Search Results (583)

The increasing use of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), and personal care products (PCPs) has led to the widespread presence of organic micropollutants (OMPs) in aquatic environments, posing a significant global challenge for environmental conservation. In recent years, advanced materials-based nanofiltration (NF) technologies have emerged as a promising solution for water and wastewater treatment. This review begins by examining the sources of OMPs, as well as the risk of OMPs. Subsequently, the key criteria of NF membranes for OMPs are discussed, with a focus on the roles of pore size, charge property, molecular interaction, and hydrophilicity in the separation performance. Against that background, this review summarizes and analyzes recent advancements in materials such as metal organic frameworks (MOFs), covalent organic frameworks (COFs), graphene oxide (GO), MXenes, hybrid materials, and environmentally friendly materials. It highlights the porous nature and structural diversity of organic framework materials, the advantage of inorganic layered materials in forming controllable nanochannels through stacking, the synergistic effects of hybrid materials, and the importance of green materials. Finally, the challenges related to the performance optimization, scalable fabrication, environmental sustainability, and complex separation of advanced materials-based membranes for OMP removal are discussed, along with future research directions and potential breakthroughs. Full article

During the coronavirus 2019 pandemic, sports activities were restricted, raising concerns about their impact on the physical condition of adolescent athletes, which remained largely unquantified. This study was designed with two primary objectives: first, to precisely quantify and elucidate the differences in the physical condition of adolescent athletes before and after activity restrictions due to the pandemic; and second, to innovatively develop and validate a non-contact medical checkup application. Medical checks were conducted on 563 athletes designated for sports enhancement. Participants were junior high school students aged 13 to 15, and the sample consisted of 315 boys and 248 girls. Furthermore, we developed a smartphone application and compared self-checks using the application with in-person checks by orthopedic surgeons to determine the challenges associated with self-checks. Statistical tests were conducted to determine whether there were statistically significant differences in range of motion and flexibility parameters before and after the pandemic. Additionally, items with discrepancies between values self-entered by athletes using the smartphone application and values measured by specialists were detected, and application updates were performed. Student’s t-test was used for continuous variables, whereas the chi-square test was used for other variables. Following the coronavirus 2019 pandemic, athletes were stiffer than during the pre-pandemic period in terms of hip and shoulder joint rotation range of motion and heel–buttock distance. The dominant hip external rotation decreased from 53.8° to 46.8° (p = 0.0062); the non-dominant hip external rotation decreased from 53.5° to 48.0° (p = 0.0252); the dominant shoulder internal rotation decreased from 62.5° to 54.7° (p = 0.0042); external rotation decreased from 97.6° to 93.5° (p = 0.0282), and the heel–buttock distance increased from 4.0 cm to 10.4 cm (p < 0.0001). The heel–buttock distance and straight leg raising angle measurements differed between the self-check and face-to-face check. Although there are items that cannot be accurately evaluated by self-check, physical condition can be improved with less contact by first conducting a face-to-face evaluation under appropriate guidance and then conducting a self-check. These findings successfully address our primary objectives. Specifically, we demonstrated a significant decline in the physical condition of adolescent athletes following pandemic-related activity restrictions, thereby quantifying their impact. Furthermore, our developed non-contact medical checkup application proved to be a viable tool for monitoring physical condition with reduced contact, although careful consideration of measurable parameters is crucial. This study provides critical insights into the long-term effects of activity restrictions on young athletes and offers a practical solution for health monitoring during infectious disease outbreaks, highlighting the potential for hybrid checkup approaches. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

Cystic fibrosis produces viscous mucus in the lung that increases bacterial invasion, causing persistent infections and subsequent inflammation. Pseudomonas aeruginosa and Staphylococcus aureus are two of the most common infections in cystic fibrosis patients that are resistant to antibiotics. One antibiotic approved to treat these infections is levofloxacin (LVX), which functions to inhibit bacterial replication but can be further developed into tailorable particles. Nanoparticles are an emerging inhaled therapy due to enhanced targeting and delivery. The extracellular matrix (ECM) has been shown to possess pro-regenerative and non-toxic properties in vitro, making it a promising delivery agent. The combination of LVX and ECM formed into nanoparticles may overcome barriers to lung delivery to effectively treat cystic fibrosis bacterial infections. Our goal is to advance CF care by providing a combined treatment option that has the potential to address both bacterial infections and lung damage. Two hybrid formulations of a 10:1 and 1:1 ratio of LVX to ECM have shown neutral surface charges and an average size of ~525 nm and ~300 nm, respectively. The neutral charge and size of the particles may suggest their ability to attract toward and penetrate through the mucus barrier in order to target the bacteria. The NPs have also been shown to slow the drug dissolution, are non-toxic to human airway epithelial cells, and are effective in inhibiting Pseudomonas aeruginosa and Staphylococcus aureus. LVX-ECM NPs may be an effective treatment for pulmonary CF bacterial treatments. Full article

