Search Results (15,711)

In intensive care units, large-scale clinical time-series data are continuously accumulated through electronic medical records and bedside monitoring systems. However, direct utilization of such data for clinical decision-making remains challenging due to irregular sampling, pervasive missingness, unstructured diagnostic information, and incomplete ICD labeling. Automated ICD coding constitutes an extreme multi-class classification problem with thousands of long-tailed categories, while intervention prediction tasks, such as mechanical ventilation management, involve rare transition events and severe class imbalance. To address these challenges, we propose CAGE, a hierarchical Clinical Decision Support System framework that integrates diagnosis, time-series signals, and intervention prediction. The framework first infers admission-level diagnostic context using a partial-label Automated ICD Coding module that combines DCNv2 with an Adaptive CLPL loss, producing probability-weighted diagnostic embeddings. These embeddings are subsequently fused with ICU time-series tensors and processed by a multi-branch Temporal Convolutional Network equipped with an ICD-conditioned gating mechanism to predict future ventilation state transitions. The experimental results demonstrate that DCNv2 achieves consistent superiority across all hit@k and probability concentration metrics for ICD coding. For intervention prediction, the proposed method substantially outperforms existing baselines, achieving a Macro-AUC of 98.2, Macro-AUPRC of 77.4, and F1-score of 79.4. These findings indicate that reinjecting diagnostic context as a conditioning variable, together with imbalance-aware loss design, effectively enhances rare-event detection and improves the practical applicability of clinical decision support systems. Full article

Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy. Full article

Background: The present study aimed to assess the caregiving burden among family caregivers of adult patients with various cancer types and stages in the Kingdom of Saudi Arabia (KSA), and to examine the associations with caregiver and patient characteristics. Materials and Methods: A cross-sectional study involving 212 family caregivers of cancer patients was conducted between March and April 2024 at King Abdullah Medical City in Makkah, KSA. The Arabic version of the Zarit Burden Interview (ZBI) scale was used to assess overall and specific burdens. Associations between overall burden and sociodemographic variables were analyzed using significance tests. Results: Over half (55%) of participants experienced burden, with a mean ZBI score of 26.33 ± 16.86, indicating a mild to moderate level. Low levels of psychological (7.34 ± 5.41), social (2.27 ± 2.93), physical (1.96 ± 2.22), and financial (1.22 ± 1.41) burdens were found. Financial difficulties and patient immobility significantly contributed to higher burden scores. Caregivers with financial hardships scored higher (31 ± 14.8 vs. 24 ± 17.3, p = 0.01), and those caring for bedridden patients experienced greater burdens (38 ± 21.8 vs. 18 ± 12.5, p = 0.001). Conclusions: Although financial difficulties and patient immobility significantly contribute to caregiver burden, the overall burden in the Makkah region remains relatively moderate. Strong cultural and familial support systems in KSA may alleviate challenges, yet coping strategies targeting financial and physical burdens are necessary. Full article

Background/Objectives: Overcrowding in emergency departments (EDs) remains a critical challenge in modern healthcare systems, driven in part by patient uncertainty regarding symptom urgency and a lack of accessible medical guidance. Recent advances in artificial intelligence, particularly large language models (LLMs), present a novel opportunity to support patient navigation and relieve pressure on ED infrastructures. Methods: A total of 238 unique patient questions were identified through a structured web search. Following deduplication and thematic clustering, 15 representative questions were selected. Each question was submitted to the three LLMs—ChatGPT (v3.5), DeepSeek, and Gemini—using a standardized prompt. Responses were assessed by clinical experts (N = 8) who were blinded to the model source. Reviewers selected the best overall response per question, as well as the individual responses of the three LLMs for each respective question. Results: ChatGPT was selected as the best-performing model in 60% of cases, with DeepSeek and Gemini selected in 23% and 17%, respectively. ChatGPT responses also achieved the highest proportion of “excellent” quality ratings and the lowest proportion of “unsatisfactory” outputs. Across all models, clarity was the most positively rated domain (79% agreement), followed by empathy (72%), length/detail appropriateness (71%), and completeness (65%). Over two-thirds of raters expressed willingness to integrate LLM-based tools into clinical practice for patient education and pre-triage counseling. Conclusions: Large language models demonstrate promising capabilities in responding to emergency care-related patient queries. Their ability to deliver medically sound and communicatively effective answers positions them as potential digital adjuncts in the management of low-acuity ED presentations and prehospital triage. Full article

