Search Results (951)

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Background: Moral harassment (mobbing) in healthcare, particularly among nurses, remains a persistent issue with detrimental effects on mental health, resilience, and quality of life. Aim: We examine the relationship between the resilience of nurses working in Emergency Departments (EDs) and how these factors influence experiences of workplace mobbing. Methods: This cross-sectional study included 90 nurses from four public hospitals in Greece’s 5th Health District. Data were collected between October 2023 and March 2024 using the WHOQOL-BREF, Workplace Psychologically Violent Behaviors (WPVB) scale and the Connor–Davidson Resilience Scale (CD-RISC). The sample consisted primarily of full-time nurses (84.3% female; mean age = 43.1 years), with 21.1% reporting chronic conditions. Most participants were married (80.0%) and had children (74.4%), typically two (56.1%). Statistical analyses—conducted using SPSS version 27.0—included descriptive statistics, Pearson and Spearman correlations, multiple linear regression, and mediation analysis, with significance set at p < 0.05. Results: Resilience was moderate (mean = 66.38%; Cronbach’s α = 0.93) and positively correlated with all WHOQOL-BREF domains—physical, psychological, social, and environmental (r = 0.30–0.40)—but not with the overall WHOQOL-BREF. The mean overall WHOQOL-BREF score was 68.4%, with the lowest scores observed in the environmental domain (mean = 53.76%). Workplace mobbing levels were low to moderate (mean WPVB score = 17.87), with subscale reliabilities ranging from α = 0.78 to 0.95. Mobbing was negatively associated with social relationships and the environmental WHOQOL-BREF (ρ = –0.23 to –0.33). Regression analysis showed that cohabitation and higher resilience significantly predicted better WHOQOL-BREF outcomes, whereas mobbing was not a significant predictor. Mediation analysis (bootstrap N = 5000) indicated no significant indirect effect of resilience in the relationship between mobbing and WHOQOL-BREF. Conclusions: Resilience was identified as a key protective factor for nurses’ quality of life in emergency care settings. Although workplace mobbing was present at low-to-moderate levels, it was negatively associated with specific WHOQOL-BREF domains. Enhancing mental resilience among nurses may serve as a valuable strategy to mitigate the psychological effects of moral harassment in healthcare environments. Full article

Plant-derived vesicles offer several advantages, including high yield, low cost, ethical compatibility, safety, and potential health benefits. These advantages enable them to overcome technological limitations associated with vesicles of mammalian origin. Ginseng, a prominent example of a natural botanical plant, is known for its abundant bioactive components. Recent studies confirmed that ginseng-derived vesicles offer significant advantages in the treatment of human diseases. Therefore, this study reviews the extraction and purification processes of ginseng-derived vesicle-like nanoparticles (GDVLNs), their therapeutic potential, and the active ingredients in GDVLNs that may exert pharmacological activities. Furthermore, this study evaluates the research and applications of nanosized ginseng extracts, with a primary focus on ginsenosides. Full article

The evidence for photobiomodulation in reducing postoperative pain after endodontic instrumentation is classified as low or very low certainty, indicating a need for further research. Longitudinal pain assessments over 24 h are crucial, and studies should explore these pain periods. Background/Objectives: This double-blind, randomized controlled clinical trial evaluated the effect of PBM on pain following single-visit endodontic treatment of maxillary molars at 4, 8, 12, and 24 h. Primary outcomes included pain at 24 h; secondary outcomes included pain at 4, 8, and 12 h, pain during palpation/percussion, OHIP-14 analysis, and frequencies of pain. Methods: Approved by the Research Ethics Committee (5.598.290) and registered in Clinical Trials (NCT06253767), the study recruited adults (21–70 years) requiring endodontic treatment in maxillary molars. Fifty-eight molars were randomly assigned to two groups: the PBM Group (n = 29), receiving conventional endodontic treatment with PBM (100 mW, 333 mW/cm², 9 J distributed at 3 points near root apices), and the control group (n = 29), receiving conventional treatment with PBM simulation. Pain was assessed using the Visual Analog Scale. Results: Statistical analyses used chi-square and Mann–Whitney tests, with explained variance (η²). Ten participants were excluded, leaving 48 patients for analysis. No significant differences were observed in postoperative pain at 24, 4, 8, or 12 h, or in palpation/percussion or OHIP-14 scores. Pain frequencies ranged from 12.5% to 25%. Conclusions: PBM does not influence post-treatment pain in maxillary molars under these conditions. These results emphasize the importance of relying on well-designed clinical trials to guide treatment decisions, and future research should focus on personalized dosimetry adapted to the anatomical characteristics of the treated dental region to enhance the accuracy and efficacy of therapeutic protocols. Full article

