Clinical – ASSET

Artificial Intelligence in Healthcare

admin — Fri, 26 Dec 2025 09:14:59 +0000

Artificial Intelligence (AI) has emerged as a pivotal driver of innovation in healthcare, reshaping diagnostic, therapeutic, and administrative paradigms. Recent systematic reviews highlight AI’s capacity to enhance clinical decision-making, medical imaging interpretation, and personalized treatment planning, thereby reducing diagnostic errors and improving patient outcomes. Advanced methodologies such as machine learning, deep learning, and natural language processing enable the rapid analysis of complex datasets, supporting early disease detection and predictive analytics for patient deterioration, epidemic surveillance, and hospital resource allocation. Beyond clinical applications, AI contributes to operational efficiency by automating administrative workflows including electronic health record management, patient scheduling, and billing, allowing healthcare professionals to devote greater attention to patient-centered care. The integration of AI into wearable devices and remote monitoring systems further facilitates continuous patient observation and proactive health management. Despite these advances, challenges persist. Concerns regarding data privacy, ethical governance, algorithmic bias, and regulatory compliance remain central to responsible AI adoption. Ensuring transparency, explainability, and human oversight is critical to mitigating risks and fostering trust among stakeholders. Overall, AI demonstrates immense potential to revolutionize healthcare by improving efficiency, accuracy, and accessibility of medical services. Sustained interdisciplinary collaboration, coupled with robust ethical frameworks, will be essential to harness AI’s transformative capabilities for building a resilient, equitable, and patient-centered healthcare ecosystem.

Authors List :

Syeda Amatul Raheem Shafiya, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

Syeda Amatul Raheem Shafiya

Affiliations :

Anwarul Uloom College

Email :

shafia.2106@gmail.com

Key Words (5 Words Maximum) :

Artificial Intelligence, Clinical Translation, Deep Learning, Predictive Modelling

Emerging Genetic Biomarkers for Breast Cancer Diagnosis

admin — Thu, 25 Dec 2025 20:17:35 +0000

Breast cancer is a complex and heterogeneous disease, and early detection is crucial for improving patient outcomes. Recent advances in genomic technology have facilitated the identification of emerging genetic biomarkers that extend beyond traditional markers such as BRCA1 and BRCA2. BRCA1 and BRCA2 have limitations in identifying high risk individuals and predicting treatment responses. This review synthesizes current research on novel candidate change and genetic alternation that demonstrate potential clinical relevance in brief cancer diagnosis and management. This shift is unable more accurate early detection,refined risk satisfaction and the development of high personalized treatment strategies. Emerging research synthesizes a wide array of new candidate genes and molecular signatures with significant potential for clinical integration. Liquid biopsy biomarkers:Circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), offer non- invasive real time monitoring of tumor dynamics. High penetrance genes:Beyond BRCA1/2, genes like PALB2, CHEK2,ATM and PIK3CA, which contribute to inherited suspectibility and Hereditary breast cancer risk.Moderate risk genes:Variants in genes like RAD51C/D and BARD1 my inform risk satisfaction.Gene expressions signatures:Multi gene panels (e.g., oncotype DX, Mamma Print) predict recurrence risk and guide treatment decisions. Epigenetic markers:DNA methylation patterns, microRNAs and histone modifications are emerging as potential diagnostics. Emerging genetics hold significant potential for refining this cancel diagnosis and management and emphasize the importance of integrating genomic data into clinical practice to advance precision medicine increase cancer care.