Cancer remains a primary global health challenge, accounting for millions of new cases and significant mortality annually. Although doxorubicin (DOX) is a fundamental anthracycline used for various malignancies, its therapeutic index is severely limited by poor selectivity, systemic toxicity, and dose-dependent cardiotoxicity. To address these issues, Janus nanoparticles (JNPs) have emerged as a promising bifunctional platform characterized by a structural asymmetry that allows for the independent functionalization of each hemisphere. This review examines primary fabrication routes—such as masking, microfluidics, self-assembly, and phase separation—and their specific applications in DOX delivery. The anisotropic architecture of JNPs enables a “separate rooms” concept, allowing for the co-delivery of incompatible drugs while facilitating multi-stimuli-responsive release mechanisms triggered by pH, enzymes, or NIR light. Furthermore, JNPs have demonstrated enhanced tumor accumulation and reduced systemic toxicity compared to conventional isotropic carriers. Recent developments even highlight the use of autonomous nanomotors to improve therapeutic delivery while minimizing premature leakage. However, clinical translation is currently hindered by manufacturing complexity, high equipment costs, scalability issues, and a lack of standardized reporting in the literature. Ultimately, JNPs represent a sophisticated frontier in precision oncology, though robust manufacturing processes and characterization protocols are required for future medical adoption. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article

Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov–Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems. Full article

Background: Safe medication administration is a fundamental aspect of nursing practice and a core component of patient safety. However, systemic failures, workload pressures, and educational gaps continue to contribute to medication errors, posing persistent challenges for healthcare systems. In this context, innovative educational technologies, particularly Artificial Intelligence (AI), have emerged as promising strategies to support the development of competencies related to safe medication administration. Methods: This scoping review aimed to map evidence on AI-based tools used to teach safe medication administration in nursing. The review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology and reported following the PRISMA-ScR guidelines. Searches were performed in PubMed, Scopus, Web of Science, LILACS, and Google Scholar, covering studies published between 2010 and October 2025 in English, Portuguese, and Spanish. Study selection was conducted in two stages, followed by standardized data extraction. Results: A total of 545 records were identified, of which only two studies met the eligibility criteria. The included studies, conducted in Israel and South Korea, evaluated a microlearning chatbot and Large Language Model (LLM)-based tools designed to support teaching safe medication administration. Both studies demonstrated improvements in knowledge and performance in tasks and simulations related to the medication process, as well as positive acceptability among participants. However, neither study assessed direct clinical outcomes, such as reductions in medication errors or preventable adverse events. Conclusions: Although AI-based educational tools show potential to enhance competencies related to medication safety in nursing, the available evidence remains limited. Further robust, multicenter, and comparative studies are needed to evaluate their impact on clinical outcomes and to support their integration into nursing education and practice. Full article

