Search Results (73)

Pediatric obstructive sleep apnea (OSA) is a common disorder associated with significant neurocognitive, behavioral, cardiovascular, and metabolic consequences. Although adenotonsillectomy remains first-line therapy for many children, residual OSA is common, particularly among those with obesity, craniofacial abnormalities, genetic syndromes, neuromuscular disease, or other forms of medical complexity. Continuous positive airway pressure (CPAP) is the standard non-surgical treatment; however, long-term effectiveness is frequently limited by poor tolerance and adherence. Heated high-flow nasal cannula (HFNC) therapy has emerged as a potential alternative for selected children with sleep-disordered breathing, particularly those who are unable to tolerate conventional positive airway pressure therapy. Unlike CPAP, HFNC delivers heated, humidified gas through an open nasal interface and may improve sleep-disordered breathing through a combination of flow-dependent positive airway pressure generation, dead-space washout, improved ventilatory efficiency, enhanced gas conditioning, and reductions in inspiratory resistance. However, the relative contribution of these mechanisms during sleep remains incompletely understood. Current clinical evidence consists primarily of physiological studies, retrospective cohorts, case series, and a limited number of prospective comparative studies. Collectively, these data suggest that HFNC can reduce obstructive respiratory events and improve oxygenation in selected pediatric populations, including children with persistent OSA, CPAP intolerance, medical complexity, and syndromic conditions. Nevertheless, important uncertainties remain regarding optimal patient selection, titration strategies, patient monitoring, long-term adherence and comparative effectiveness relative to CPAP. This review summarizes the physiological basis of HFNC therapy, critically appraises the current clinical evidence, discusses practical considerations related to adherence and implementation, and highlights key knowledge gaps and future research priorities. Overall, HFNC should be viewed as an alternative for selected children who cannot tolerate CPAP, rather than as a universal substitute for pressure-based therapy. Full article

Background/Objectives: Children with cancer admitted to the pediatric intensive care unit (PICU) are at increased risk for post-intensive care syndrome (PICS-p) due to prolonged immobility, deep sedation, and severe illness. The ABCDEF bundle offers a framework for enhancing ICU care and patient recovery, but implementing all components in pediatric oncology patients is challenging. This study assesses the development and implementation of the BRAVE (Beginning Restorative Activities Very Early) initiative, specifically BRAVE2, to integrate the comprehensive ABCDEF bundle and a nurse-led mobility program, in collaboration with rehabilitation specialists, within a pediatric oncology intensive care unit. Methods: BRAVE2 was a quality improvement project conducted in a single pediatric ICU from 2022 to 2023. We analyzed ICU data to assess patient demographics, frequency of physical and occupational therapy (PT/OT) consultations, time to initial mobilization, and delirium screening rates (CAPD score of 9 or higher) for patients with ICU stays over 48 h. BRAVE2 addressed all elements of the ABCDEF bundle, including regular pain assessments, evaluation of spontaneous breathing readiness, sedation adjustments, delirium screening, early mobilization, and family engagement. Outcomes were monitored using statistical process control methods. Results: Of 140 patients, 117 (84%) remained in the ICU for more than 48 h. The delirium screening rate was 15.5%, consistently below the target of 30%. PT/OT consultations within 72 h occurred in 80.7% of patients, and early mobilization in 49.7% of patients, both below the 80% goal. However, 90.4% of patients with tracked mobility were able to ambulate during their ICU stay. No mobility-related safety incidents were reported. Conclusions: Rolling out a full ICU liberation plan in a pediatric oncology ICU is possible, and implementing a comprehensive one is feasible and sustainable despite challenges. Although therapist-led early mobilization did not meet targets, incorporating nurse-led mobility strategies and routine delirium screening has established a scalable model to enhance ICU care and support long-term recovery for these patients. Full article

Construction dust is a major contributor to urban inhalable particulate matter (PM₁₀) pollution, posing severe respiratory and cardiovascular health risks to construction workers and nearby residents, severely undermining urban environmental sustainability. Construction fences are widely adopted as a primary dust mitigation measure, yet their underlying dispersion mechanisms and comprehensive impacts on vertical air quality remain poorly understood due to the limitations of traditional field monitoring and empirical models, creating critical barriers to site-level pollution control and long-term urban sustainability. In this study, a reliable computational fluid dynamics (CFD) method was developed to investigate the spatial distribution of construction dust and quantify the dust suppression performance of fences with heights ranging from 0 to 3 m. Three mainstream k-ε turbulence models (Standard, RNG, and Realizable) were evaluated using on-site measurement data, and the RNG k-ε model was found to provide the best agreement with field observations, with statistical metrics of q = 1, FB = 0.052, and NMSE = 0.028. The results show that construction fences effectively reduce dust dispersion into the surrounding environment, particularly in the pedestrian breathing zone (z < 1.5 m). Increasing the fence height from 1.5 m to 3 m improves the breathing-zone dust reduction rate from 39% to 55%, with the most significant mitigation effect observed within 50 m downwind of the fence. However, a critical dual effect was identified: while fences suppress near-ground pollution, they induce strong upward airflow and turbulence, leading to elevated dust concentrations in the upper part of the near-ground region (z = 1.5–9 m), a phenomenon absent in the no-fence scenario. These findings provide practical implications for urban construction site management, suggesting that fence height and configuration should be carefully designed not only to reduce pedestrian-level exposure but also to avoid unintended pollutant accumulation aloft, thereby improving overall air quality control strategies and delivering balanced, long-term environmental sustainability at construction sites. Full article