Authors List :

Aamna Sultana, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Aamna Sultana

Affiliations :

Anwarul Uloom College

Email :

aamnasultana2@gmail.com

Key Words (5 Words Maximum) :

Breast Cancer; BRCA1,2; Liquid biopsy biomarkers: Circulating tumor DNA (ctDNA); circulating tumor cells (CTCs)

Integrative Bioengineering: Multiscale Control and Functional Design in Programmable Morphogenetic Agents (Xenobots)

admin — Thu, 25 Dec 2025 20:12:15 +0000

The emergence of synthetic biological machines necessitates a departure from classical reductionist frameworks toward an integrative paradigm combining developmental biology, information theory, and robotics. This study evaluates the transition from passive cellular assemblies to autonomous, programmable living systems, characterized by the development of “Xenobots”—millimeter-scale bio-machines derived from Xenopus laevis progenitors. We analyze the synthesis of these organisms through the lens of computational morphogenetics and multiscale control. By repurposing pluripotent contractile and epithelial cells, these constructs are engineered to execute specific kinematic tasks. We examine the principles of self-assembly and functional plasticity, arguing that organizational control mechanisms are substrate-independent and can be modeled using algorithmic frameworks. Xenobots demonstrate unique emergent properties, including autonomous locomotion, collective behavior, and robust self-repair following physical trauma. Unlike traditional soft robotics, these biological agents offer complete biodegradability and metabolic efficiency. The synthesis of these systems challenges the conventional dichotomy between “living organisms” and “engineered machines,” suggesting that biological media can serve as a programmable interface for complex task execution. The convergence of developmental biology and bioengineering provides a robust framework for designing adaptive, biodegradable tools for targeted drug delivery and regenerative medicine. By leveraging the inherent intelligence of cellular collectives, this integrative approach opens new frontiers in the creation of biocompatible machines capable of operating within complex physiological environments.

Authors List :

Shaza asim khan, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

Shaza asim khan

Affiliations :

Anwarul Uloom College

Email :

Khanshazaasim@gmail.com

Key Words (5 Words Maximum) :

Xenobots, Synthetic Biology, Bioengineering, Morphogenesis, Programmable Organisms, Soft Robotics.

From Passive Generation to Autonomous Agency: A Comprehensive Survey of Agentic AI Architectures

admin — Thu, 25 Dec 2025 20:11:01 +0000

The evolution of Artificial Intelligence (AI) from passive generative models to autonomous, goal-driven entities marks a paradigm shift toward Agentic AI. This survey synthesizes recent advancements in agentic architectures, offering a comprehensive analysis of frameworks that transcend traditional Large Language Models (LLMs) by incorporating autonomy, contextual reasoning, tool integration, and environmental feedback loops. Drawing on over 50 state-of-the-art implementations, we classify agentic systems into three principal lineages: symbolic, neural, and hybrid. Symbolic agents rely on persistent state and algorithmic planning, while neural agents leverage stochastic generation and prompt-driven orchestration; hybrid models blend these modalities to enhance adaptability and scalability. A prominent trend identified is the emergence of collaborative agent ecosystems, wherein multiple specialized agents coordinate to execute complex, multi-step workflows—ranging from scientific discovery to software engineering. These systems demonstrate increasing competence in long-horizon planning, dynamic tool selection, and inter-agent communication. However, the transition from sandboxed experimentation to enterprise-grade deployment is hindered by unresolved challenges in reliability, safety, and evaluation metrics. We highlight the need for standardized benchmarks, robust interpretability frameworks, and fail-safe mechanisms to ensure trustworthy operation in real-world environments. This review positions Agentic AI as a foundational layer for next-generation intelligent systems, with implications for autonomous research assistants, adaptive robotics, and scalable decision-making platforms. By bridging conceptual gaps between reactive generation and proactive agency, this work contributes to the theoretical and practical scaffolding required for building resilient, collaborative, and context-aware AI agents

Authors List :

Kubra Fatima, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Kubra Fatima

Affiliations :

Anwarul Uloom College

Email :

kayrafatima7722@gmail.com

Key Words (5 Words Maximum) :

Agentic AI, Autonomous Agents, Symbolic Reasoning, Neural Architectures, Tool-Use, Multi-Agent Systems, Safety in AI, AI Evaluation Metrics