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

Hybrid implants composed of magnesium and titanium are a promising direction in orthopaedics, as these implants combine the stability of titanium with the biological activity of magnesium. These partly soluble implants require careful investigation, as the degradation of magnesium releases hydrogen, which can enter the Ti matrix and thus alter the mechanical properties. To investigate this scenario and quantify the hydrogen uptake along with its structural impacts, we employed inert gas fusion, scanning electron microscopy, X-ray diffraction, and a combination of synchrotron absorption and X-ray diffraction tomography. These techniques enabled us to investigate the concentration and distribution of hydrogen and the formation of hydrides in the samples. Titanium hydride formation was observed in a region approximately 120 µm away from the titanium surface and correlates with the amount of absorbed hydrogen. We speculate that the degradation of magnesium at the magnesium/titanium implant interface leads to the penetration of hydrogen due to a combination of electrochemical and gaseous charging. Full article

Background/Objectives: Reinforcement learning (RL), a subset of machine learning, has emerged as a promising tool for supporting precision medicine and dynamic treatment regimes by enabling adaptive, data-driven clinical decision making. Despite its potential, challenges such as interpretability, reward definition, data limitations, and clinician adoption remain. This review aims to evaluate the recent advancements in RL in precision medicine and dynamic treatment regimes, highlight clinical fields of application, and propose practical frameworks for future integration into medical practice. Methods: A systematic review was conducted following PRISMA guidelines across PubMed, MEDLINE, and Web of Science databases, focusing on studies from January 2014 to December 2024. Articles were included based on their relevance to RL applications in precision medicine and dynamic treatment regime within healthcare. Data extraction captured study characteristics, algorithms used, specialty area, and outcomes. Results: Forty-six studies met the inclusion criteria. RL applications were concentrated in endocrinology, critical care, oncology, and behavioral health, with a focus on dynamic and personalized treatment planning. Hybrid and value-based RL methods were the most utilized. Since 2020, there has been a sharp increase in RL research in healthcare, driven by advances in computational power, digital health technologies, and increased use of wearable devices. Conclusions: RL offers a powerful opportunity to augment clinical decision making by enabling dynamic and individualized patient care. Addressing key barriers related to transparency, data availability, and alignment with clinical workflows will be critical to translating RL into everyday medical practice. Full article

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Introduction: Assessing the quality of patient care within healthcare systems remains a multifaceted challenge due to varying definitions of “quality” and the complexity of care delivery structures worldwide. Patient-centeredness, institutional responsiveness, and contextual adaptability are increasingly recognized as core pillars in quality assessment. Objective: This narrative literature review aims to explore conceptual models and practical frameworks for evaluating healthcare quality, emphasizing tools that integrate technical, functional, and emotional dimensions and proposing a comprehensive model adaptable to diverse health system contexts. Methodology: A systematic literature search was conducted in the PubMed, Scopus, and Cochrane Library databases, covering the years 2000 to 2024. Studies were selected based on relevance to quality assessment models, patient satisfaction, accreditation, and strategic improvement methodologies. The review followed a thematic synthesis approach, integrating structural, process-based, and outcome-driven perspectives. Results: Core frameworks such as Donabedian’s model and balancing measures were reviewed alongside evaluation tools like the Dutch Consumer Quality Index, SERVQUAL, and Importance–Performance Analysis (IPA). These models revealed significant gaps between patient expectations and actual service delivery, especially in functional and emotional quality dimensions. This review also identified limitations related to contextual generalizability and bias. A novel integrative model is proposed, emphasizing the dynamic interaction between institutional structure, clinical processes, and patient experience. Conclusions: High-quality healthcare demands a multidimensional approach. Integrating conceptual frameworks with context-sensitive strategies enables healthcare systems to align technical performance with patient-centered outcomes. The proposed model offers a foundation for future empirical validation, particularly in resource-limited or hybrid settings. Full article

