Search Results (1,440)

Point-of-care ultrasound (POCUS) is an essential component of emergency medicine, enabling rapid bedside assessment across a wide spectrum of acute conditions. Its effectiveness, however, remains constrained by operator dependency, variable image quality, and time-critical decision-making. Recent advances in artificial intelligence (AI) offer opportunities to augment POCUS by supporting image acquisition, interpretation, and quantitative analysis. This narrative review synthesizes current evidence on AI-enhanced POCUS applications in emergency care, encompassing trauma, non-traumatic emergencies, integrated workflows, resource-limited settings, and education and training. Across trauma settings, AI-assisted POCUS has demonstrated promising performance for automated detection of pneumothorax, hemothorax, and free intraperitoneal fluid, supporting standardized eFAST examinations and rapid triage. In non-traumatic emergencies, AI-enabled cardiovascular, pulmonary, and abdominal applications provide automated measurements and pattern recognition that can approach expert-level performance when image quality is adequate. Integrated AI–POCUS systems and educational tools further highlight the potential to expand ultrasound access, support non-expert users, and standardize training. Nevertheless, important limitations persist, including limited generalizability, dataset bias, device heterogeneity, and uncertain impact on clinical decision-making and patient outcomes. In conclusion, AI-enhanced POCUS is transitioning from proof-of-concept toward early clinical integration in emergency medicine. While current evidence supports its role as a decision-support tool that may enhance consistency and efficiency, widespread adoption will require prospective multicentre validation, development of representative POCUS-specific datasets, vendor-agnostic solutions, and alignment with clinical, ethical, and regulatory frameworks. Full article

Alopecia profoundly impacts psychological well-being and quality of life, yet current therapeutic options such as minoxidil and finasteride exhibit limited efficacy. Fibroblast growth factor 7 (FGF-7), also known as keratinocyte growth factor (KGF), is a paracrine growth factor secreted by dermal papilla cells that specifically activates the epithelial receptor FGFR2b. Receptor engagement triggers multiple downstream signaling cascades, including the MAPK/ERK, PI3K/Akt, and Wnt/β-catenin pathways, promoting keratinocyte proliferation, stem cell activation, and the transition of hair follicles into the anagen phase. Both in vitro and in vivo animal studies consistently demonstrate that FGF-7 accelerates telogen-to-anagen transition and enhances follicular regeneration. FGF-7 acts synergistically with insulin-like growth factor 1 (IGF-1) and vascular endothelial growth factor (VEGF) to sustain nutrient delivery and cell proliferation. Human scalp studies further reveal a strong association between the FGF-7/FGFR2b signaling and follicular activity; however, clinical trials remain scarce. Topical application of FGF-7 has demonstrated an excellent safety profile, whereas systemic administration necessitates careful monitoring. Future directions include the development of engineering to extend the systemic half-life, advanced delivery systems, and gene or mRNA-based therapeutic approaches. Thus, the FGF-7/FGFR2b axis is a highly compelling molecular target for next-generation hair regeneration therapies. Full article

Management of resectable esophageal cancer has evolved into a multidisciplinary paradigm centered on multimodality therapy. Historically, induction chemoradiotherapy followed by surgery, as established by the CROSS trial, became the standard of care for locally advanced disease due to improvements in R0 resection rates and overall survival. More recently, the ESOPEC trial reexamined this paradigm in esophageal adenocarcinoma, demonstrating superior survival and improved systemic disease control with perioperative chemotherapy using the FLOT regimen compared with chemoradiotherapy. In parallel, the MATTERHORN trial further advanced perioperative treatment by showing improved event-free survival with the addition of the immune checkpoint inhibitor durvalumab to FLOT chemotherapy. Alongside these systemic therapy advances, surgical management has transitioned toward minimally invasive and robotic-assisted esophagectomy, offering equivalent oncologic outcomes with reduced perioperative morbidity. This review summarizes the evolving evidence from pivotal clinical trials, highlights ongoing studies integrating immunotherapy, and discusses emerging strategies such as adoptive cell transfer which currently is under investigation for metastatic recurrence, but in the future may provide additional treatment options for resectable esophageal cancer. Full article