Background/Objectives: We developed a telehealth-enabled fiber-bundle endomicroscopy platform and evaluated its preclinical feasibility for targeted fluorescence imaging in cervical cancer models. Methods: The platform integrates a portable fiber-bundle endomicroscopy (FBE) system, fluorescein isothiocyanate (FITC)-labeled candidate peptides, and a secure web-based telehealth platform for remote consultation. The FBE probe achieved a field of view of 1,700 µm and a lateral resolution of 4 µm, enabling cellular-level fluorescence imaging in a compact, portable format. Four FITC-labeled peptides (SHS1*, SHS2*, FPP*, and CRL*) were evaluated in A549, SiHa, and CaSki cell lines. Ex vivo testing was performed on commercial cervical tissue-array samples. The telehealth platform was assessed for secure medical-image/video transmission and end-to-end latency in a simulated remote-consultation setting. Results: Among the tested probes, FPP*-FITC and CRL*-FITC showed higher fluorescence-positive fractions in the p16-overexpressing cervical cancer cell lines than in the A549 comparator line, with the strongest signals observed in CaSki cells. In ex vivo testing, CRL*-FITC generated higher fluorescence intensity in malignant cervical tissue-array samples than in non-malignant comparator tissues, with a reported 4.6- to 7.4-fold difference in mean signal intensity (p < 0.001). The telehealth platform supported the secure transmission of medical images and video and demonstrated an end-to-end latency of <500 ms in a simulated remote consultation setting. Conclusions: These results support the technical and preclinical feasibility of integrating targeted fluorescence imaging, portable fiber-bundle endomicroscopy, and telehealth into a single platform. This study should therefore be interpreted as a preclinical feasibility study evaluating optical, molecular, and telehealth integration, rather than as a clinically validated cervical cancer screening test. Full article

Titanium and its alloys are used in dental and orthopedic implants. However, long-term stability remains a clinical challenge. To overcome this limitation, surface modification has been investigated to improve surface properties. Our previous study demonstrated that the immobilization of secretory leukocyte protease inhibitor (SLPI) on the titanium surface promotes osteoblast adhesion, proliferation, and differentiation in vitro. The current study demonstrated the first in vivo evaluation of SLPI as a bioactive coating for medical implants. Grade 5 titanium screws were coated with 10 µg/mL of recombinant human SLPI (rhSLPI) for 24 h via simple physical adsorption, and the results were preliminarily validated via FE-SEM and ELISA. These SLPI-coated titanium screws (TiSs) were then placed in the tibia of Sprague–Dawley rats for 4 and 8 weeks. The hematological and biochemical parameters (BUN, Creatinine, AST, and Troponin I) demonstrated no acute systemic alterations within the 8-week period across all groups. Moreover, micro-computed tomography (micro-CT) and histological analysis revealed significantly higher bone volume fraction (%BV/TV) at 4 weeks compared to uncoated controls (20.64% 2.452% vs. 11.73% 0.524%). Finally, the biomechanical stability of implants, assessed using the removal torque test, showed that TiSs showed higher strength compared to Ti at both 4 and 8 weeks. In conclusion, this study represents a novel approach to transitioning rhSLPI-coated titanium evaluation from in vitro models to an in vivo rat model. rhSLPI surface functionalization enhances early-stage osseointegration and improves implant mechanical stability without acute hematological and biochemical alterations. These proof-of-concept findings suggest the potential of SLPI as a bioactive coating strategy. Full article

Background/Objectives: Large language models (LLMs) such as ChatGPT are rapidly being integrated into healthcare for tasks ranging from clinical documentation to diagnostic support. Current ethical discussions focus predominantly on bias, privacy, and accuracy, leaving three critical governance questions unresolved: What kind of knowledge does an LLM output represent in clinical reasoning? When is a clinician’s or patient’s trust in that output justified? Who bears responsibility when an AI-informed decision leads to patient harm? This study proposes the Epistemic Authority–Trust–Responsibility (ETR) Architecture, a normative conceptual framework that addresses these three questions as an integrated governance challenge. Methods: The framework was developed through normative conceptual analysis—a method that constructs governance proposals by synthesising philosophical principles, ethical theories, and empirical evidence. The literature was identified through structured searches of PubMed, PhilPapers, and EUR-Lex (January 2020–March 2026), drawing on the philosophy of medical knowledge, the ethics of trust and testimony, and the moral philosophy of responsibility. Results: The ETR Architecture produces four outputs: (i) a four-tier classification system that distinguishes LLM outputs—from administrative drafts to clinical evidence claims—and matches each tier to appropriate verification requirements; (ii) the concept of the ‘epistemic placebo’, formally defined as a governance measure that creates a documented appearance of compliance while lacking at least one operative element of genuine oversight; (iii) a model specifying four conditions under which trust in healthcare AI is justified; (iv) four testable hypotheses with associated research designs connecting governance design to trust calibration and patient safety. Conclusions: The 2025–2027 regulatory transition period offers a critical window for shaping how healthcare institutions govern AI. We argue that deploying LLMs without explicitly classifying their outputs and building appropriate oversight risks allows governance norms to be set by technology vendors rather than by evidence-informed, patient-centred policy. Full article

