Search Results (69)

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

This review aims to summarize the global prevalence of healthcare-associated infections in patients with acute heart failure who have been admitted to coronary care units, highlighting the underrepresented burden of infection in this high-risk population. Coronary care units (CCUs) play a pivotal role in the care of patients experiencing acute or decompensated heart failure, offering a highly monitored environment with immediate access to advanced cardiac interventions. The management of heart failure in CCUs involves a multidisciplinary approach that includes hemodynamic monitoring, pharmacologic therapy, respiratory support, and, in selected cases, mechanical circulatory assistance. The early identification of deterioration, rapid therapeutic escalation, and close monitoring of cardiac function are hallmarks of CCU care. However, the complexity and severity of illness in this population are compounded by a high risk of infections, including hospital-acquired pneumonia, bloodstream infections, and device-related infections. These infections not only increase morbidity and prolong hospitalization but also significantly impact mortality and healthcare costs. The immunocompromised state of many heart failure patients—due to poor perfusion, malnutrition, and the use of invasive devices—further elevates their vulnerability. Effective infection prevention, early diagnosis, and targeted antimicrobial therapy are, therefore, critical components of heart failure management within CCUs. This intersection of advanced cardiac care and infection control highlights the need for integrated, multidisciplinary strategies to improve outcomes in this high-risk population. Full article

Platinum-induced ototoxicity constitutes a significant adverse effect in pediatric oncology, frequently resulting in permanent hearing impairment with profound implications for quality of life, language acquisition, and scholastic performance. This comprehensive review critically evaluates contemporary ototoxicity monitoring practices across various pediatric oncology settings, analyzes current guideline recommendations, and formulates strategies for implementing standardized surveillance protocols. Through examination of recent literature—encompassing retrospective cohort investigations, international consensus recommendations, and functional outcome assessments—we present an integrated analysis of challenges and opportunities in managing chemotherapy-associated hearing loss among childhood cancer survivors. Our findings demonstrate marked heterogeneity in monitoring methodologies, substantial implementation obstacles, and considerable impact on survivors’ functional status across multiple domains. Particularly concerning is the persistent absence of an evidence-based consensus regarding the appropriate duration of audiological surveillance for this vulnerable population. We propose a structured framework for comprehensive ototoxicity management emphasizing prompt detection, standardized assessment techniques, and integrated long-term follow-up care to minimize the developmental consequences of platinum-induced hearing impairment. This approach addresses critical gaps in current practice while acknowledging resource limitations across diverse healthcare environments. Full article

Escherichia coli (E. coli) remains a major concern in poultry production due to its ability to incite foodborne illness and public health crisis, zoonotic potential, and the increasing prevalence of antibiotic-resistant strains. The contamination of poultry products with pathogenic E. coli, including avian pathogenic E. coli (APEC) and Shiga toxin-producing E. coli (STEC), presents risks at multiple stages of the poultry production cycle. The stages affected by E. coli range from, but are not limited to, the hatcheries to grow-out operations, slaughterhouses, and retail markets. While traditional detection methods such as culture-based assays and polymerase chain reaction (PCR) are well-established for E. coli detection in the food supply chain, their time, cost, and high infrastructure demands limit their suitability for rapid and field-based surveillance—hindering the ability for effective cessation and handling of outbreaks. Biosensors have emerged as powerful diagnostic tools that offer rapid, sensitive, and cost-effective alternatives for E. coli detection across various stages of poultry development and processing where detection is needed. This review examines current biosensor technologies designed to detect bacterial biomarkers, toxins, antibiotic resistance genes, and host immune response indicators for E. coli. Emphasis is placed on field-deployable and point-of-care (POC) platforms capable of integrating into poultry production environments. In addition to enhancing early pathogen detection, biosensors support antimicrobial resistance monitoring, facilitate integration into Hazard Analysis Critical Control Points (HACCP) systems, and align with the One Health framework by improving both animal and public health outcomes. Their strategic implementation in slaughterhouse quality control and marketplace testing can significantly reduce contamination risk and strengthen traceability in the poultry value chain. As biosensor technology continues to evolve, its application in E. coli surveillance is poised to play a transformative role in sustainable poultry production and global food safety. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Groundwater remains a critical water source for urban communities, particularly in rapidly urbanizing countries such as the Philippines. However, intensifying anthropogenic pressures have contributed to widespread contamination from heavy metals, nutrients, pathogens, volatile organic compounds (VOCs), and emerging pollutants, including pharmaceuticals and personal care products (PPCPs). This review synthesizes findings from 130 peer-reviewed studies on groundwater monitoring and remediation, emphasizing technological advancements and their application in urban environments. The literature is categorized into five thematic areas: monitoring technologies, contaminant profiles, remediation strategies, Philippine-specific case studies, and alignment with global frameworks. Recent innovations—such as Internet of Things (IoT)-enabled systems, remote sensing, biosensors, and artificial intelligence/machine-learning (AI/ML) models—show strong potential for real-time and predictive monitoring. Despite these advancements, technology adoption in the Philippines remains limited due to regulatory, technical, and infrastructural constraints. This review identifies key research and implementation gaps, particularly in the monitoring of emerging contaminants and the integration of data into policy-making and urban planning. To address these challenges, a conceptual framework is proposed to support more sustainable, technology-driven, and context-sensitive groundwater management in heavily urbanized areas. Full article

