Search Results (596)

A chemiresistive nitric oxide (NO) gas sensor based on Pt/WO${}_3$ co-decorated carbon nanofibers (CNFs) was fabricated using a simple and scalable electrospinning process. This sensor demonstrates high-ppb-level NO detection at room temperature (25 ${}^{\circ }$C), with an experimentally demonstrated detection limit of 100 ppb. It exhibits rapid response, good signal repeatability, excellent batch-to-batch reproducibility, and high selectivity toward NO. Compared with previously reported NO sensors, this work highlights the integration of Pt and WO${}_3$ within a conductive CNF network, enabling room-temperature NO detection down to 100 ppb using a simple chemiresistive architecture. In addition, preliminary sensing tests were conducted using dried simulated breath samples prepared by introducing exogenous NO into exhaled breath from healthy volunteers, demonstrating the sensor’s capability to resolve different NO levels in a complex breath-related background. Owing to its reliable performance and cost-effective fabrication, the sensor holds potential as a NO sensing platform, providing a materials-level basis for future breath NO analysis and other related applications. Full article

Background: Pediatric respiratory disorders frequently necessitate clinical evaluation, often during sleep. Traditional polysomnography (PSG), while the gold standard for sleep-related respiratory assessment, is resource-intensive and can cause discomfort, particularly in children. Therefore, in a prior published study, we designed and technically validated a video-based prototype for contactless monitoring of respiratory movements. Objective: Our present study aimed to clinically validate the contactless monitoring prototype in pediatric patients, with a primary focus on detecting respiratory rate and identifying abnormal breathing patterns. Methods: Twenty-seven pediatric patients (aged 6 months to 12 years) were recruited from a pediatric sleep laboratory. To monitor thoracoabdominal movements in real time, the prototype employed a time-of-flight camera and a 3D imaging module, coupled with artificial-intelligence-based determination of the region of interest (ROI). Respiratory rates obtained from the prototype were compared to simultaneously recorded PSG data. Data were collected under various conditions, including different sleeping positions. A total of 296 h of respiratory data were acquired, of which selected 60 s segments (54 during N3 sleep and 27 during REM sleep) were analyzed using the prototype and compared with PSG-derived respiratory parameters. Conclusion: The contactless prototype demonstrates that reliable and non-invasive respiratory monitoring is feasible in pediatric patients. It enables accurate detection of respiratory rate as well as abnormal breathing patterns under routine clinical conditions, while reducing patient burden compared with conventional approaches. Its usability and minimal patient discomfort suggest potential for broader clinical adoption. Future work should focus on full-night recordings across all sleep stages and the development of automated data analysis pipelines to facilitate routine clinical implementation. 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

Background/Objectives: Non-atherosclerotic pathological findings on coronary arteries involve various disorders that might lead to myocardial ischemia, independent of plaque complications and consequent lumen narrowing and obstruction. These patients often present with non-specific symptoms such as shortness of breath, rapid fatigue, and exertional chest tightness. When the underlying causes are non-atherosclerotic, these findings are frequently overlooked in radiology reports as a possible differential diagnosis. Therefore, the objective of this paper is to present the role of multidetector computed tomography (MD CT) coronary angiography in the diagnostic work-up of patients with rare but clinically valuable non-atherosclerotic pathological conditions of coronary arteries. Methods: We performed a literature search on Medline (via PubMed) for works presenting data on rare, non-occlusive, pathological findings on coronary arteries. Results: The review of the collected literature was performed in a narrative manner, intended to summarize mainly findings of imaging characteristics of non-occlusive pathologies: myocardial bridge, coronary aneurysm, ectasia, fistula, stenosis, and dissection. MD CT images of selected cases that were examined at our department, showing non-occlusive pathological changes in the coronary arteries, are displayed in planar and/or volume-rendered formats. Conclusions: Non-atherosclerotic abnormalities of the coronary vessel wall should be considered in the differential diagnosis of coronary causes of chest pain, dyspnea, and arrhythmias, as they may lead to both acute and chronic myocardial ischemia. Based on the presented literature and specific cases from our clinical practice, MD CT is shown to be an important tool for the rapid, non-invasive evaluation of non-atherosclerotic pathologies. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a prevalent, yet underdiagnosed, disorder associated with cardiovascular and cognitive risks. While overnight polysomnography (PSG) remains the diagnostic gold standard, it is resource-intensive and impractical for large-scale rapid screening. Methods: This study extends prior work on feature extraction and binary classification using tracheal breathing sounds (TBS) and anthropometric data by introducing a meta-modeling framework that utilizes machine learning (ML) and aggregates six one-vs.-one classifiers for multi-class OSA severity prediction. We employed out-of-bag (OOB) estimation and three-fold cross-validation to assess model generalization performance. To enhance reliability, the framework incorporates conformal prediction to provide calibrated confidence sets. Results: In the three-class setting (non, mild, moderate/severe), the model achieved 76.7% test accuracy, 77.7% sensitivity, and 87.1% specificity, with strong OOB performance of 91.1% accuracy, 91.6% sensitivity, and 95.3% specificity. Three-fold confirmed stable performance across folds (mean accuracy: 77.8%; mean sensitivity: 78.6%; mean specificity: 76.4%) and conformal prediction achieved full coverage with an average set size of 2. In the four-class setting (non, mild, moderate, severe), the model achieved 76.7% test accuracy, 75% sensitivity, and 92% specificity, with OOB performance of 88.2% accuracy, 91.6% sensitivity, and 88.2% specificity. Conclusions: These findings support the potential of this non-invasive system as an efficient and rapid OSA severity assessment whilst awake, offering a scalable alternative to PSG for large-scale screening and clinical triaging. Full article