Background: Doctors have made increasing use of artificial intelligence-based clinical decision support systems in recent years in eastern China, but far less so in poorer western China, where hospitals with less access to specialized expert services might be expected to make greater use of such aids. Methods: This study of the reasons for lower uptake in the western hospitals focused on a tertiary referral hospital in the capital city of the poorest province in China. Drawing on UTAUT (unified theory of acceptance and use of technology) theoretical literature and previous studies, seven variables most likely to explain the limited adoption of the technology were identified and tested by means of an explanatory sequential mixed-methods study. Results: Initial bivariate tests revealed no significant differences across variables; however, multivariate logistic regression identified social influence as the sole statistically significant predictor of adoption willingness. Follow-up structured interviews revealed a surprisingly low awareness of the technology by medical personnel, with very limited deployment. Conclusions: The failure to adopt AI diagnosis technology is attributable not to the variables usually cited as factors inhibiting technology adoption but rather the failure of hospital and medical faculty administrators to acquire the technology and train doctors and medical students. Full article

Background/Objectives: Sepsis is a leading cause of hospital mortality and represents a time-sensitive medical emergency. Current diagnostic strategies rely on clinical assessment, severity scores, biomarkers, and blood cultures. However, blood cultures require 24–72 h for pathogen identification and demonstrate limited sensitivity, while biomarkers such as procalcitonin and C-reactive protein lack optimal specificity. These limitations support the widespread empirical use of broad-spectrum antibiotics and highlight the need for rapid, sensitive, and culture-independent diagnostic tools. Methods: A narrative literature review was conducted using PubMed and Google Scholar, including 28 studies published over the past 10 years, encompassing observational and preclinical investigations. Current evidence on the application of Raman spectroscopy in sepsis was summarized, with a dual focus on pathogen identification and the assessment of the host response. Results: Raman spectroscopy has demonstrated the ability to detect early molecular alterations in circulating immune cells and mitochondrial redox status, potentially preceding conventional biomarkers. For pathogen identification, Raman techniques have achieved diagnostic accuracies comparable to automated systems, but with significantly shorter turnaround times. Integration with microfluidics, optical tweezers, and deep learning algorithms has further enhanced performance, although these applications remain largely experimental. Conclusions: Despite these promising results, the lack of methodological standardization, spectral overlap among phylogenetically related species, limited large-scale validation, and challenges in interpreting certain spectral signatures remain unresolved. Most available evidence originates from preclinical, single-center, and controlled studies, underscoring the need for prospective multicenter trials and harmonized protocols. Full article