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article

Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use. Full article

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings. Full article

Objectives: This study aims to develop performance indicators for service robotics in senior community-based environments and analyze their impact on independent living and quality of life for older adults. Methods: To achieve this, a sequential exploratory design within the Mixed Methods Research (MMR) framework was employed, integrating qualitative research (Focus Group Interview, FGI) and quantitative research (Analytic Hierarchy Process, AHP). The FGIs were conducted with a panel of six experts over three rounds, leading to the identification of six key performance indicators (KPIs) for service robotics in senior communities: Technical Performance, User-Centered Performance, Social and Psychological Impact, Ethical and Safety Performance, Economic and Operational Performance, and Service Efficiency. Following this, the AHP analysis was conducted with a final sample of 29 participants from an initial 32 respondents. Results: The AHP analysis results revealed that Technical Performance (rank 1, 0.256) was the most critical factor, followed by User-Centered Performance (rank 2, 0.205) and Social and Psychological Impact (rank 3, 0.167). These findings suggest that enhancing a user-friendly, intuitive UI/UX is essential for ensuring ease of use by older adults. Additionally, while Ethical and Safety Performance (rank 3, 0.139), Economic and Operational Performance (rank 4, 0.126), and Service Efficiency (rank 5, 0.105) had relatively lower importance scores, the study highlights the necessity of establishing optimized systems through ethical and safety standards and emphasizes that real-time monitoring systems play a crucial role in enhancing operational efficiency. Conclusions: Enhancing service robotics performance requires prioritizing technical capabilities and user-centered design, along with ethical standards and real-time monitoring. This study proposes a structured evaluation framework to support more effective robotic solutions in senior care environments. Full article

Conservation of rare plant species diversity is often found within the context of disturbance and land use planning. In mountainous regions, globally, critical plant conservation issues can occur at esthetically pleasing topoedaphic positions, such as popular mountain summits. Here, we assess the spatiotemporal ecology of an imperiled cushion plant assemblage in such a situation. Plant community dynamics of three rare cushion plant species [scented pussytoes (Antennaria aromatica), Howard’s alpine forget-me-not (Eritrichum howardii), and Shoshone carrot (Shoshonea pulvinata)] were measured at a 2475 m mountain summit near Cody, WY, USA. The survey was conducted in the summer of 2017–2019 using 1 m² quadrats across three macroplots (ranging from 295 to 2250 m² in size) to estimate all vascular plant species abundance. Altitude, canopy height, vegetative cover, standing dead biomass, rock, litter, and bare soil were also measured. We assessed annual changes in abundances, richness (#), evenness (N2/N1), and diversity (H′) and performed a constrained ordination to understand ecological drivers of distribution. Nineteen total plant species were identified, all of which were native perennial species. Five additional species were also noted to be species of conservation concern. For the three rare cushion plants of focus, abundance did not significantly change over the three-year period. Species richness was lower in 2017 than in subsequent years, but there was no difference in evenness or diversity. In the constrained ordination, the first axis explained 56.1% of the variation and was attributed to the rock-to-vegetation gradient of the environment, while the second axis explained an additional 28.7% of the variance and was attributed to altitude. The three rare cushion plants of focus appeared to segregate and occupy differential habitat niches. The popularity of this mountain peak, coupled with the presence of a diverse rare cushion plant community, should facilitate the careful monitoring and management of tourism to ensure the conservation of diversity. Full article

