In the published version of this article [1], spelling errors were identified in Figure 4. Specifically, the words "containing," “Oxadiazole,” “filtered,” and "scaffold" were misspelled. These errors have now been corrected. The original article can be found online at: https://www.eurekaselect.com/article/153236.
Introduction: Tissue biopsy and histopathological evaluation-considered the gold standard-are traditionally used for cancer diagnosis; however, these procedures are invasive and limited in their ability to accurately capture tumor heterogeneity. Liquid biopsy has emerged as a minimally invasive alternative that enables molecular characterization through circulating tumor- derived biomarkers. Methods: This review critically evaluates the biological basis, technical platforms, and clinical applications of liquid biopsies, including circulating tumor DNA (ctDNA), Circulating Tumor Cells (CTCs), exosomes, and other circulating biomarkers. A comparative analysis with conventional tissue biopsies was conducted based on invasiveness, diagnostic accuracy, assessment of tumor heterogeneity, capacity for timely monitoring, cost-effectiveness, and clinical utility. Results: Liquid biopsy offers significant advantages regarding minimal invasiveness, repeatability, and the dynamic monitoring of tumor progression and treatment response. It holds potential for detecting minimal residual disease and emerging resistance mutations. However, challenges remain concerning analytical sensitivity, test standardization, cost, and regulatory implementation in early-stage cancer. Tissue biopsy continues to provide essential morphological, histological, and staging information. Discussion: Current evidence indicates that liquid biopsy is a complement to, rather than a substitute for, traditional tissue-based diagnosis. Integrating molecular profiling with conventional histopathology can advance the precision oncology approach. Conclusion: 'Liquid biopsy' represents a revolutionary advancement in cancer testing. While it holds great promise for real-time disease monitoring and personalized therapy, its optimal clinical role lies in complementing existing diagnostic techniques rather than completely replacing them.
Introduction: Dementia is a progressive, neurodegenerative disease that involves a gradual loss of functions of the brain, such as memory, reasoning, and attention. This review will assess the association between Artificial Intelligence (AI) technologies and the pharmacological treatment of dementia for diagnosis and treatment. Methods: A narrative review was conducted using literature retrieved from Google Scholar, ScienceDirect, and PubMed databases. Relevant peer-reviewed studies published between 2000 and 2025 related to dementia, AI, machine learning, neuroimaging, and pharmacotherapy were analysed. A total of 141 eligible articles were included and thematically reviewed. Results: Artificial Intelligence methods such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Support Vector Machines (SVMs), and Natural Language Processing (NLP) technologies have significantly improved the detection and classification of dementia using neuroimaging, speech analysis, and cognitive assessment data in very early or early stages of diagnosis. Newer advanced neuroimaging techniques, such as MRI and PET, provide clinicians with more accurate test results and allow for more precise measurement. The continued use of pharmacological approaches, such as donepezil, rivastigmine, galantamine, memantine, piracetam, and nootropic agents, remains the mainstay of symptomatic treatment and support for additional cognition. The combination of AI-assisted drug discovery and predictive modelling showed promise for developing personalised treatment methods while speeding up the development of new therapies. Discussion: The combination of artificial intelligence with traditional dementia treatment methods enables medical professionals to diagnose patients earlier, make better treatment decisions, and deliver customised care to individual patients. Medical professionals face major challenges that prevent them from using the technology due to issues with data protection, system understanding, and medical validation. Conclusion: The use of AI in diagnostic and treatment methods is a new development with potential for improving dementia care. The process of developing dementia care solutions needs more interdisciplinary research to improve AI models, test their clinical use, and develop successful personalised treatment methods.
Introduction: Obesity has emerged as a major global public health concern. It is a multifactorial condition encompassing genetic, environmental, socioeconomic, and psychosocial factors. This narrative review aims to highlight the key pathophysiological processes associated with chronic low-grade inflammation (CLGI) in obesity. Methods: Data collection was performed using the PubMed, Scopus, Web of Science, and Google Scholar databases, using the search terms “chronic low-grade inflammation”, “pathophysiology of obesity,” and “metabolic syndrome” to obtain relevant articles. Results: The pathophysiological sequence underlying CLGI-associated obesity begins with adipose tissue expansion, during which hypertrophic adipocytes trigger cellular stress manifested as hypoxia and ER stress, accompanied by the release of damage-associated molecular patterns (DAMPs). This initiates a vicious cycle involving the recruitment of macrophages and their proinflammatory M1 polarization, with activation of inflammatory pathways, such as NF-κB and the NLRP3 inflammasome. These changes further contribute to the induction of systemic insulin resistance and metabolic dysregulation. Discussion: Both tissue and systemic biomarkers are valuable in identifying and monitoring CLGI-associated obesity. Treatment approaches ranging from pharmacological agents and bariatric surgery to dietary modifications, exercise, and microbiome-based therapies are relevant for managing inflammation and metabolic health. Conclusion: In conclusion, strategic immunotargeting of CLGI through context-specific interventions can serve as an innovative therapeutic strategy to treat CLGI-associated obesity and potentially mitigate the resulting comorbid metabolic burden. Future efforts should prioritize the development of multi-marker approaches within precision medicine frameworks to enable more individualized care for patients with CLGI-associated obesity.
Introduction: The direct-to-consumer (DTC) genetic testing industry has grown substantially, enabling consumers to obtain information regarding ancestry, disease predisposition, pharmacogenomics, and various traits without the necessity of healthcare provider intervention. Marketed as tools aimed for democratizing personal health insights, direct-to-consumer genetic tests may improve genetic knowledge, promote preventative healthcare, and encourage proactive health management. Methods: The study utilized medical databases such as PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar. Results: Challenges like inadequate variant coverage, demographic bias in reference databases, inconsistent interpretation standards, and restricted access to genetic counseling raise concerns around false positives, increased anxiety, and suboptimal health decisions. Ethical and legal dilemmas about privacy, consent, and third-party data sharing intensify concerns about confidentiality, participant rights, and regulatory compliance. Discussion: Numerous tests lack recognized clinical importance and utility, while state-level regulatory control limits wider implementation. This mini-review assesses the advantages and disadvantages of direct-to-consumer genetic testing, focusing on scientific, clinical, and societal ramifications. It emphasizes the equilibrium between consumer empowerment and the dangers of disinformation, highlighting the necessity for stringent validation, transparent communication, and professional genetic counseling. Conclusion: As direct-to-consumer genetic testing becomes more accessible, it is crucial to acknowledge its advantages and drawbacks to ensure responsible utilization and to enhance its contribution to precision medicine while protecting individual and public health.
