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298 articles for “Clinical Data”
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Prime Editing and Base Editing in Human Hematopoietic Stem Cells Toward Scarless Correction of Monogenic Blood Disorders
Abstract: Monogenic hematological disorders – such as sickle cell disease (SCD), β-thalassemia, and X-linked chronic granulomatous disease (X-CGD) – affect >400, 000 newborns worldwide each year, with a considerable burden in low-resource settings . Although donor-derived allogeneic hematopoietic stem-cell transplantation (HSCT) is widely regarded as curative, its application is limited due to issues of donor availability and graft-versus-host disease (GVHD) and conditioning-related toxicity. DSB-based conventional CRISPR-Cas9 strategies are limited by inefficient …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 4, Issue 1, 2026 Read article
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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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Phytotherapeutic Potential of Melaleuca: An Integrative Review of Phytochemistry and Antimicrobial, Antifungal, Antioxidant, and Antiviral Mechanisms
Abstract: Melaleuca alternifolia and other species of the Melaleuca genus stand out for the broad spectrum of pharmacological properties attributed to their bioactive compounds, such as monoterpenes, sesquiterpenes, flavonoids, and polyphenols. This study aimed to critically review and analyze scientific evidence produced between 2005 and 2025 regarding the antimicrobial, antifungal, antioxidant, and antiviral activities of Melaleuca, correlating its phytochemistry with molecular mechanisms of action. For this purpose, an integrative literature review …
Published in Research & Reviews : Journal of Herbal Science · Vol. 15, Issue 2, 2026 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article
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SGLT2 Inhibitors in Heart Failure and Chronic Kidney Disease: Expanding Therapeutic Horizons beyond Glycemic Control
Abstract: Heart failure (HF) and chronic kidney disease (CKD) are closely related clinical diseases that significantly increase morbidity and death worldwide. The co-occurrence of both conditions is frequently referred to as the "cardio-renal syndrome," in which the failure of one organ hastens the decline of the other. The original purpose of sodium–glucose cotransporter-2 (SGLT2) inhibitors was to treat type 2 diabetic mellitus (T2DM) by reducing blood sugar levels. Nevertheless, new data …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Patient Profile and Antimicrobial Susceptibility Testingin Clinical Isolates of Staphylococcus aureus
Abstract: INTRODUCTION: Staphylococcus aureus is a gram-positive bacterium capable of causing various infections, ranging from minor skin infections to serious bloodstream infections. The rise of methicillin-resistant S. aureus (MRSA) strains has made treating these infections more challenging, as MRSA is frequently resistant to several types of antibiotics.. OBJECTIVE: The aim of this study was to investigate the patient profile and antimicrobial susceptibility patterns of clinical isolates of S. aureus in a …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 14, Issue 2, 2024 · pp. 31–38 Read article
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Demodex spp. (Acari: Demodicidae) Infestation in Humans: Diagnostic Clues and Therapeutic Approaches to Primary and Secondary Demodicosis.
Abstract: Demodicosis represents an inflammatory dermatosis and adnexal disorder arising from pathologic overgrowth of Demodex mites, primarily Demodex folliculorum and Demodex brevis, which are ubiquitous human ectoparasites residing in pilosebaceous units and eyelid margins. Once regarded as benign commensals, these mites are now recognized as primary drivers or key cofactors in diverse clinical phenotypes, including papulopustular eruptions, pityriasis folliculorum, rosacea-like disorders, blepharitis, meibomian gland dysfunction, and exacerbations of comorbid dermatoses such …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 12–20 Read article
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Advances in Simulation and Surgical Skill Training Evolution: Narrative Integrative Review
Abstract: Simulation-based education has emerged as a cornerstone of contemporary general surgery training, driven by increasing emphasis on patient safety, competency-based education, and rapid technological innovation. Traditional apprenticeship models, while foundational, are constrained by reduced operative exposure, work-hour limitations, and variability in clinical case mix. In this context, simulation provides a structured, reproducible, and safe environment for acquisition, assessment, and refinement of surgical skills across the training continuum. This narrative review …
Published in Research and Reviews : Journal of Surgery · Vol. 15, Issue 1, 2026 · pp. 7–13 Read article
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Comparative Analysis of Data Augmentation Techniques in CNN-based Classification of Atelectasis
Abstract: This research delves into the critical issue of atelectasis, its causes, and potential complications if left untreated. Leveraging deep learning algorithms, particularly convolutional neural networks (CNN), the paper explores their application in medical image analysis, focusing on the detection of atelectasis using the “chestX-ray8” database. The study compares various data augmentation techniques for improved accuracy, showcasing the importance of augmentation in enhancing model generalization. Through meticulous experimentation and evaluation, the …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article