Background: The rapid ageing of the cardiovascular population has profoundly transformed clinical practice, with an increasing proportion of patients presenting advanced age, frailty, multimorbidity, and functional vulnerability. Conventional cardiology models, largely derived from younger and selected populations, often fail to adequately address the complexity of cardiovascular care in older adults. Despite the growing development of cardiogeriatrics, the core competencies required for contemporary geriatric cardiology practice remain insufficiently defined. Methods: This narrative review synthesises evidence from cardiology, geriatrics, heart failure, and the palliative care literature, complemented by clinical expertise in integrated cardiogeriatric care pathways, to identify key competencies relevant to the care of older adults with cardiovascular disease. Results: Four major domains of geriatric cardiology competencies were identified: (1) advanced cardiovascular expertise adapted to ageing physiology, frailty, and multimorbidity; (2) integration of comprehensive geriatric assessment into cardiovascular decision-making; (3) a dedicated cardiogeriatric communication mindset supporting shared decision-making under prognostic uncertainty; and (4) system-based competencies focused on multidisciplinary coordination, care transitions, and therapeutic proportionality. Conclusions: Defining the core competencies of the geriatric cardiologist is essential to addressing the clinical and organisational challenges of an ageing cardiovascular population. This framework provides a pragmatic foundation for clinical practice, education, and future research, supporting integrated cardiogeriatric care models aligned with patient-centred outcomes. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

Background: Language barriers remain a major obstacle to equitable healthcare access for immigrants and refugees across Europe. Greece, as both a transit and host country, faces persistent challenges in providing linguistically and culturally appropriate care. Methods: This study presents a narrative literature review synthesizing international, European, and Greek evidence on the effects of limited language proficiency, professional interpretation, and intercultural mediation on healthcare access, patient safety, satisfaction, and clinical outcomes. Peer-reviewed studies and selected grey literature were identified through searches of PubMed, Scopus, Web of Science, and CINAHL. Results: The evidence consistently demonstrates that the absence of professional interpretation is associated with substantially higher rates of clinically significant communication errors, longer hospital stays, increased readmissions, and higher healthcare costs. In contrast, the use of trained medical interpreters and intercultural mediators improves comprehension, shared decision-making, patient satisfaction, and clinical outcomes. Comparative European data from Italy, Spain, Germany, and Sweden show that institutionalized interpretation systems outperform Greece’s fragmented, NGO-dependent approach. Greek studies further reveal that limited proficiency in Greek is associated with reduced service utilization, longer waiting times, and lower patient satisfaction. Conclusions: This narrative review highlights the urgent need for Greece to adopt a coordinated, professionally staffed interpretation and intercultural mediation framework. Strengthening linguistic support within the healthcare system is essential for improving patient safety, equity, efficiency, and the integration of migrant and refugee populations. Full article

Background/Objectives: Cancer pain affects 55–95% of patients with advanced malignancy, representing a complex syndrome involving nociceptive, neuropathic and nociplastic mechanisms. Despite therapeutic advances, two-thirds of patients with metastatic cancer experience inadequate pain control. This scoping review synthesizes recent advances in cancer pain pathophysiology and management, focusing on molecular and cellular mechanisms, emerging pharmacological, interventional and technological therapies and key evidence gaps to inform future precision-based pain management strategies. Methods: Following PRISMA-ScR methodology, we searched PubMed, Embase, Scopus, and Web of Science for studies published between January 2022 and September 2025. After screening 3412 records, 278 studies were included and analyzed across different domains: biological mechanisms, pharmacological management, interventional and neuromodulatory approaches, radiotherapy developments, and digital health innovations. Results: Recent mechanistic research reveals cancer pain arises from tumor–neuron–immune crosstalk, with malignant cells secreting neurotrophic factors that promote axonal sprouting and nociceptor sensitization. Genetic polymorphisms and epigenetic modifications contribute to inter-individual pain variability. Management strategies are evolving toward multimodal precision medicine: NSAIDs and opioids remain foundational, complemented by adjuvant agents and interventional procedures including nerve blocks, intrathecal delivery, and neuromodulation (spinal cord and dorsal root ganglion stimulation). Stereotactic body radiotherapy demonstrates superior analgesic durability versus conventional approaches. Digital health innovations, such as mobile applications, remote monitoring, wearables, and AI-enabled predictive models, enable continuous assessment and personalized treatment optimization. Conclusions: Cancer pain management is transitioning toward mechanism-based precision medicine integrating biological insights, advanced interventional techniques, and digital technologies. However, implementation challenges persist, including limited randomized trials for interventional approaches, the incomplete external validation of AI tools, and digital health equity concerns. Future research must prioritize prospective controlled studies and equitable integration into routine care. Full article