Objectives: Digitalization has revolutionized dentistry, introducing advanced technological tools that improve diagnostic accuracy and access to healthcare. This study aims to examine the effects of integrating digital technologies into the dental field, analyzing the associated benefits and risks, with particular paid attention to the therapeutic relationship and decision-making autonomy. Materials and Methods: A literature search was conducted in PubMed, Scopus, Web of Science, and Cochrane Library, complemented by Google Scholar for non-indexed studies. The selection criteria included peer-reviewed studies published in English between 2014 and 2024, focusing on digital dentistry, artificial intelligence, and medical ethics. This is a narrative review. Elements of PRISMA guidelines were applied to enhance transparency in reporting. Results: The analysis highlighted that although digital technologies and AI offer significant benefits, such as more accurate diagnoses and personalized treatments, there are associated risks, including the loss of empathy in the dentist–patient relationship, the risk of overdiagnosis, and the possibility of bias in the data. Conclusions: The balance between technological innovation and the centrality of the dentist is crucial. A human and ethical approach to digital medicine is essential to ensure that technologies improve patient care without compromising the therapeutic relationship. To preserve the quality of dental care, it is necessary to integrate digital technologies in a way that supports, rather than replaces, the therapeutic relationship. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background: Anti-synthetase Syndrome (ASyS) is an idiopathic inflammatory myopathy characterized by muscle weakness and inflammatory infiltrates in muscles. Sjogren’s disease (SD) is an autoimmune condition primarily affecting exocrine glands. Both these conditions may present lung involvement. We describe a female patient with anti-synthetase/SD overlap syndrome and review the literature to identify published cases describing this overlap, aiming to better define its clinical, radiological, and serological features. Methods: The case description was based on a retrospective collection of clinical, laboratory, and imaging data related to the patient’s diagnostic process and clinical course. Data were anonymized and handled in accordance with the competent territorial Ethics Committee. A literature review was performed using the MEDLINE and Scopus databases by combining the keywords “Anti-Synthetase syndrome”, “Sjögren disease”, “Sjögren syndrome”, “Myositis”, and “Interstitial lung disease” (ILD). Published cases were selected if they met the 2016 EULAR/ACR criteria for SD and at least one of the currently proposed classification criteria for ASyS. Results: The described case concerns a 68-year-old woman with rapidly progressive ILD. The diagnosis of anti-synthetase/SD overlap syndrome was based on clinical, serological (anti-Ro52 and anti-PL7 antibodies), histological, and radiological findings. Despite immunosuppressive and antifibrotic treatment, the clinical course worsened, leading to a poor outcome. In addition, six relevant cases were identified in the literature. Clinical presentations, autoantibody profiles, radiological findings, and outcomes were highly heterogeneous. Among the reported cases, no standardized treatment protocols were adopted, reflecting the lack of consensus in managing this rare condition. Conclusions: In anti-synthetase/SD overlap syndrome, ILD may follow a rapidly progressive course. Early recognition can be challenging, especially in the absence of muscular involvement. This case-based review highlights the need for more standardized approaches to the diagnosis and management of this rare and complex overlap syndrome. Full article