Molecular Diagnostics: Transforming Infectious Disease Detection

admin — Thu, 25 Dec 2025 19:58:06 +0000

Molecular diagnostics have revolutionized the detection and management of infectious diseases, addressing one of modern medicine’s greatest challenges: identifying the cause of infection quickly and accurately. Traditional diagnostic methods often required days for bacterial or fungal cultures to grow in the laboratory, delaying treatment decisions. In contrast, recent advances in molecular technologies—such as Polymerase Chain Reaction (PCR), DNA sequencing, and biomarker identification—have transformed clinical practice by enabling rapid, precise, and comprehensive pathogen detection. PCR remains a cornerstone in clinical diagnostics, capable of detecting minute quantities of pathogen genetic material within hours. Next-Generation Sequencing (NGS) has further expanded diagnostic capabilities by providing complete genomic profiles of infectious agents. This allows clinicians to identify rare pathogens, monitor viral mutations, and predict antimicrobial resistance from a single patient sample. Biomarker analysis adds another dimension, leveraging signals from the host immune response to distinguish between bacterial and viral infections, thereby guiding appropriate antibiotic use and supporting early diagnosis and treatment stratification. The integration of these technologies into routine clinical workflows has significantly improved diagnostic accuracy, accelerated therapeutic decision-making, and enhanced patient outcomes, particularly in critical care settings. While challenges remain—such as cost, data interpretation, and infrastructure requirements—ongoing innovations and increasing accessibility are expected to broaden their impact. By transitioning these tools from specialized research laboratories into everyday hospital practice, molecular diagnostics are driving the emergence of precision medicine for infectious diseases. Ultimately, these advances reduce hospital stays, lower healthcare costs, and, most importantly, save lives by ensuring timely and targeted interventions.

Authors List :

Nishat Fatima, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

NISHAT FATIMA

Affiliations :

Anwarul Uloom College

Email :

fatimannishat@gmail.com

Key Words (5 Words Maximum) :

Biomarkers, Data interpretation, Infectious Diseases, Molecular Diagnostics, Polymerase chain reaction

Quantum Computing in Drug Discovery: Accelerating the Path to Novel Therapeutics

admin — Thu, 25 Dec 2025 19:51:30 +0000

The pharmaceutical industry faces mounting challenges in developing new drugs, with traditional computational methods often falling short when modeling complex molecular interactions. Quantum computing has emerged as a promising solution to overcome these limitations, offering unprecedented computational power to tackle problems that are intractable for classical computers. By leveraging quantum mechanical principles such as superposition and entanglement, quantum algorithms can simulate molecular behavior with remarkable accuracy, enabling researchers to predict drug-target interactions, optimize molecular structures, and identify promising drug candidates more efficiently. Current applications of quantum computing in drug discovery focus on several key areas: molecular simulation, protein folding prediction, and optimization of lead compounds. Unlike classical computers that struggle with the exponential complexity of quantum systems, quantum computers can naturally represent and manipulate quantum states, making them ideal for modeling chemical reactions and molecular dynamics. Early implementations have demonstrated significant potential in reducing the time and cost associated with the drug discovery pipeline, which traditionally spans over a decade and costs billions of dollars. Despite these promising advances, practical challenges remain. Today’s quantum computers are still in the noisy intermediate-scale quantum (NISQ) era, characterized by limited qubit counts and high error rates. However, as hardware improves and hybrid quantum-classical algorithms become more sophisticated, the pharmaceutical industry is increasingly investing in quantum computing partnerships and research initiatives. This convergence of quantum technology and pharmaceutical science represents a paradigm shift that could revolutionize how we discover and develop life-saving medications in the coming decades.