Detecting kidney function biomarkers is critical for the early diagnosis of kidney diseases and monitoring treatment efficacy. In this work, a portable, 3D-printed colorimetric sensor platform was developed to detect key kidney biomarkers: uric acid, creatinine, and albumin. The platform features a 3D-printed enclosure with integrated diffused LED lighting to ensure a controlled environment for image acquisition. A disposable 3D-printed flow cell holds samples, ensuring precision and minimizing contamination. The sensor relies on colorimetric analysis, where a reagent reacts with blood serum to produce a color shift proportional to the biomarker concentration. Using a smartphone, the color change is captured, and RGB values are normalized to calculate concentrations based on the Beer-Lambert Law. The system adapts to variations in smartphones, reagent brands, and lighting conditions through an adaptive calibration algorithm, ensuring flexibility and accuracy. The sensor demonstrated good linear detection ranges for uric acid (1–30 mg/dL), creatinine (0.1–20 mg/dL), and albumin (0.1–8 g/dL), with detection limits of 1.15 mg/dL, 0.15 mg/dL, and 0.11 g/dL, respectively. These results correlated well with commercial biochemistry analyzers. Additionally, an Android application was developed to handle image processing and database management, providing a user-friendly interface for real-time blood analysis. This portable, cost-effective platform shows significant potential for point-of-care diagnostics and remote health monitoring. Full article

The Internet of Things (IoT) enables remote monitoring of various environmental components through existing network infrastructures, thereby facilitating the integration of diverse computing systems. IoT systems encompass a wide range of devices and communication protocols, offering flexibility across various application domains. This adaptability makes IoT solutions particularly suitable for healthcare applications. For example, hospitals have implemented the Internet of Medical Things (IoMT) to collect and transmit patient data to healthcare professionals, as continuous monitoring is critical for patients in intensive care. Healthcare systems often demand high availability and have stringent performance requirements due to the necessity for rapid medical decision-making. However, the simultaneous assessment of performance and availability in IoMT systems is often overlooked. This paper introduces a modeling approach using stochastic Petri nets (SPNs) to evaluate both the availability and performance of IoMT systems. The approach also takes into account redundancy techniques, which may significantly improve system availability. The results highlight the practical feasibility of the proposed approach, demonstrating a reduction in downtime from 46.36 h to 0.21 h, while the response time remained constant. This indicates that the proposed modeling approach can enhance system availability without compromising performance. In addition, the proposed models adopt data collected from a real environment designed to support this approach. Furthermore, a sensitivity analysis was performed to identify the components that have a significant impact on system operation. Full article

The rapid development of flexible sensor technology has made flexible sensor arrays a key research area in various applications due to their exceptional flexibility, wearability, and large-area-sensing capabilities. These arrays can precisely monitor physical parameters like pressure and strain in complex environments, making them highly beneficial for sectors such as smart wearables, robotic tactile sensing, health monitoring, and flexible electronics. This paper reviews the fabrication processes, operational principles, and common materials used in flexible sensors, explores the application of different materials, and outlines two conventional preparation methods. It also presents real-world examples of large-area pressure and strain sensor arrays. Fabrication techniques include 3D printing, screen printing, laser etching, magnetron sputtering, and molding, each influencing sensor performance in different ways. Flexible sensors typically operate based on resistive and capacitive mechanisms, with their structural designs (e.g., sandwich and fork-finger) affecting integration, recovery, and processing complexity. The careful selection of materials—especially substrates, electrodes, and sensing materials—is crucial for sensor efficacy. Despite significant progress in design and application, challenges remain, particularly in mass production, wireless integration, real-time data processing, and long-term stability. To improve mass production feasibility, optimizing fabrication processes, reducing material costs, and incorporating automated production lines are essential for scalability and defect reduction. For wireless integration, enhancing energy efficiency through low-power communication protocols and addressing signal interference and stability are critical for seamless operation. Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. Finally, ensuring long-term stability and environmental adaptability demands new materials and protective coatings to withstand harsh conditions. Ongoing research and development are crucial to overcoming these challenges, ensuring that flexible sensor arrays meet the needs of diverse applications while remaining cost-effective and reliable. Full article

High-sensitivity and high-specificity biodetection is critical for advancing applications in life sciences, biosafety, food safety, and environmental monitoring. CRISPR/Cas systems have emerged as transformative tools in biosensing due to their unparalleled specificity, programmability, and unique enzymatic activities. They exhibit two key cleavage behaviors: precise ON-target cleavage guided by specific protospacers, which ensures accurate target recognition, and bystander cleavage activity triggered upon target binding, which enables robust signal amplification. These properties make CRISPR/Cas systems highly versatile for designing biosensors for ultra-sensitive detection. This review comprehensively explores recent advancements in CRISPR/Cas system-based biosensors, highlighting their impact on improving biosensing performance. We discuss the integration of CRISPR/Cas systems with diverse signal readout mechanisms, including electrochemical, fluorescent, colorimetric, surface-enhanced Raman scattering (SERS), and so on. Additionally, we examine the development of integrated biosensing systems, such as microfluidic devices and portable biosensors, which leverage CRISPR/Cas technology for point-of-care testing (POCT) and high-throughput analysis. Furthermore, we identify unresolved challenges, aiming to inspire innovative solutions and accelerate the translation of these technologies into practical applications for diagnostics, food, and environment safety. Full article