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177 articles for “Early Intervention”
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IoT-Enabled Remote Patient Monitoring System Using Wearable Sensors
Abstract: In recent years, the Internet of Things (IoT) has revolutionized healthcare by enabling seamless connectivity between patients, medical devices, and healthcare professionals. The increasing demand for continuous health monitoring and early disease detection has driven the development of IoT-based remote patient monitoring systems. This paper presents an IoT-enabled framework that integrates wearable physiological sensors, wireless communication modules, and cloud- based analytics to facilitate real-time health tracking. The proposed system continuously …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 1, 2026 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Artificial intelligence’s role in mental health: Innovations, Challenges, and future prospects
Abstract: Among the many ways in which mental health services are gaining from the integration of artificial intelligence (AI) are improvements in diagnosis, tailored treatment programs, and round the- clock patient help. Two AI-driven solutions are virtual therapists and prediction algorithms, which could increase access to mental health therapy and enable early intervention. However, the application of artificial intelligence in this field raises ethical concerns about privacy, discrimination, and the potential …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Multifactorial Diseases Becoming the Epigenetic Factor for the Genz
Abstract: This comprehensive paper investigates the complex interplay between multifactorial diseases, particularly obesity and cardiovascular disease (CVD), and their relationship with epigenetic mechanisms, highlighting the transgenerational implications of these interactions. The multifaceted nature of obesity and CVD, involving genetic predisposition, environmental influences, and epigenetic modifications, underscores the intricate pathways contributing to disease susceptibility and progression. Drawing upon a synthesis of diverse research findings, the paper elucidates the critical role of genetic …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 2, Issue 1, 2024 · pp. 16–22 Read article
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A Review on Applications of Artificial Intelligence (AI) in Parkinsons’s Disease Diagnosis and Treatment and Its Future Challenges
Abstract: Parkinson’s disease (PD) is a long-term, progressive neurodegenerative disorder that mainly occurs in people older than 60 years, affecting nearly 1% of this population. It is chiefly marked by the loss of dopaminergic neurons in the substantia nigra, a crucial brain region responsible for controlling motor functions. The resultant dopamine deficiency significantly disrupts motor control, manifesting in clinical symptoms such as tremors, bradykinesia, muscle rigidity, and postural instability. While PD …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 1–15 Read article
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Crispr Cas – Revolutionizing Modern Therapies and Beyond
Abstract: CRISPR-Cas technology has emerged as a transformative tool in modern molecular biology, revolutionizing both fundamental research and clinical applications. This RNA-guided gene-editing system enables precise and efficient genomic modifications, offering unprecedented potential for addressing genetic disorders, infectious diseases, and oncological conditions through innovative therapeutic interventions. The inherent specificity and programmability of CRISPR-Cas systems have facilitated breakthroughs in diverse fields, including precision medicine, regenerative therapies, and immuno-oncology. Beyond its therapeutic applications, …
Published in International Journal of Genetic Modifications and Recombinations · Vol. 3, Issue 1, 2025 · pp. 25–38 Read article
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Effectiveness of Evidence-Based Pediatric Nursing Practices on Clinical and Developmental Outcomes in Children: A Comprehensive Systematic Review
Abstract: Evidence-based pediatric nursing practices are essential for improving health outcomes among children by integrating clinical expertise with the best available research evidence and patient-centered care approaches. This systematic review examines the effectiveness of evidence-based pediatric nursing interventions on clinical and developmental outcomes in children across various healthcare settings. A comprehensive search of databases including PubMed, CINAHL, Scopus, and the Cochrane Library was conducted for studies published between 2016 and 2026. …
Published in International Journal of Evidence Based Nursing And Practices · Vol. 4, Issue 1, 2026 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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Editorial: Advancements in Movement Analysis for Understanding Neurological Disorders
Abstract: Neurological disorders pose a significant challenge, demanding innovative approaches for accurate diagnosis, effective treatment, and deeper understanding. Movement analysis emerges as a powerful tool, offering a quantitative window into the complexities of motor function. This editorial delves into the transformative impact of movement analysis on neurological research and clinical practice. Traditional diagnostic methods, reliant on subjective observations, often miss subtle motor impairments, particularly in early disease stages. Movement analysis tackles …
Published in International Journal of Brain Sciences · Vol. 1, Issue 2, 2024 · pp. 28–32 Read article
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X-Ray Insight: Deep Learning-Enhanced Detection and Grading of Knee Osteoarthritis
Abstract: Osteoarthritis (OA) is the most prevalent form of arthritis affecting the knee. It is a degenerative joint disease characterized by the gradual deterioration of cartilage, typically impacting individuals aged 50 and above, although it can also occur in younger people. The condition progresses slowly, with symptoms intensifying over time, leading to significant pain and discomfort. Early diagnosis and intervention can significantly alleviate pain and enhance the quality of life for …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 2, Issue 2, 2024 · pp. 1–6 Read article
