gallbladder carcinogenesis, particularly the PI3K/AKT pathway. Therefore, quercetin may serve as a promising natural compound for the development of future therapeutic strategies against estrogen-associated gallbladder cancer. Further in vitro, in vivo, and clinical studies are recommended to validate its therapeutic efficacy and safety.

PHYTOREMEDIATION BASED EVALUATION OF AGROCHEMICAL CONTAMINATION IN SOIL AND WATER: A SIMPLE INDEX BASED, ANALYTICAL, AND ECO-TOXICOLOGICAL PERSPECTIVE

2026-05-01T00:00:00Z

Title: PHYTOREMEDIATION BASED EVALUATION OF AGROCHEMICAL CONTAMINATION IN SOIL AND WATER: A SIMPLE INDEX BASED, ANALYTICAL, AND ECO-TOXICOLOGICAL PERSPECTIVE Authors: VIRMANI, LAKSHAY; VIJ, DAANISH; Bhandari, Kriti (SUPERVISOR) Abstract: Aim The goal of the present study was to assess the levels of agrochemical contamination in soil and water and to evaluate the impact on the environment and sustainability of phytoremediation as a remediation method. Atrazine and chlorpyrifos were chosen as model agrochemical pollutants because of their universal usage in agricultural production and environmental persistence. Water lettuce, Hydrothylax, are commonly found in shallow waters. The aquatic macrophytes that are prevalent in the shallow waters are Eichhornia crassipes (Water Hyacinth) and Hydrothylax. Lemna minor (Duckweed), were used for phytoremediation studies. The work became integrated with High-Spatial analysis, Performance Liquid Liquid Chromatography (HPLC), ecological risk assessment, and statistical analysis are used to analyze the data and ecotoxicological assessment to assess the efficiency of contaminant removal and the environmental recovery. Results Before and after phytoremediation treatment HPLC results showed that significant decrease in atrazine and chlorpyrifos levels. The decrease in peak area of the chromatograms proved that both the plant species were effective at removing contaminants. Among the tested, Duckweed did not show high remediation efficiency when compared to water hyacinth. The more biomass and root system it has, the higher toxicity removal capacity. The decrease of the pollutant levels led to decrease in Contamination Index (CI) and Hazard Quotient (HQ) values, which shows reduced Ecological risk following treatment. Further, analysis of data using mean, standard deviation, one way analysis ANOVA and Tukey's post hoc test results showed that there was significant difference among the treatment groups (p < 0.05). Additionally, ecotoxicological evaluation using germination bioassay of the seeds showed that the germination percentage and root growth in treated samples was improved, which signifies reduced phytotoxicity and improvement of environmental quality. Conclusion The research findings showed that phytoremediation is an effective, environmentally friendly and cost-efficient method for decreasing the concentration of agrochemicals from soil and water. Eichhornia crassipes and Lemna minor were found to be promising species with potential for clean up, with water hyacinth having superior removal efficiency of contaminants. An HPLC 6 based analytical monitoring, coupled with ecological indices, statistical validation and assessment of ecotoxicological effects fulfilled a comprehensive monitoring framework for environmental restoration. In conclusion, results indicate that phytoremediation could be an effective and sustainable approach for agrochemical pollution management and ecological conservation.