Background: Long-term symptoms after SARS-CoV-2 infection such as fatigue, shortness of breath and cognitive impairment represent a major burden on society. Risk factors include female sex, smoking, comorbidities and socioeconomic deprivation. Physical activity (PA) has been suggested as a potential protective factor, although all population groups, including athletes, were affected. Therefore, this study aimed to investigate the influence of PA duration and intensity on the odds of the presence of long-term symptoms. Methods: This cross-sectional study included 5413 individuals following acute COVID-19 within the CoCo-Fakt online monitoring study. The type, duration and intensity of exercise in the four weeks before quarantine were recorded, and the odds of the presence of long-term symptoms beyond 12 weeks after infection were determined, adjusted for demographics, health status and acute COVID-19 outcomes. Results: Among participants, 561 (10.4%) reported long-term symptoms. Those with long-term symptoms reported a longer duration (p = 0.019, d = −0.61) of exercise in the four weeks before quarantine compared to those without long-term symptoms. Adjusted for demographics, health status and acute COVID-19 outcomes, higher exercise intensity (MET/day) was associated with 16.7% increased odds of long-term symptoms (Nagelkerke R² = 18.0%). After the Bonferroni–Holm correction, this association did not remain significant. Conclusions: Current data suggests that PA has a protective effect on post-COVID-19 condition when performed at a moderate level. In our study, however, neither PA intensity nor duration emerged as a predictor of long-term symptoms. Future studies must clarify which intensities and types of exercise can help to maintain overall physical and mental health and to prevent or improve the long-term outcomes following SARS-CoV-2 infection, considering individual circumstances. Full article

Wind turbine blades are subjected to complex environmental conditions during long-term operation, which may lead to structural degradation and performance loss. To ensure structural integrity, fatigue testing prior to deployment is essential. This paper proposes a vision-based method for measuring the full-cycle breathing deformation of wind turbine blades during fatigue testing. The method captures dynamic image sequences of the blade’s hotspot cross-section using industrial cameras and employs a feature-based template matching approach to reconstruct the three-dimensional coordinates of target points. Through coordinate transformation, the deformation trajectories are obtained, enabling quantitative analysis of the blade’s dynamic responses in both flapwise and edgewise directions. A dedicated hardware–software system was developed and validated through full-scale fatigue experiments. Quantitative comparison with strain gage measurements shows that the proposed method achieves mean absolute deviations of 0.84 mm and 0.93 mm in two independent experiments, respectively, with closely matched deformation trends under typical loading conditions. These results demonstrate that the proposed method can reliably capture the global deformation behavior of the blade with millimeter-level accuracy, while significantly reducing instrumentation complexity compared to conventional contact-based approaches. The proposed method provides an effective and practical solution for full-field dynamic deformation measurement in blade fatigue testing, offering strong potential for structural health monitoring and early damage detection in wind turbine systems. Full article

Respiration rate (RR) is a key physiological indicator of health, stress, and thermoregulatory load in dairy cattle, yet continuous RR monitoring under commercial farm conditions remains challenging. In this Technical Note, we present a non-invasive clip-on nose ring device for continuous respiration monitoring based on acoustic recording directly at the nostril. The device integrates a MEMS microphone, embedded electronics, battery, and removable storage in a sealed, mechanically robust housing suitable for real-world barn environments. The system was deployed on five dairy cows under commercial farm conditions, enabling repeated multi-day recordings over several weeks. The respiration rate was extracted offline from raw audio using a deterministic signal-processing pipeline based on multiscale periodicity detection. Algorithm-derived RR estimates were evaluated against manually annotated breath events. Using 10-min rolling median values, the algorithm achieved a mean absolute error (MAE) of 1.47 breaths per minute (bpm), a root mean square error (RMSE) of 1.92 bpm, and a high correlation with reference values (r = 0.98, R² = 0.96). In addition to short-term accuracy, the system enabled stable multi-day monitoring. Group-level analysis across all five animals revealed a clear diurnal respiration pattern over multiple consecutive days, with lower RR during nighttime and higher RR during daytime summer conditions, without signs of a baseline drift. These results demonstrate the feasibility of continuous, long-term respiration monitoring in dairy cattle using an audio-based clip-on nose ring device and provide a practical foundation for longitudinal (multi-day, within-animal) RR assessment under commercial farm conditions, with potential for future extensions towards advanced respiratory health monitoring. While the system demonstrated stable performance under summer farm conditions, validation under extreme heat-stress environments and larger animal cohorts is required for comprehensive population-level assessment. Full article