Background: Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders associated with cardiovascular complications, cognitive impairments, and reduced quality of life. Early and accurate diagnosis is essential. The present gold standard, polysomnography, is expensive and resource-intensive. This work develops a non-invasive machine-learning-based framework to classify four OSA severity groups (non, mild, moderate, and severe) using tracheal breathing sounds (TBSs) and anthropometric variables. Methods: A total of 199 participants were recruited, and TBS were recorded whilst awake (wakefulness) using a suprasternal microphone. The workflow included the following steps: signal preprocessing (segmentation, filtering, and normalization), multi-domain feature extraction representing spectral, temporal, nonlinear, and morphological features, adaptive feature normalization, and a three-stage feature selection that combined univariate filtering, Shapley Additive Explanations (SHAP)-based ranking, and recursive feature elimination (RFE). The classification included training ensemble learning models via bootstrap aggregation and validating them using stratified k-fold cross-validation (CV), while preserving the OSA severity and anthropometric distributions. Results: The proposed framework performed well in discriminating among OSA severity groups. TBS features, combined with anthropometric ones, increased classification performance and reliability across all severity classes, providing proof for the efficacy of non-invasive audio biomarkers for OSA screening. Conclusions: TBS-based model’s features, coupled with anthropometric information, offer a promising alternative or supplement to PSG for OSA severity detection. The approach provides scalability and accessibility to extend screening and potentially enables earlier detection of OSA, compared to cases that might remain undiagnosed without screening. Full article

Hakuin Ekaku 白隠慧鶴, the reviver of the Japanese Rinzai school 臨濟宗, introduced Chinese Daoist cosmology and views on the body through the narrative persona of the immortal Hakuyūshi 白幽子 in works such as Yasen Kanna 夜船閑話 (Chats on a Night Boat). He elaborated on specific techniques of Daoist internal alchemy (nèidān 內丹), such as focusing the mind on the dāntián 丹田 (elixir field) and regulating the breath to enter a state of tranquility, as methods to address “Zen sickness” and nurture both body and mind. This approach to self-cultivation exerted a profound influence in Japan. From the late Meiji into the Taishō period (early 20th century), practitioners such as Futaki Kenzo 二木謙三, Fujita Reisai 藤田靈齋, and Okada Torajirō 岡田虎二郎 developed their own health methods based on their respective understandings, forming practices such as the “abdominal breathing method” 腹式呼吸法 and the “method of harmonizing breath and mind” 息心調和法. These contributions promoted the popularization of quiet sitting within Japanese society. Related books were subsequently translated and introduced to China, inspiring modern scholars such as Jiang Weiqiao 蔣維喬 to reinterpret Chinese traditional self-cultivation methods in a new language, leading to the publication of health-preserving works like The Yinshizi’s Method of Quiet Sitting 因是子靜坐法. At the same time, the Chinese self-cultivation community engaged in reflection on and correction of potential drawbacks in the practice of sitting meditation. By tracing this cross-religious and cross-regional process of cultural transmission and transformation, this paper reveals the enduring vitality of Daoist practices during East Asia’s modernization, as well as their universal significance beyond the confines of any single religious tradition. Full article