B cells are key drivers of immune dysregulation across systemic autoimmune diseases. Among their progeny, plasmablasts occupy a uniquely revealing niche: short-lived, highly proliferative intermediates that mirror real-time B-cell activation. Their appearance in peripheral blood integrates antigenic stimulation, cytokine-driven differentiation, and aberrant germinal-center dynamics, transforming them into sensitive indicators of ongoing immunological activity. This review synthesizes current knowledge on plasmablast biology and highlights disease-specific phenotypes across systemic lupus erythematosus (SLE), primary Sjögren disease (pSjD), IgG4-related disease (IgG4-RD), ANCA-associated vasculitis (AAV), and rheumatoid arthritis (RA). We incorporate molecular insights from single-cell technologies that have uncovered previously unrecognized plasmablast subsets, metabolic states, and interferon-related signatures with prognostic and mechanistic value. Beyond descriptive immunology, plasmablasts are emerging as dynamic biomarkers capable of informing real-time clinical decisions. One of the most robustly supported applications is the prognostic interpretation of plasmablast kinetics following B-cell-depleting therapies, where early reconstitution patterns consistently predict relapse across multiple autoimmune conditions. As clinical immunology shifts from static serological markers toward kinetic, cell-based monitoring, plasmablast quantification offers a path toward precision immune surveillance. Integrating plasmablast dynamics into routine care may ultimately allow clinicians to anticipate disease flares, time therapeutic reinforcements, and transition from reactive management to preventive intervention. Full article

Severe emotional stress constitutes a significant public-health concern associated with negative health outcomes. Although the clinical effects are well acknowledged, the specific biological mechanisms that translate emotional suffering into systemic disease remain incompletely understood. Psychological stress activates the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, which directly target mitochondria and alter their bioenergetic and redox capacity. For this reason, this narrative review proposes that mitochondria serve as the primary subcellular link in the mind–body connection, as they play a pivotal role in converting neuroendocrine signals into cellular dysfunction. In particular, we focus on the concept of mitochondrial allostatic load (MALT), a framework explaining how the progressive decline in mitochondrial functions, from their initial adaptive roles in energy production, reactive oxygen species signaling, and calcium regulation, to being sources of inflammation and systemic damage, occurs when stress exceeds regulatory limits. We also, discuss how this transition turns mitochondria from adaptive responders into drivers of multi-organ disease. In subsequent sections, we examine diagnostic potentials related to MALT, including the use of biomarkers, such as growth differentiation factor 15, cell-free mitochondrial desoxyribonucleic acid, and functional respirometry. Furthermore, we evaluate mitochondria-targeted therapeutic strategies, encompassing pharmacological compounds, such as mitoquinone mesylate, Skulachev ions, and elamipretide, alongside lifestyle and psychological interventions. Here, we aim to translate MALT biology into clinical applications, positioning mitochondrial health as a target for preventing and treating stress-related disorders. We propose that MALT may serve as a quantifiable bridge between emotional stress and somatic disease, enabling future precision medicine strategies integrating mitochondrial care. Full article

Background and Objective: The accurate detection of sleep stages and disorders in older adults is essential for the effective diagnosis and treatment of sleep disorders affecting millions worldwide. Although Polysomnography (PSG) remains the primary method for monitoring sleep in medical settings, it is costly and time-consuming. Recent automated models have not fully explored and effectively fused the sleep features that are essential to identify sleep stages and disorders. This study proposes a novel automated model for detecting sleep stages and disorders in older adults by analyzing PSG recordings. PSG data include multiple channels, and the use of our proposed advanced methods reveals the potential correlations and complementary features across EEG, EOG, and EMG signals. Methods: In this study, we employed three novel advanced architectures, (1) CNNs, (2) CNNs with Bi-LSTM, and (3) CNNs with a transformer encoder, for the automatic classification of sleep stages and disorders using multichannel PSG data. The CNN extracts local features from RGB spectrogram images of EEG, EOG, and EMG signals individually, followed by an appropriate column-wise feature fusion block. The Bi-LSTM and transformer encoder are then used to learn and capture intra-epoch feature transition rules and dependencies. A residual connection is also applied to preserve the characteristics of the original joint feature maps and prevent gradient vanishing. Results: The experimental results in the CAP sleep database demonstrated that our proposed CNN with transformer encoder method outperformed standalone CNN, CNN with Bi-LSTM, and other advanced state-of-the-art methods in sleep stages and disorders classification. It achieves an accuracy of 95.2%, Cohen’s kappa of 93.6%, MF1 of 91.3%, and MGm of 95% for sleep staging, and an accuracy of 99.3%, Cohen’s kappa of 99.1%, MF1 of 99.2%, and MGm of 99.6% for disorder detection. Our model also achieves superior performance to other state-of-the-art approaches in the classification of N1, a stage known for its classification difficulty. Conclusions: To the best of our knowledge, we are the first group going beyond the standard to investigate and innovate a model architecture which is accurate and robust for classifying sleep stages and disorders in the elderly for both patient and non-patient subjects. Given its high performance, our method has the potential to be integrated and deployed into clinical routine care settings. Full article