Respiratory rate (RR) is a critical vital sign for the early detection of hypoxia and respiratory deterioration, yet its continuous monitoring remains challenging in clinical environments. Photoplethysmography (PPG) provides a non-invasive source of physiological information from which respiratory dynamics can be inferred. In this study, numerical physiological features derived from PPG data were used to comparatively evaluate multiple deep learning models for respiratory rate estimation. Fixed-length sliding windows were constructed from the dataset and used to train five neural network architectures: a Deep Feedforward Neural Network (DFNN), unidirectional and bidirectional Recurrent Neural Networks (RNN, Bi-RNN), and unidirectional and bidirectional Long Short-Term Memory networks (LSTM, Bi-LSTM). Model performance was assessed using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), and computational runtime. Results indicate that models incorporating temporal dependencies outperform the static feedforward baseline, achieving MAE values as low as 0.521 breaths/min, making them competitive with or lower than previously reported PPG-based approaches. These findings highlight the effectiveness of temporal deep learning models for respiratory rate estimation from PPG-derived numerical features and provide insight into accuracy–efficiency trade-offs relevant to real-time monitoring applications. Full article

During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. Full article

Methane (CH₄) is a potent greenhouse gas, with livestock rumination being a significant contributor to global emissions. This study developed a real-time monitoring system utilizing Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) to simultaneously track rumination behavior and CH₄ concentrations in cattle breath. By optimizing the off-axis integrated cavity structure and implementing a specialized environmental control system, we enhanced stability and detection accuracy, achieving a rapid 3 s response time to dynamic concentration changes. Laboratory stability tests and Allan deviation analysis demonstrated a minimum detection limit of 0.07 ppm. Continuous field monitoring of Simmental cattle revealed a daily methane production of approximately 311.83 L. The emission rates exhibited a distinct double-peak pattern heavily influenced by feeding schedules. Furthermore, a positive correlation was observed between the time elapsed post feeding and both the frequency and intensity of methane emission peaks. This method enables highly dynamic, stable, long-term monitoring of greenhouse gas emissions from ruminants, providing a robust tool for quantifying emissions and informing scientific feeding practices. Full article

Roadside public transport stops represent localized air pollution hotspots where short-term exposure may differ substantially from levels reported by urban background monitoring. This study investigates the application of low-cost air quality sensors for long-term characterization of particulate matter and gaseous pollutants in a traffic-dominated urban microenvironment. The novelty of this work lies in the combined use of collocated low-cost sensors, energy-independent solar-powered deployment, height-resolved placement representative of different breathing zones, and integrated statistical and predictive analysis to resolve exposure-relevant pollutant dynamics at a single transport stop. Hourly concentrations of particulate matter (PM) PM₁, PM₂.₅, PM₁₀, nitrogen dioxide (NO₂), and ozone (O₃) were measured over one year at a roadside transport stop adjacent to a four-lane urban road carrying approximately 30,000 vehicles per day. Measurements were obtained using two collocated low-cost sensor units based on optical particle sensing for particulate matter and electrochemical sensing for gases, together with concurrent meteorological observations. Strong agreement between the two particulate matter sensors supported the use of averaged concentrations. Mean PM₂.₅ concentrations were substantially higher in winter (32.4 µg/m³) than in summer (10.4 µg/m³), indicating pronounced seasonal variability. PM₁ and PM₂.₅ exhibited closely aligned temporal patterns, while PM₁₀ showed greater variability. NO₂ displayed sharp diurnal peaks associated with traffic activity, whereas O₃ exhibited opposing seasonal and diurnal behavior and was negatively correlated with both PM₂.₅ (r = −0.32) and NO₂ (r = −0.29). One-hour-ahead predictive models incorporating meteorological and temporal variables achieved coefficients of determination up to 0.84. The results demonstrate that energy-independent low-cost sensor systems can robustly capture temporal patterns, pollutant interactions, and short-term predictability in localized roadside environments relevant to exposure assessment. Full article