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Wiskott-Aldrich Syndrome: A Comprehensive Guide
Abstract: Wiskott-Aldrich Syndrome (WAS) is an uncommon genetic disorder inherited through the X chromosome, marked by a combination of immune system deficiencies, eczema, and low platelet counts. This syndrome primarily affects males, leading to significant morbidity and mortality due to recurrent infections, bleeding complications, and autoimmune diseases. Mutations in the WAS gene disrupt the production of the Wiskott-Aldrich Syndrome protein (WASp), crucial for the functioning of immune cells and platelet formation. …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 2, 2024 · pp. 39–46 Read article
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Diabetes Prediction Using ML Techniques
Abstract: Diabetes mellitus, commonly referred to as diabetes, denotes a cluster of prevalent endocrine disorders characterized by persistent elevated levels of blood sugar. Diabetes is classified into two main types: type 1 and type 2. Type 1 diabetes arises when the body is unable to produce insulin, while type 2 diabetes involves either insulin resistance or insufficient insulin production. Early detection and intervention are essential to reduce its harmful impacts. The …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Awareness and Knowledge of Postpartum Psychosis Among Antenatal Women: A Call for Educational Intervention
Abstract: Postpartum psychosis (PP) is a rare yet critical mental health disorder that poses serious risks to the well-being of new mothers. This condition manifests through a range of acute symptoms, including manic episodes, depressive states, confusion, hallucinations, and delusions. Symptoms typically present abruptly within the first two weeks following childbirth, making it a medical emergency that necessitates urgent care. Left untreated, PP can lead to severe consequences, including self-harm or …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 3, 2024 · pp. 125–134 Read article
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Role of PI3K/AKT/mTOR Signaling Pathways in Breast Cancer
Abstract: Abstract: Breast cancer is among the most commonly diagnosed cancers and remains a leading cause of mortality among women globally. Although early detection and interventions aimed at curtailing tumor progression have significantly improved breast cancer survival rates, there is an ongoing demand for more potent systemic therapies to effectively prevent metastasis. A central pathway frequently associated with the growth, survival, and motility of breast cancer cells is the PI3K/AKT/mTOR signaling …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 1, 2025 · pp. 23–34 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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Innovations in Tuberculosis Management: Advancements in Drug-Resistant TB Treatment and Rapid Diagnostics
Abstract: Tuberculosis (TB) remains one of the deadliest infectious diseases worldwide, exacerbated by the increasing prevalence of drug-resistant strains, such as multidrug-resistant (MDR-TB) and extensively drug-resistant TB (XDR-TB). Despite advancements in first-line and second-line treatments, resistance to conventional antibiotics has complicated therapeutic strategies, necessitating novel approaches. This mini review explores emerging advancements in TB treatment, including gene editing technologies, such as CRISPR, which offer potential for precise targeting of drug-resistant bacterial …
Published in International Journal of Antibiotics · Vol. 2, Issue 2, 2025 · pp. 1–7 Read article
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Gene Expression Profiling in Autism Spectrum Disorder: A Microarray Analysis Using Gse42133
Abstract: Autism Spectrum Disorder (ASD) is a diverse neurodevelopmental disorder characterized by difficulties in social interaction, communication impairments, and restricted or repetitive patterns of behavior. Despite its increasing prevalence, the underlying molecular mechanisms remain poorly understood. Advances in transcriptomics offer opportunities to investigate the gene expression changes that may contribute to ASD pathophysiology. In this study, the microarray dataset GSE42133 was analyzed, which comprises gene expression profiles from peripheral blood samples …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 37–48 Read article
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A Review on: Cotard’s Syndrome
Abstract: Cotard’s Syndrome is a rare and severe mental health condition characterized by nihilistic delusions, in which individuals firmly believe that they are dead, no longer exist, or that parts of their body are decaying or missing. These beliefs are not symbolic or metaphorical but are experienced as absolute truths, making the disorder particularly distressing and difficult to manage. The syndrome is most commonly observed in association with major depressive disorder, …
Published in Recent Trends in Infectious Diseases · Vol. 3, Issue 1, 2026 · pp. 5–9 Read article
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High-Definition Electroencephalography: A New Horizon in Neurological Pathology Research
Abstract: The advent of high-density electroencephalography (HD-EEG) has catalyzed a paradigm shift in the exploration of neurological pathologies. This editorial underscore its transformative potential in elucidating brain dynamics and refining diagnostic approaches for a spectrum of conditions, spanning from epilepsy and dementia to cognitive impairments in preterm infants. Our objective is to optimize the utility of HD-EEG by emphasizing the imperative for methodological homogenization and fostering collaborative endeavors. The remarkable spatial …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 15–21 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article