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Enhancing Nursing Education Through AI-Driven Adaptive Learning Systems
Abstract: The integration of Artificial Intelligence (AI) in nursing education offers significant potential to enhance learning experiences by personalizing education, improving knowledge retention, and developing clinical competencies. This study evaluates the effectiveness of AI-driven adaptive learning systems compared to traditional lecture-based teaching methods in nursing education. A mixed-methods approach was used, with 200 nursing students participating in a quasi-experimental design. The intervention group (100 students) used AI-powered adaptive learning platforms for …
Published in Journal of Nursing Science & Practice · Vol. 15, Issue 2, 2025 · pp. 29–34 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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Enhancing the User Experience of Asthma Inhalers: A Redesign Approach
Abstract: Asthma is a widespread chronic respiratory condition impacting millions globally, presenting significant challenges in its management and treatment. While conventional inhalers effectively administer medication, they often encounter usability issues, hindering patient adherence and treatment outcomes. This abstract delineates the development and potential impact of a redesigned asthma inhaler aimed at addressing these challenges. Incorporating human-centered design principles, the revamped inhaler prioritizes usability, portability, and effectiveness to enhance the overall management …
Published in International Journal of Solid State Innovations & Research · Vol. 1, Issue 2, 2023 · pp. 32–36 Read article
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Trauma- Its Transgenerational Inheritance and Associated Nuances in Perinatal Care: A Systematic Review
Abstract: The research is a systematic review of the existing clinical and animal studies pertaining to effects of trauma on immediate offsprings and its transgenerational inheritance. The research also summarises the current statistical data on perinatal care globally with special emphasis on India as a developing nation and the current strategies, government policies and international recommendations on maternal and child health as put forward in the Sustainable development goal 2030 by …
Published in International Journal of Children · Vol. 1, Issue 1, 2024 · pp. 37–45 Read article
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Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 Read article
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A Comparative Study of Deep Learning Methods for Depression Detection in Social Media Data
Abstract: With the rise of social media platforms like Twitter, Reddit, and Facebook, individuals increasingly share personal information about their moods, behaviors, and mental states. This trend provides a unique opportunity to leverage large-scale textual data for understanding and monitoring mental health conditions, particularly depression, a prevalent and challenging mental health issue. Traditional depression assessments are often confined to clinical environments and lack the capacity for real-time monitoring. In contrast, social …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 55–65 Read article
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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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Patient Profile and Antimicrobial Susceptibility Testing of Clinically Isolates In E. coli
Abstract: Escherichia coli, commonly known as E. coli, is a prevalent bacterium capable of causing infections in humans. The development of antimicrobial resistance in E. coli poses a significant public health issue. This study aimed to determine the patient profile and antimicrobial susceptibility patterns of clinically isolated E. coli. A retrospective study was carried out on E. coli samples collected from clinical specimens within a hospital environment. Patient profiles, including age, …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 14, Issue 1, 2024 · pp. 32–47 Read article
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AI in Healthcare: Drug Delivery in Tuberculosis
Abstract: Tuberculosis (TB) , mainly caused by Mycobacterium tuberculosis, remains a major global health burden, accounting for millions of new infections and deaths each year. Although progress has been made in diagnosis and treatment, the growing threat of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB makes disease control increasingly difficult. Conventional diagnostic approaches such as chest X-rays, sputum smear microscopy, and culture methods continue to play an important role, but they …
Published in Trends in Drug Delivery · Vol. 13, Issue 1, 2026 · pp. 9–17 Read article
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How Scientists Look for New Pathogens Before They Spread
Abstract: Emerging infectious illnesses are a persistent danger to global health. They often come from unexpected places, such wildlife reservoirs, changes in the climate, or human activities. Finding new diseases before they create epidemics is one of the biggest problems in modern epidemiology. This article talks about how scientists use genetic monitoring, field sampling, and real-time data processing to find, track, and describe new infectious organisms in a way that works …
Published in International Journal of Pathogens · Vol. 3, Issue 1, 2026 · pp. 11–17 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article