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298 articles for “Clinical Data”
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AI Application in the Creation of Medications for COPD
Abstract: The crippling lung condition known as chronic obstructive pulmonary disease (COPD) is typified by a continuous restriction of airflow, which results in increased respiratory dysfunction and a reduced quality of life. The rising incidence of COPD worldwide emphasizes the pressing need for innovative pharmaceutical approaches to address the illness. Even though COPD care has advanced significantly, most current medications concentrate on symptom relief rather than disease change. This gap in …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 01–05 Read article
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A Systematic Review of the Herbal Medicinal Plants With Anti-Obesity Potential
Abstract: Obesity is a chronic, multifactorial metabolic condition characterized by abnormal fat accumulation. It is mainly driven by the intricate influence of genetic, environmental, hormonal, metabolic and lifestyle determinants. The burden of obesity has risen considerably over the past few years, making it one of the major health concerns in both developed and developing countries alike. The rapid increase globally in obesity prevalence is linked with numerous health complications such as …
Published in Research & Reviews : Journal of Herbal Science · Vol. 15, Issue 2, 2026 Read article
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A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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MIRDcalc & OLINDA/EXM Dosimetry Software Analysis by SPECT/CT Scan Data of Lu-177 DOTATATE Radionuclide Therapy of NET Patients
Abstract: Accurate dosimetry is essential in nuclear medicine for optimizing radionuclide therapies and ensuring patient safety. In Radiopharmaceutical dosimetry the Medical Internal Radiation Dosimetry (MIRD)Society is the pioneer in organ-level dosimetry providing the fundamental basis for commonly used clinical and research dosimetry software like MIRDOSE and OLINDA/EXM. Recently, in MIRD Pamphlet No. 28, Part 1, the MIRD committee of the Society of Nuclear Medicine and Medical Imaging presented a new Software …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 48–63 Read article
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Role of Artificial Intelligence in Simulation and Therapeutics in Neurodegenerative Diseases
Abstract: Neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, etc., are a cause of significant mortality rates due to a lack of curative treatments and their complex nature. Traditional therapeutic methodologies have several disadvantages such as slow diagnosis and a lack of effective treatments. They mainly focused on the management of the disease rather than curing it. The integration of artificial intelligence in the simulation and therapeutics of neurodegenerative …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 19–29 Read article
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Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson's Disease
Abstract: This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 44–54 Read article
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SiRNA-Based Multivalent Vaccines: A Systematic Review of Potential Risks, Benefits, and Future Applications
Abstract: Background: Small interfering RNA (SiRNA) technology represents a promising innovation in vaccine development, especially for multivalent vaccines that can target multiple pathogens or antigenic variants simultaneously. siRNA-based vaccines offer a unique mechanism to induce specific immune responses, presenting an adaptable and potentially powerful tool for tackling infectious diseases and cancer. Methods: This systematic review synthesizes recent studies on siRNA multivalent vaccines, focusing on their immunogenicity, safety, and effectiveness. We reviewed …
Published in International Journal of Vaccines · Vol. 2, Issue 1, 2025 · pp. 43–49 Read article
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Recent Update on Advanced Drug Delivery System
Abstract: Over the past decade, there has been a growing interest in the use of artificial intelligence (AI) technology for analysing and interpreting biological or genetic data, accelerating drug discovery, and identifying selective small-molecule modulators or rare molecules in addition to predicting their behaviour. The use of artificial neural networks (ANNs) for the rapid analysis of massive amounts of data, the development of novel hypotheses and treatment plans, the prediction of …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 1, 2023 · pp. 22–30 Read article
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Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
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Nutraceuticals Unveiled: Exploring the Science, Benefits, and Future of Functional Food
Abstract: The rapidly developing subject of nutraceuticals is critically examined in this review, with a focus on its scientific basis, proven health advantages, and possible future applications in the functional food industry. Key bioactive ingredients that contribute to the therapeutics effectiveness of functional foods, including flavonoids, polyphenols, carotenoids, probiotics, and essential fatty acids, have been identified and characterized as a result of recent developments in nutritional science. Both experimental and clinical …
Published in International Journal of Nutritions · Vol. 2, Issue 2, 2025 Read article
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Iron Deficiency Anaemia_A Evidence-Based Literature Review
Abstract: Iron deficiency anaemia represents a medical condition characterised by a reduction in the concentration of haemoglobin in the bloodstream below a defined level, according to the World Health Organization. The condition presents itself in two forms: functional IDA and absolute IDA. The former is characterized by normal or elevated total body iron stores, but insufficient iron supply to the bone marrow. At the same time, the latter occurs when total …
Published in Research & Reviews : Journal of Herbal Science · Vol. 13, Issue 1, 2024 · pp. 17–28 Read article