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

Background: Aquaculture increasingly seeks sustainable alternatives to fishmeal, a key protein source in fish diets. Black Soldier Fly Larvae (BSFL) meal is a promising substitute, but its effects on fish growth, immunity, and gut health need further investigation. This study aimed to evaluate the impact of varying BSFL inclusion levels on juvenile hybrid grouper (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂), a widely farmed species in tropical aquaculture. Methods: Juvenile hybrid grouper were fed diets with four levels of BSFL substitution (0%, 10%, 30%, and 50%) over 56 days. Key metrics such as growth performance, immune function, antioxidant capacity, and gut transcriptome were analyzed. Results: Replacing fish meal with BSFL meal had no significant effect on the survival rate of hybrid grouper (p > 0.05) but significantly affected growth performance, immune function, and antioxidant capacity (p < 0.05). BSFL10 and BSFL30 groups showed good growth and elevated immune enzyme activity, with significantly higher HIS levels (p < 0.05); the Wf of the BSFL10 group was comparable to the control. However, excessive replacement (BSFL50) led to reduced growth (Wf significantly lower, p < 0.05) and increased oxidative stress, as indicated by higher CAT activity (p < 0.05). Transcriptomic analysis revealed upregulation of immune- and metabolism-related genes with increasing BSFL levels, with immune pathways notably activated in the BSFL50 group. Conclusions: BSFL meal is a promising alternative to fishmeal in juvenile hybrid grouper diets, with moderate inclusion (10–30%) being most beneficial. Excessive BSFL substitution (50%) may impair fish health, highlighting the need for careful formulation in aquaculture diets. Full article

The rapid advancements in healthcare technologies are reshaping the medical landscape, prompting a reconsideration of clinical methodologies and their ethical foundations. This article explores the need for an updated approach to medical deontology, emphasizing the transition from traditional practices to a hybrid clinical methodology that integrates both human expertise and technological innovations. With the increasing use of Artificial Intelligence, data analytics, and advanced medical tools, healthcare professionals are presented with new ethical and professional challenges. These challenges demand a reevaluation of professional responsibility, highlighting the importance of scientific evidence in decision-making while mitigating the influence of economic and ideological factors. By framing medical practice within a systemic and integrated perspective, this article proposes a model that moves beyond the reductionist and anti-reductionist dualism, fostering a more realistic understanding of healthcare. This new paradigm necessitates the evolution of the Medical Code of Ethics, integrating the concept of “medical intelligence” to address the complexities of data management and its ethical implications. The article ultimately advocates for a dynamic and adaptive approach that aligns medical practice with emerging technologies, ensuring that patient care remains person-centered and ethically grounded in a rapidly changing healthcare environment. Full article

Sexual dysfunction is a prevalent and often under-addressed concern among prostate cancer survivors, significantly affecting quality of life for patients and their partners. The True North Sexual Health and Rehabilitation eClinic (SHAReClinic) is a virtual, biopsychosocial intervention developed to improve access to sexual health support for prostate cancer survivors and their partners. This study used a qualitative descriptive design to examine barriers and facilitators influencing the integration of SHAReClinic into oncology care across nine Canadian health care centres. Semi-structured interviews were conducted with 17 knowledge users, including health care providers and institutional leaders. Data were analyzed using a hybrid deductive–inductive thematic approach guided by the Consolidated Framework for Implementation Research (CFIR) 2.0. Participants described SHAReClinic as a much-needed resource, particularly in the absence of standardized sexual health pathways in oncology care. The virtual format was seen as accessible and well suited to addressing sensitive topics. However, limited funding, lack of institutional support, and workflow integration challenges emerged as primary barriers to implementation. Findings offer practical, theory-informed guidance for integrating SHAReClinic into oncology care and highlight key considerations for developing sustainable and scalable survivorship care models. Full article

Background: China’s rapidly aging population has increased the need for effective community-based eldercare services. However, the lack of standardized, culturally relevant evaluation frameworks hinders consistent service quality assessment and improvement. Objective: This systematic review aims to identify, synthesize, and critically evaluate the existing quality indicators (QIs) currently utilized for home-based community elderly care HCEC in China. It also aims to highlight gaps to inform the development of a more comprehensive and context-appropriate quality framework. Methods: Following PRISMA guidelines, systematic searches were conducted across Web of Science, PubMed, Wiley, and CNKI databases for studies published in English and Chinese from 2008 onward. Extracted QIs from eligible studies were categorized using Donabedian’s structure–process–outcome (SPO) model. Results: Fifteen studies met the inclusion criteria, with QI sets ranging from 5 to 64 indicators. Most studies emphasized structural and procedural aspects, while outcome measures were limited. Key gaps include inconsistent terminology, insufficient medical care integration, narrow stakeholder engagement, and limited cultural adaptation of Western theoretical frameworks. Furthermore, subjective weighting methods predominated, impacting indicator reliability. Conclusions: Currently, there is no formal quality framework to guide service providers in HCEC, and therefore, quality indicators can be described as fragmented and lack cultural specificity, medical integration, and methodological robustness. Future research should prioritize developing culturally anchored and medically comprehensive QI frameworks, standardize indicator terminology, actively involve diverse stakeholders through participatory methods, and adopt hybrid methodological approaches combining subjective expert insights and objective, data-driven techniques. Alignment with established international standards, such as the OECD long-term care quality indicators, is essential to enhance eldercare quality and support evidence-based policymaking. Full article