Authors List :

Mohammed Mansoor Waheed, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Mohammed Mansoor Waheed

Affiliations :

Anwarul Uloom College

Email :

mohammadmansoor651@gmail.com

Key Words (5 Words Maximum) :

Drug Discovery, Molecular Simulation, noisy intermediate-scale quantum (NISQ), Protein Folding, Quantum Dots,

Immune Empowerment in Oncology: A New Era of Healing

admin — Thu, 25 Dec 2025 19:23:58 +0000

Immunotherapy, often abbreviated as IT, is a modern medical treatment that works by strengthening or modifying the body’s own immune system to fight against diseases, like cancer. Unlike traditional treatments such as chemotherapy or radiation, which directly target diseased cells but not precisely selective, immunotherapy helps the immune system recognize and attack harmful cells more effectively. Because of this unique approach, it has become one of the most promising advances in medical science. In cancer treatment, immunotherapy is frequently referred to as Immuno-Oncology (IO). This term is commonly used in hospitals, research papers, and clinical discussions. Within IO, there are several specialized forms of immunotherapy. One important category is ICI, which stands for Immune Checkpoint Inhibitors. These drugs help “unlock” the immune system so it can recognize and destroy cancer cells that would otherwise go unnoticed. Another advanced form is CAR-T therapy (Chimeric Antigen Receptor T-cell therapy). In this method, a patient’s T-cells are modified in a laboratory to better target cancer, then returned to the body. Adoptive Cell Therapy (ACT), is a broader term that includes CAR-T and similar treatments involving immune cells. In medical notes and presentations, shorter abbreviations like IMT, ImTx, or iTx are also used to refer to immunotherapy in a simplified way. These abbreviations make communication easier for doctors and researchers while discussing complex treatments.

Authors List :

Shoeb Khan, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

SHOEB KHAN

Affiliations :

Anwarul Uloom College

Email :

shoaibkhan28563@gmail.com

Key Words (5 Words Maximum) :

Adoptive Cell Therapy (ACT), Immunotherapy, Cancer, Chimeric Antigen Receptor T-cell (CAR-T) therapy, Immune Checkpoint Inhibitors (ICI)

Emerging Genetic Biomarkers for Breast Cancer Diagnosis

admin — Thu, 25 Dec 2025 19:16:27 +0000

Breast cancer remains a heterogeneous malignancy in which early detection and molecular stratification are critical for improving patient outcomes. While BRCA1 and BRCA2 mutations have historically defined hereditary risk, their predictive limitations have prompted the search for broader genomic and epigenomic markers. Recent advances in next-generation sequencing, liquid biopsy technologies, and multi-gene expression profiling have expanded the biomarker landscape, enabling more precise risk assessment and therapeutic decision-making. This review synthesizes current evidence on emerging genetic biomarkers with potential clinical utility. High-penetrance genes such as PALB2, CHEK2, ATM, and PIK3CA are increasingly recognized for their role in hereditary susceptibility, while moderate-risk variants in RAD51C/D and BARD1 refine individualized risk stratification. Liquid biopsy approaches, including circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), provide non-invasive, real-time monitoring of tumor dynamics and treatment response. Gene expression signatures (e.g., Oncotype DX, MammaPrint) have demonstrated prognostic value in predicting recurrence risk and guiding adjuvant therapy. Furthermore, epigenetic markers—such as DNA methylation profiles, microRNAs, and histone modifications—are emerging as promising diagnostic and predictive tools, reflecting tumor heterogeneity and microenvironmental influences. Collectively, these advances highlight a paradigm shift toward precision oncology, where integrated genomic and epigenomic data inform personalized screening, risk stratification, and therapeutic strategies. The incorporation of novel biomarkers into clinical practice has the potential to enhance early detection, optimize treatment selection, and improve long-term survival. Continued validation in large-scale, multi-ethnic cohorts and harmonization of biomarker-driven guidelines remain essential for translating these discoveries into routine cancer care

Authors List :

Aamna sultana

Presenting Author :

Aamna Sultana, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Affiliations :

Anwarul Uloom College

Email :

aamnasultana2@gmail.com

Key Words (5 Words Maximum) :

Biomarkers, Breast Cancer, DNA Methylation, Liquid Biopsy, microRNAs

Patient-Centric Pharmacovigilance: Development of a Mobile Application for Direct Adverse Drug Reaction Reporting.