IN SILICO EVALUATION OF NATURAL POLYPHENOLS AS POTENTIAL MODULATORS OF TREM 2 IN ALZHEIMER’S DISEASES

2026-05-01T00:00:00Z

Title: IN SILICO EVALUATION OF NATURAL POLYPHENOLS AS POTENTIAL MODULATORS OF TREM 2 IN ALZHEIMER’S DISEASES Authors: RAJKONWAREE, NAINA; Verma, Smita Rastogi (SUPERVISOR) Abstract: The Alzheimer’s Disease in today’s world is one of the most leading and progressively debilitating neurodegenerative diseases characterized due to the deposition of beta-amyloid plaques, and presence of tau tangles, and neuroinflammation. Although years of research have been put into finding out a cure for this progressive disease, very little success has been achieved, thus necessitating the need for new targets to be discovered. Triggering Receptor Expressed found on Myeloid Cells-2 (TREM2) has proven to be recognized as an important immunoreceptor which is expressed mainly on microglia innate immune cells which are found on the Central Nervous System (CNS) and they act as the brain's first responders to pathological stimuli. TREM2 acts as a vital regulatory receptor of microglial homeostasis by controlling various neuroprotective mechanisms such as phagocytosis of Amyloid-Beta deposits, regulation of neuroinflammatory processes, microglial survival/proliferation, and synapse preservation. There are many considerable evidence based on genetic studies and functional analysis which proves that a lack of function mutations or improper regulation of TREM2 signaling pathways results in poor phagocytosis, persistent neuroinflammation, and faster progression of Alzheimer's Disease (AD). In contrast to this, it also proves that enhancing TREM2 activity improves neuroprotective responses of microglial cells; hence, TREM2 is considered as an appealing candidate for pharmacological modulation to achieve disease modification therapies for AD. In light of the increasing attention paid to natural bioactive molecules as novel neurotherapeutic agents, this study explores the ability of naturally occurring plant polyphenols as potential TREM2 regulators. Epigallocatechin Gallate (EGCG), apigenin, curcumin, and resveratrol are widely recognized to possess potent natural reductive phytocompounds, inflammation suppressing effects, the ability to pass through the Blood Brain Barrier, and neuroprotective properties. By evaluating the molecular interactions between TREM2 and these polyphenolic compounds, a computational in silico approach employing molecular docking was adopted in the present study. The 3D crystal configuration of TREM2 (PDB ID: 5ELI) was derived from the RCSB PDB i.e Protein Data Bank and used as the macromolecular target. In Silico docking activation were stimulated to systematically Evaluate the binding energies, interaction patterns and structural complementarity of EGCG, apigenin, curcumin, and resveratrol within the TREM2 binding site. The docking analysis revealed a distinct hierarchy of binding affinities among the four polyphenols. Apigenin exhibited the highest binding affinity of −8.6 kcal/mol followed closely by EGCG at −8.5 kcal/mol, indicating that both compounds form thermodynamically favorable, stable, and highly specific interactions with TREM2. The strong binding energies observed for apigenin and EGCG suggest their capacity to engage critical residues within the TREM2 active site through multiple non-covalent interactions, H-bond mediated stabilization, apolar molecular contacts, and van der Waals forces, thereby stabilizing the receptor-ligand complex. Curcumin exhibited a moderate binding affinity of −7.1 kcal/mol, indicating reasonable but comparatively weaker interaction, while resveratrol recorded the lowest binding affinity of −6.3 kcal/mol among the tested compounds, suggesting limited binding efficiency with TREM2. The superior binding affinities of apigenin and EGCG highlight their strong potential to modulate TREM2 receptor activity and, consequently, influence downstream microglial functional responses central to AD pathology — most notably neuroinflammation regulation and amyloid-β phagocytic clearance. These findings suggest that both apigenin and EGCG can be taken as lead phytochemical scaffolds for the evolution of TREM2-targeted neuroprotective therapeutics. While the results of this in silico molecular docking study are preliminary in nature, they provide a robust computational foundation and mechanistic rationale for advancing these polyphenolic candidates into subsequent stages of experimental validation, including in silico binding, cell-based operative studies in microglial models, and ultimately in vivo preclinical investigations. Collectively, this study contributes meaningful insights into the therapeutic modulation of TREM2 signaling through natural polyphenols and paves new pathways for the advancement of effective, plant-based interventional strategies for Alzheimer's disease.

MACHINE LEARNING-DRIVEN FRAMEWORK FOR EARLY CANCER DETECTION USING CELLULAR MORPHOMETRIC FEATURES

2026-05-01T00:00:00Z

Title: MACHINE LEARNING-DRIVEN FRAMEWORK FOR EARLY CANCER DETECTION USING CELLULAR MORPHOMETRIC FEATURES Authors: ANSARI, RIFAH; Verma, Smita Rastogi (SUPERVISOR) Abstract: Background: Cancer remains a leading global health challenge, with breast cancer specifically representing a significant portion of oncology caseloads worldwide. Despite advancements in treatment modalities, early detection remains notoriously difficult, and diagnoses often occur at advanced stages where therapeutic efficacy is diminished. Conventional pathological analysis is heavily time-intensive and uniquely subject to inter-observer variability, especially in border-line cases. Artificial Intelligence (AI) and machine learning offer promising pathways to augment clinical accuracy, improve workflow efficiency, and facilitate truly personalized care Objective: To develop, validate, and comprehensively evaluate an interpretable machine learning framework for the binary classification and clinical risk stratification of breast cancer using structured cellular morphometrics. This framework is explicitly designed to serve as a foundational, highly transparent module for a broader multimodal Clinical Decision Support System (CDSS) [2]. Methods: Utilizing the highly validated Breast Cancer Wisconsin (Diagnostic) dataset ( ), four distinct machine learning models (Logistic Regression, Random Forest, Gradient Boosting, and a Multi-Layer Perceptron Neural Network) were trained to classify cytological tumors as benign or malignant. The optimal model was subsequently leveraged to generate continuous probability distributions, establishing actionable, data-driven clinical risk thresholds (Low, Intermediate, High) [5], [6]. Results: All evaluated models demonstrated exceptionally high discriminative performance. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUROC) at 0.9960, with a sensitivity of 0.9286 and specificity of 0.9861. Feature importance analysis illuminated the biological mechanisms driving the algorithms, revealing that cellular "worst area" and "worst concave points" were the strongest predictors of malignancy. Furthermore, the risk stratification framework successfully separated benign and malignant probability densities, effectively minimizing clinical ambiguity and creating a clear "grey zone" for targeted physician review [7]. Conclusion: The proposed AI-CDSS provides a highly accurate, computationally efficient, and rigorously interpretable method for early breast cancer detection. By providing probabilistic risk stratification rather than mere binary outputs, the system effectively supports clinical triaging and personalized patient management without functioning as an opaque "black box"