Cardiogenic pulmonary edema (CPE) is a life-threatening manifestation of acute heart failure characterized by rapid accumulation of fluid in the interstitial and alveolar spaces, leading to severe dyspnea, hypoxemia, and respiratory failure. The condition arises from elevated left-sided filling pressures that increase pulmonary capillary hydrostatic pressure, disrupt alveolo-capillary barrier integrity, and impair gas exchange. Neurohormonal activation further perpetuates congestion and increases myocardial workload, creating a vicious cycle of hemodynamic overload and respiratory compromise. Respiratory support is a cornerstone of management in CPE, aimed at stabilizing oxygenation, reducing the work of breathing, and facilitating ventricular unloading while definitive therapies, such as diuretics, vasodilators, inotropes, or mechanical circulatory support (MCS), address the underlying cause. Among available modalities, non-invasive ventilation (NIV) with continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) has the strongest evidence base in moderate-to-severe CPE, consistently reducing the need for intubation and providing rapid relief of dyspnea. High-flow nasal cannula (HFNC) represents an emerging alternative in patients with moderate hypoxemia or intolerance to mask ventilation, and should be considered an adjunctive option in selected patients with less severe disease or NIV intolerance, although its efficacy in severe presentations remains uncertain. Invasive mechanical ventilation is reserved for refractory cases, while extracorporeal membrane oxygenation (ECMO) and other advanced circulatory support modalities may be necessary in cardiogenic shock. Integration of respiratory strategies with hemodynamic optimization is essential, as positive pressure ventilation favorably modulates preload and afterload, synergizing with pharmacological unloading. Future directions include personalization of ventilatory strategies using advanced monitoring, novel interfaces to improve tolerability, and earlier integration of MCS. In summary, respiratory support in CPE is both a bridge and a decisive therapeutic intervention, interrupting the cycle of hypoxemia and hemodynamic deterioration. A multidisciplinary, individualized approach remains central to improving outcomes in this high-risk population. Full article

This study focuses on the site-specific improvisatory dance performance Deconstruction and Reconstruction of the Path of Life, a self-directed and self-performed work in Lhasa’ s sacred space dominated by a huge Buddha statue. It aims to explore how site-specific context and altitude sickness shape performance, and how freedom and meaning are created within limitations. Using auto-ethnography including video documentation, creative journals and reflective observation, this research examines interactions with spatial elements (Xuan paper, Buddha feet, stairs, flowers) and physiological responses to low oxygen. Main findings include that altitude-induced breath difficulty, chest oppression, and movement imbalance became generative forces: breathing rhythm changes (steady-rapid-steady) symbolized life’s struggles, while a “pain-movement-meaning” chain fostered new bodily senses, framing pain as a gateway to spirituality. Rather than treating the space as a static backdrop, this study explores how the material and cultural characteristics of the location actively lead to dance movement choices and choreographic logic under extreme physiological condition. Full article

Anxiety is a common psychological challenge among athletes, particularly in response to intense training sessions. This randomized crossover study investigated the immediate effects of a single session of focused attention meditation on anxiety, autonomic responses, and performance during high-intensity intermittent training (HIIT) in twenty-six university-level speed skaters. Participants completed three pre-exercise interventions (focused attention meditation, controlled breathing, and random thinking) on separate occasions in a randomized order. Following each intervention, participants performed a leg cycling-based HIIT protocol consisting of 20 s of maximal effort work followed by 10 s of passive rest, repeated for 8 sets using a cycling ergometer. State anxiety was assessed using the State–Trait Anxiety Inventory, and mood disturbance was evaluated using the Profile of Mood States. Autonomic and physiological responses were assessed via heart rate variability (coefficient of variation), oxygen uptake, and power output, measured before and after the intervention and the HIIT bout. Focused attention meditation significantly reduced state anxiety compared with random thinking (ΔSTAI: −5.0 [6.0] vs. −1.0 [4.3]; p < 0.05, effect size = 0.527), whereas controlled breathing primarily influenced heart rate variability (CV: 0.10 [0.11] vs. 0.07 [0.03]; p = 0.041, effect size = 0.736). No significant differences were observed among conditions in mean power output or fatigue index during HIIT. These findings suggest that single-session focused attention meditation may serve as a practical pre-exercise strategy for an immediate reduction in state anxiety, without compromising subsequent high-intensity exercise performance. Full article