Introduction: Given the impact of aneurysmal subarachnoid hemorrhage (aSAH) on patients’ health, preclinical research is substantial to understand its pathophysiology and improve treatment strategies, which necessitates reliable and comprehensive animal models. Traditionally, aSAH models utilize iliac or subclavian access for angiography, requiring invasive procedures that are associated with significant risks and animal burden. This pilot study explores a less invasive method of digital subtraction angiography (DSA) by using the auricular artery (AA) as an alternative access point. Our aim was to demonstrate the feasibility of this refined technique, with the intention of reducing procedural risks, providing shorter operation times with enhanced neurological recovery, and simplifying the process for both researchers and animals. Materials and Methods: In this study, six female New Zealand white rabbits (3.2–4.1 kg body weight) underwent experimental induction of aSAH via a subclavian-cisternal shunt. The initial steps of this procedure followed traditional techniques, consisting of subclavian access through microsurgical preparation, followed by DSA to analyze retrograde filling of the basilar artery (BA). To evaluate the alternative method, on day 3 after induction of aSAH, DSA was performed via the AA instead of the traditional subclavian or femoral access. A catheter was placed in the AA to allow retrograde filling of the BA. This approach aimed to simplify the procedure while maintaining comparable imaging quality. Results: All rabbits survived until the study endpoint. Postoperatively, two rabbits showed signs of hemisyndrome, which significantly improved by the time of follow-up. No additional morbidities were observed. Upon euthanasia and necropsy, all animals showed clear subarachnoid bleeding patterns. DSA via the AA produced strong contrasting of the BA comparable to the traditional method. Conclusions: This technical note presents an initial evaluation of AA access as a feasible and potentially advantageous method for DSA in a rabbit model of blood shunt subarachnoid hemorrhage. The method shows promise in reducing invasiveness and procedural complexity, but further studies are required to fully establish its efficacy and safety. Future research should focus on expanding the sample size, refining the anatomical understanding of the AA, and continuing to align with ethical considerations regarding animal welfare. Full article

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration. Full article

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate how artificial intelligence-driven models, such as predictive algorithms, electronic phenotyping, and automated flow cytometry analysis, might enable early diagnosis, minimize diagnostic delays, and enhance personalized treatment methods. Furthermore, artificial intelligence-driven immunopeptidomics and phenotypic categorization are enhancing vaccine development and biomarker identification. Successful implementation necessitates overcoming problems associated with data standardization, model validation, and ethical issues. Future advancements will necessitate a multidisciplinary partnership among physicians, data scientists, and governments to effectively use the revolutionary capabilities of artificial intelligence, therefore ushering in an age of precision medicine in immunodeficiencies. Full article

RNA viruses pose significant threats to global health, causing diseases such as COVID-19, HIV/AIDS, influenza, and dengue. These viruses are characterized by high mutation rates, rapid evolution, and the ability to evade traditional antiviral therapies, making effective treatment and prevention particularly challenging. In recent years, CRISPR/Cas13 has emerged as a promising antiviral tool due to its ability to specifically target and degrade viral RNA. Unlike conventional antiviral strategies, Cas13 functions at the RNA level, providing a broad-spectrum and programmable approach to combating RNA viruses. Its flexibility allows for rapid adaptation of guide RNAs to counteract emerging viral variants, making it particularly suitable for highly diverse viruses such as SARS-CoV-2 and HIV. This review discusses up-to-date applications of Cas13 in targeting a wide range of RNA viruses, including SARS-CoV-2, HIV, dengue, influenza, and other RNA viruses, focusing on its therapeutic potential. Preclinical studies have demonstrated Cas13’s efficacy in degrading viral RNA and inhibiting replication, with applications spanning prophylactic interventions to post-infection treatments. However, challenges such as collateral cleavage, inefficient delivery, potential immunogenicity, and the development of an appropriate ethical framework must be addressed before clinical translation. Future research should focus on optimizing crRNA design, improving delivery systems, and conducting rigorous preclinical evaluations to enhance specificity, safety, and therapeutic efficacy. With continued advancements, Cas13 holds great promise as a revolutionary antiviral strategy, offering novel solutions to combat some of the world’s most persistent viral threats. Full article

Early diagnosis is critical for the effective management of neurodegenerative disorders, and retinal alterations have emerged as promising early biomarkers due to the retina’s close developmental and functional link to the brain. The zebrafish (Danio rerio), with its rapid development, transparent embryos, and evolutionarily conserved visual system, represents a powerful and versatile model for studying retinal degeneration. This review discusses a range of behavioral assays—including visual adaptation, motion detection, and color discrimination—that are employed to evaluate retinal function in zebrafish. These methods enable the detection of subtle visual deficits that may precede overt anatomical damage, providing a non-invasive, efficient strategy for early diagnosis and high-throughput drug screening. Importantly, these behavioral tests also serve as sensitive functional readouts to evaluate the efficacy of pharmacological treatments over time. Compared to traditional murine models, zebrafish offer advantages such as lower maintenance costs, faster development, optical transparency for live imaging, and ethical benefits due to reduced use of higher vertebrates. However, variability in experimental protocols highlights the need for standardization to ensure reliability and reproducibility. Full article

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article