This study examines the influence of sintering temperature on the structural and transport properties of GdBa₂Cu₃O₇ (Gd123) superconductors prepared from nano-sized precursors via the co-precipitation method. The metal-oxalate precursor (average particle size < 50 nm) was calcined at 900 °C for 12 h, and then the prepared pellets were sintered under an oxygen atmosphere in the range of 920–950 °C for 15 h. All samples showed metallic properties and a sharp superconducting transition. Critical temperatures T_C(R=0) were 94–95 K, with higher sintering temperatures steadily boosting critical current density. X-ray diffraction confirmed orthorhombic Gd123 as the dominant phase, with its phase fraction increasing from 92% to 99.8% as the sintering temperature increased. SEM micrographs showed large, densely packed grains, with higher sintering temperatures promoting improved grain connectivity and reduced porosity. The sample sintered at 950 °C exhibited the most favorable transport performance, attributed to enhanced intergranular coupling and the presence of nanoscale secondary phases acting as effective flux-pinning centers. Overall, these results demonstrate that careful control of sintering temperature can significantly optimize the microstructure and superconducting properties of Gd123 materials, supporting their advancement for practical electrical and magnetic applications. Full article

Background: Artificial intelligence (AI) is rapidly transforming clinical medicine by enabling earlier disease detection, personalized risk stratification, precision diagnostics, and optimized therapeutic decision-making across multiple specialties. Methods: This narrative review synthesizes the most recent evidence from prospective randomized controlled trials, large cohort studies, and real-world implementations of AI in cardiology, pulmonology, neurology, hepatology, pancreatic diseases, and other key areas of internal medicine. Studies were selected based on clinical impact, external validation, and regulatory approval status where applicable. Results: AI systems now outperform traditional clinical tools in numerous high-stakes applications: >88% freedom from atrial fibrillation at 1 year with AI-guided ablation, noninferior stent optimization versus OCT guidance, >95% sensitivity for atrial fibrillation and low ejection fraction detection on single-lead ECG, substantial increases in adenoma detection rate and melanoma triage accuracy, automated pancreatic cancer detection on routine CT with 89–90% sensitivity, and significant improvements in palliative care consultation rates and post-PCI outcomes using AI-supported telemedicine. Over 850 FDA-cleared AI devices exist as of November 2025, with cardiology and radiology dominating clinical adoption. Conclusions: AI has transitioned from experimental to clinically indispensable in multiple specialties, delivering measurable reductions in mortality, morbidity, hospitalizations, and healthcare resource utilization. Remaining challenges include external validation gaps, bias mitigation, and the need for large-scale prospective trials before universal implementation. Full article

Vulnerable populations experience higher mortality and readmission after hospital discharge. We sought to evaluate the impact of the Transitions Of Care Bundle (TOCB™) on COVID-19 patient outcomes post-discharge compared to a control cohort. This retrospective study used electronic health record data collected for 243 COVID-19 patients (65 TOCB™, 178 control) during the initial pandemic months at a large academic facility in Northeast New Jersey (NJ). Data included demographics, comorbidities, readmissions, mortality, and payor. The TOCB™ cohort had proportionally more Hispanic patients (56.92% vs. 48.3%, p = 0.0885). All TOCB™ patients were discharged home without needing additional services, compared to only 36% of the control group. The implementation of TOCB™ was associated with shorter hospital stays, a potential decrease in readmission rates, and fewer emergency department visits. These results imply that well-coordinated post-discharge services are linked to a diminished risk of mortality, possible hospital readmission, and other adverse health outcomes. Full article

Diseases associated with yeast pathogens have become an increasingly serious global health issue. The range of virulence factors and the development of mechanisms of resistance have posed a significant challenge in the fight against these types of infections. Lectins, proteins capable of reversibly binding to carbohydrates and glycoconjugates, have been assessed as antifungal agents. This review shows that lectins have demonstrated versatility and significant potential as therapeutic agents against Candida, Nakaseomyces and Cryptococcus. These molecules act through diverse mechanisms, including disruption of fungal cell membranes, induction of oxidative stress, inhibition of ergosterol biosynthesis, and interference with mitochondrial and lysosomal functions. Some lectins have been shown to inhibit yeast-to-hyphae morphological transitions and biofilm formation, which are critical virulence factors for pathogenic yeasts. Moreover, some lectins have shown potential to enhance the efficacy of conventional antifungal drugs through synergistic interactions, though these effects can depend on the fungal isolate. Beyond in vitro activity, translational considerations remain underdeveloped in the context of antifungal applications of lectins. Some lectins exhibit minimal toxicity, while others require careful dosing due to potential toxicity or undesired immunogenicity. Delivery and stability also present challenges, though strategies such as chemical modifications and topical, mucosal, or nanoparticle-based formulations show promise. Overall, the multifaceted antifungal activities of lectins highlight their promising role as innovative candidates in the development of novel therapies to address the growing challenge of yeast pathogen resistance. However, significant knowledge gaps persist, highlighting the urgent need for coordinated research that bridges in vitro findings with practical pharmacological applications. Full article

Cross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions. Full article