admin — Sat, 20 Dec 2025 19:00:59 +0000

The increasing prevalence of adverse drug reactions (ADRs) poses a significant threat to public health, while underreporting remains a major limitation of existing pharmacovigilance systems. This paper presents the development of a mobile application, designed to facilitate direct, timely, and structured reporting of ADRs by patients and healthcare professionals.
The application features a user-friendly interface, standardizes ADR reporting forms, and secures data storage to ensure data integrity and confidentiality.
The application aims to improve accessibility, reduces reporting barriers, and enhances patient participation in pharmacovigilance. By empowering patients to report ADRs, we seek to improve drug safety and healthcare outcomes.
The ADR Reporter app demonstrates potential in bridging gaps in existing pharmacovigilance systems, promoting a scalable and patient-centric approach to medication safety, with future potential for integration into national ADR monitoring frameworks.

Authors List :

Mohammad Roohan, Aqsa Athar, Syed Ziaur Rahman

Presenting Author :

Mohammad Roohan, Aqsa Athar

Affiliations :

Department of Pharmacology, JNMC, AMU, Aligarh, India

Email :

m.roohan2010@gmail.com

Key Words (5 Words Maximum) :

ADR, Pharmacovigilance, Reporting

An in-silico analysis of pentraxin-3 as a predictor of ethanol and morphine induced inflammation:

admin — Fri, 06 Dec 2024 02:52:28 +0000

Alcohol and opioid abuse remain endemic issues in the United States and the world at large. In the United States alone alcohol abuse accounts for 178,000 deaths annually, and opioids most recently accounted for 81,806 deaths in 2022. Their deleterious effects on societies worldwide cannot be understated, as they are both leading causes of preventable death. Literature and clinical evaluation demonstrate their effects in short term intoxication as well as long term systemic effects. Alcohol abuse is one of the premier causes of fatty liver disease, an inflammatory process characterized by elevated liver transaminases, ascites, jaundice, altered mental status, eventually resulting in liver or kidney failure without being appropriately addressed. Neurologic sequalae include cerebral stroke, basal ganglia injury, hepatic encephalopathy and cognitive decline resulting from CNS damage. Opioids, including morphine, hydroxycodeine, fentanyl when in excess similarly exert neurologic effects, resulting in drowsiness, euphoria as well as associated hippocampal injury, which can result in amnestic syndromes. A large component of these disease processes is the activation of inflammatory pathways, that damage surrounding cells and tissues through recruitment of cytokines and immune cells. Clinically derived modalities have been developed to measure levels of inflammation. Measurements of erythrocyte sedimentation rate (ESR) and C-Reactive Protein (CRP), an acute phase reactant (APR) are two very common clinically used modalities that can quantify the degree of inflammation. They are by no means the only measures of inflammation, as many other acute phase reactants (APR’s) increase with recruitment of cytokines and immune cells. One such APR is Pentraxin-3 (PTX-3) from the pentraxin family. The pentraxin family includes many acute phase reactants, of which CRP is also a member. They can be found in many cells, regulate inflammation, and are upregulated by cytokines such as Tumour Necrosis Factor-Alpha (TNF-α), a cytokine released by macrophages and monocytes. Through the employment of the in-silico model Ingenuity Pathway Analysis (IPA), we have modeled the biological pathways that ethanol and morphine share with PTX-3. The analysis serves as an assessment of inflammatory processes activated by these two compounds, in addition to the utility of Pentraxin-3 as a biomarker of ethanol/morphine-induced inflammation. The analysis looked at models in macrophages, in various CNS structures such as the hippocampus, amygdala, and overall findings suggest an activating effect of morphine on PTX-3, and an inhibitory effect of ethanol on PTX-3.

Authors List :

Zahid Asghar

Presenting Author :

Zahid Asghar

Affiliations :

Dow University of Health Sciences

Email :

zahidasghar@hotmail.com

Key Words (5 Words Maximum) :

Ethanol, Morphine, Pentraxin-3