Plants of the genus Salacia (Celastraceae) have long been used in traditional medical systems of South and Southeast Asia for the management of diabetes and related metabolic disorders. Modern phytochemical and pharmacological studies have confirmed the antidiabetic potential of several Salacia species, leading to the identification of a distinctive group of sulfur-containing sugars as their principal bioactive constituents. Salacinol, neosalacinol, kotalanol, neokotalanol, and related analogues represent a novel class of thiosugar sulfonium compounds that act as potent and selective α-glucosidase inhibitors, providing a clear mechanistic basis for their glucose-lowering effects. Simpler thiosugars, such as 5-thiomannose, further contribute to the overall metabolic activity of Salacia extracts and may serve as biosynthetic or functional precursors. Beyond Salacia, sulfur-containing natural products are widespread in nature and perform diverse biological roles. In particular, the genus Allium is well known for producing organosulfur compounds, including thioethers and polysulfides, which exhibit antidiabetic, hypolipidemic, antioxidant, and cardioprotective activities. In a different context, sulfur-containing hopanes have been identified in sediments and petroleum as products of early diagenetic sulfurization of bacterial hopanoids. Although these compounds have been studied primarily as geochemical biomarkers, recent QSAR/PASS analyses suggest that sulfur hopanes may also possess biologically relevant activities, particularly related to metabolic and cardiovascular regulation. Recent PASS-based QSAR evaluations of Salacia-derived thiosugars and sulfur hopanes predict significant antidiabetic activity, including potential type 2 diabetes-related pharmacological effects, supported by predicted α-glucosidase inhibitory, hypoglycemic, hepatic, and gastrointestinal activities. Collectively, these findings highlight sulfur-containing natural products from both plant and sedimentary sources as chemically diverse yet functionally convergent scaffolds with promising potential for the development of functional foods and therapeutic agents targeting metabolic disorders. Full article

Background: Saudi Arabia has made significant progress toward malaria elimination; however, imported cases continue to occur, particularly in the southwestern regions. This study aimed to describe the clinical characteristics and outcomes of patients with malaria in the Aseer Region, Saudi Arabia. Methods: A retrospective observational study was conducted at Khamis Mushait General Hospital, Aseer Region, Saudi Arabia, including all patients with malaria from January 2022 to December 2025. Demographic, clinical, laboratory, and outcome data were extracted from the electronic medical records. Severe malaria was defined according to the World Health Organization criteria. Multivariate logistic regression using Firth’s penalized maximum likelihood estimation was performed to identify independent predictors of severe malaria (≥1 WHO criterion). Statistical analysis was performed using R software (version 4.2.1). Results: A total of 311 patients were included, predominantly male (90.0%), with a mean age of 28.8 ± 11.3 years. Ethiopian nationals comprised nearly half the cases (48.2%), followed by Saudi (16.4%) and Yemeni (15.1%) nationals. Plasmodium vivax was the most common species (51.1%), followed by Plasmodium. falciparum (40.2%). Fever was the most frequent symptom (89.4%), followed by fatigue (50.8%), chills (46.9%), and vomiting (39.5%). Low parasitemia (<1%) was the most frequent finding (33.8%), followed by moderate (27.3%) and mild (18.3%) levels, while high (4.2%) and very high parasitemia (1.9%) were uncommon. Severe malaria (≥1 criterion) was diagnosed at 43.7%, with severe anemia (26.0%) and jaundice (23.2%) being the most frequent WHO severity criteria. Notably, 84% of the cases occurred during 2024–2025, indicating a recent outbreak, with a sharp peak of 43 cases in October 2024. Multivariate logistic regression identified two independent predictors of having at least one WHO severity criterion: higher parasitemia level (adjusted OR = 1.70 per 1% increase, 95% CI: 1.40–2.11, p < 0.001) and non-Saudi nationality (adjusted OR = 2.40, 95% CI: 1.10–5.62, p = 0.027). Conclusions: Malaria in the Aseer Region predominantly affects young adult male expatriates, suggesting its imported nature. The predominance of P. vivax represents a shift from historical patterns. Parasitemia level and being of non-Saudi nationality independently predict severe malaria and may therefore support risk stratification and clinical decision-making. The dramatic case surge in 2024–2025 highlights regional vulnerability to outbreaks despite control progress. These findings support enhanced screening for at-risk populations, maintenance of clinical capacity for severe malaria management, and robust surveillance systems for early outbreak detection. Full article