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Parental Burden and Its Contribution to Mental Health Relapse
Abstract: In today’s era, psychologists need to comprehend the complex dynamics of burden on parents and how it affects mental health relapse. Rationale: The chapter explores the relationship between high levels of expressive emotion (e.g., anger, criticism, and emotional over-involvement) and parents’ stress. There is a critical need for focused therapies by examining a variety of models and research, that focus on neurodevelopmental disorders, schizophrenia, and bipolar illness. Creating efficient support …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 14, Issue 3, 2024 · pp. 1–7 Read article
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A Knowledge Graph Approach for Breast Cancer Diagnosis and Data Sharing Platform Implementation in the Context of Human Papillomavirus Infection
Abstract: Background: Breast cancer remains among the most prevalent malignancies in women worldwide, and effective diagnosis and data integration continue to challenge clinical practice. Diagnostic reports from mammography and ultrasound contain rich clinical information that is often under-utilised due to heterogeneous formats and limited data-sharing infrastructure. In the context of human papillomavirus (HPV) infection, which may influence oncogenic pathways and data complexity, advanced computational methods offer new solutions to this problem. …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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The Role of Bioinformatics in Nursing: Transforming Healthcare through Data-Driven Insights
Abstract: Bioinformatics, an interdisciplinary field combining biology, computer science, and information technology, is increasingly shaping the nursing profession. It offers powerful tools for improving patient care, advancing clinical research, and enabling personalized healthcare through data-driven decision-making. This article examines the integration of bioinformatics into nursing practice, tracing its historical roots from the Human Genome Project to its current applications in genomic medicine, precision healthcare, and population health. Nurses now play a …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 18–21 Read article
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Glitches in the Implementation of Bioinformatics in Medical Settings: A Comprehensive Review
Abstract: Bioinformatics is a dynamic field at the intersection of biology, computer science, and information technology, offering new possibilities in medicine by enabling a deeper understanding of genomics, molecular biology, and personalized treatment approaches. Its integration into healthcare could greatly enhance diagnostics, enable tailored treatments for individuals, and facilitate the analysis of large biological datasets. However, despite its potential, several barriers impede its successful implementation in clinical settings. These include technical …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–6 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article
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Advances in Total Knee Replacement: A Systematic Review of Clinical Outcomes and Complications
Abstract: Total Knee Replacement (TKR) is a widely used surgical intervention for patients with knee osteoarthritis and other degenerative knee disorders, offering significant improvements in pain relief, functional restoration, and quality of life. This systematic review synthesizes data from multiple clinical studies to evaluate the outcomes and complications associated with TKR. To find applicable research published in the last ten years, a thorough search of electronic databases was performed. Randomised controlled …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 1, 2024 · pp. 41–56 Read article
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Revolutionizing Artificial Organs: Next-Generation Membrane Technologies for Precision Medicine and Global Health
Abstract: Membrane technology has emerged as a cornerstone in the advancement of artificial organ systems, offering critical functionalities in selective molecular filtration, tissue scaffolding, and controlled therapeutic delivery. Recent innovations have propelled the field beyond traditional polymeric membranes to include nanostructured, biomimetic, and stimuli-responsive materials, significantly enhancing biocompatibility, selectivity, and durability. The integration of smart technologies, such as AI-driven membrane design, bioelectronic sensors, and personalized fabrication via 3D printing, is ushering …
Published in International Journal of Membranes · Vol. 2, Issue 2, 2025 · pp. 29–39 Read article
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Kisunla (Donanemab): A Novel Breakthrough Illuminating a Path to Better Alzheimer’s Outcomes
Abstract: Background: Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive decline, memory loss, and loss of independence. Despite decades of extensive research, disease-modifying treatments have been limited in success. Recent therapeutic advancements have led to the development of monoclonal antibodies targeting amyloid-beta (Aβ) plaques, particularly the pyroglutamate-modified variant, a key pathological hallmark of AD. Objectives: This review aims to explore the therapeutic potential of Donanemab (Kisunla), a novel …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 37–45 Read article
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Quantitative Image-Based Assessment of Degradation Patterns in Polymer-Based Medical Implants
Abstract: Polymer-based medical devices are widely used in clinical practice, where long-term material degradation can compromise performance and patient safety. Traditional polymer degradation studies predominantly rely on laboratory-based experiments, which often fail to capture real-world operational and usage conditions. In this study, a multimodal, data-driven framework is proposed for the quantitative assessment of degradation patterns in polymer-based medical devices using publicly available clinical failure data. Structured operational parameters, including cumulative usage …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1510–1518 Read article