Humidity sensing is essential in medical fields such as respiratory support, neonatal care, sterilization, and pharmaceutical storage. However, current sensors face limitations, including slow response/recovery, low sensitivity, and poor long-term stability. To address these challenges, we developed borophene-multiwalled carbon nanotube (MWCNT) heterostructures using a stepwise in situ thermal decomposition method. The resulting humidity sensor exhibits an ultrabroad detection range (11–97% RH), ultra-high sensitivity (55,000% at 97% RH), and fast response/recovery times (10.04 s/4.8 s). Through interfacial charge-transfer engineering, the system facilitates rapid electron migration, enhances Schottky barrier modulation, and provides abundant active adsorption sites for water molecules, thereby achieving comprehensive improvement in sensing performance. It also demonstrates excellent selectivity, mechanical flexibility, and operational stability. Notably, the sensor’s sensitivity at 97% RH surpasses that of sensors based on pure borophene or MWCNT by 37–462 times, highlighting the advantages of heterostructure engineering. The multifunctionality of the device suggests its potential in areas beyond conventional sensing, including non-contact voice recognition, skin humidity mapping, and real-time breath monitoring. These results lay a solid foundation for developing borophene-MWCNT heterostructures into a high-performance platform for next-generation medical diagnostics and intelligent health monitoring. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Electronic nose (e-nose) systems have emerged as transformative tools for odor and gas analysis, leveraging advances in nanomaterials, sensor arrays, and machine learning (ML) to mimic biological olfaction. This review synthesizes recent developments in e-nose technology, focusing on innovations in sensor design (e.g., graphene-based nanomaterials, MEMS, and optical sensors), drift compensation techniques, and AI-driven data processing. We highlight key applications across healthcare (e.g., non-invasive disease diagnostics via breath analysis), food quality monitoring (e.g., spoilage detection and authenticity verification), and environmental management (e.g., pollution tracking and wastewater treatment). Despite progress, challenges such as sensor selectivity, long-term stability, and standardization persist. The paper underscores the potential of e-noses to replace conventional analytical methods, offering portability, real-time operation, and cost-effectiveness. Future directions include scalable fabrication, robust ML models, and IoT integration to expand their practical adoption. Full article

This study evaluates occupational exposure to respirable particulate matter (PM2.5) and crystalline silica (c-silica) among workers in five ceramic industries in Indonesia. Personal sampling revealed that 55.3% of workers were exposed to c-silica levels exceeding the Threshold Limit Value (TLV) of 50 µg/m³, with concentrations ranging from 1.5 to 1395.3 µg/m³. PM2.5 levels reached as high as 4152.4 µg/m³ in certain production zones. Health surveys identified frequent respiratory symptoms such as shortness of breath (27.1%) and chronic cough (14.6%), with 6.4% of workers showing lung abnormalities on chest X-rays. Risk assessments based on chronic daily intake (CDI), hazard quotient (HQ), and risk quotient (RQ) revealed that 63.8% of workers faced unsafe exposure, particularly those with longer job tenures, older age, and poor compliance with personal protective equipment (PPE). To mitigate risks, the study recommends engineering controls such as more local exhaust ventilation, improved PPE usage, and administrative measures including job rotation and regular health monitoring. These findings highlight the urgent need for improved occupational health strategies in silica-intensive industries and call for further research on long-term health impacts and effective intervention programs. Full article

Obesity Hypoventilation Syndrome (OHS), also known as Pickwickian syndrome, is a complex disorder characterized by obesity (BMI > 30 kg/m²), daytime hypercapnia (PaCO₂ ≥ 45 mmHg), and sleep-disordered breathing, primarily affecting individuals with severe obesity. Its diagnosis requires the exclusion of other causes of alveolar hypoventilation and involves comprehensive assessments, including clinical history, physical examination, pulmonary function tests, arterial blood gases, and sleep studies. The pathophysiology of OHS involves mechanical constraints from excessive adipose tissue, diminished central respiratory drive often linked to leptin resistance, mitochondrial dysfunction, and oxidative stress, all contributing to impaired ventilation and systemic inflammation. The condition often coexists with obstructive sleep apnea (OSA), exacerbating nocturnal hypoxia and hypercapnia, which can lead to severe cardiopulmonary complications such as pulmonary hypertension and right-sided heart failure. Epidemiologically, the rising global prevalence of obesity correlates with an increased incidence of OHS, yet underdiagnosis remains a significant challenge, often resulting in critical presentations like acute hypercapnic respiratory failure. Management primarily centers on non-invasive ventilation modalities like CPAP and BiPAP, with an emphasis on individualized treatment plans, continuous monitoring, and addressing comorbidities such as hypertension and diabetes. Pharmacological interventions are still evolving, focusing on supportive care and metabolic regulation. Long-term adherence, psychological factors, and complications like ventilator failure or device intolerance highlight the need for ongoing multidisciplinary management. Overall, advancing our understanding of OHS’s multifactorial mechanisms and optimizing tailored therapeutic strategies are crucial for improving patient outcomes and reducing mortality associated with this increasingly prevalent syndrome. Full article