This work presents the fabrication and a systematic evaluation of an ionic polymer-metal composite (IPMC) sensor, focusing on its potential for human motion monitoring and human–computer interaction. The sensor was fabricated via a solution casting and electroless plating process, and its morphology characterized using scanning electron microscopy. The sensing performance was comprehensively assessed, revealing high sensitivity (1.059 mV/N) in the low-pressure region, a fast response time (~50 ms), and reliable stability over prolonged cyclic testing. Furthermore, the sensor can respond to both the magnitude and rate of applied mechanical stimuli. To explore its application potential, the IPMC was tested in scenarios ranging from input pattern recognition—including distinguishing mouse-click patterns, handwritten letters, and binary-encoded presses—to human motion monitoring, where it effectively captured and differentiated signals from facial expressions, swallowing, breathing, and joint movements. The results suggest that the developed IPMC sensor is a promising candidate for applications in wearable health monitoring and flexible interactive systems. Full article

Introduction: Parents of children presenting global developmental delay (GDD) need to be involved in their therapy to intensify treatment. Vojta therapy (VT) is an intensive physiotherapeutic treatment that can be administered at home. Whilst parental experience of Home-Based Program (HBP) for preterm or cerebral palsy is well documented, there is a lack of understanding about parents of GDD children on HBP with VT. Objectives: The aim of this work was to describe parents’ perspectives concerning their participation in, concerns with, and perception of the results of an HBP with VT. Methods: A qualitative case design based on an interpretative approach was presented. A purposeful sampling was used. Data was collected in two stages: firstly, semi-structured interviews, and secondly, photo-elicitation. An inductive thematic analysis was used. Results: Seventeen parents were included. Three themes emerged from parents’ perspectives. Firstly, parents’ active participation in VT, which includes their desire to become an active agent to contribute to their child’s improvement, their implication of compromise, learning process, time required, effort, and factors that influence their adherence and continuity. Secondly, parents’ perception of the results achieved: motor improvement and better resting, feeding, and breathing; and time and commitment required to achieve them. Thirdly, parents’ initial concerns about suitability, daily implementation, therapy functioning, or evidence, as well as concerns about emotional bonds. Conclusions: Parents universally perceive that their commitment and efforts were rewarded. They recognized that the emotional bond with their child was strengthened by the therapy. The results regarding the beneficial effects perceived by the parents should be treated with caution, as no instruments for assessing the effect or efficacy were used in this study. Full article

To identify hypertension, Ballistocardiograph (BCG) signals can be primarily utilized. The BCG signal must be thoroughly understood and interpreted so that its application in the classification process could become clearer and more distinct. Various unhealthy habits such as excess consumption of alcohol and tobacco, accompanied by a lack of good diet and a sedentary lifestyle, lead to hypertension. Common symptoms of hypertension include chest pain, shortness of breath, blurred vision, mood swings, frequent urination, etc. In this work, two pragmatic models are proposed for the detection of hypertension using BCG signals and machine learning models. The first model uses K-means clustering, the maximum overlap discrete wavelet transform (MODWT) and the Empirical Wavelet Transform (EWT) techniques for feature extraction, followed by the Binary Tunicate Swarm Algorithm (BTSA) and Information Gain (IG) for feature selection, as well as two efficient hybrid classifiers such as the Hybrid AdaBoost–-Maximum Uncertainty Linear Discriminant Analysis (MULDA) classifier and the Hybrid AdaBoost–Random Forest (RF) classifier for the classification of BCG signals. The second model uses Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and the Random Feature Mapping (RFM) technique for feature extraction, followed by IG and the Aquila Optimization Algorithm (AOA) for feature selection, as well as two versatile hybrid classifiers such as the Hybrid AutoRegressive Integrated Moving Average (ARIMA)–AdaBoost classifier and the Time-weighted Hybrid AdaBoost–Support Vector Machine (TW-HASVM) classifier for the classification of BCG signals. The proposed methodology was tested on a publicly available BCG dataset, and the best results were obtained when the KPCA feature extraction technique was used with the AOA feature selection technique and classified using the Hybrid ARIMA–AdaBoost classifier, reporting a good classification accuracy of 96.89%. Full article

High-flow nasal cannula (HFNC) therapy is frequently described as a positive pressure modality, yet this classification lacks mechanistic support. This critical narrative review integrates experimental, computational, and clinical evidence to examine the established physiological mechanisms underlying HFNC, with emphasis on precise terminology. The study clarifies that labeling HFNC as “positive pressure” is conceptually inaccurate, as the system delivers transient, flow-dependent pressures characteristic of open-circuit administration. Evidence is synthesized to quantify the relative contributions of nasopharyngeal dead-space clearance versus emergent pressure generation. Unlike CPAP, HFNC produces pressures ranging from 0.2 to 13.5 cmH₂O, determined by airway geometry, leak magnitude, and mouth position. Fluid dynamic modeling using Bernoulli and Darcy–Weisbach equations demonstrates oscillatory rather than sustained pressures, with magnitudes linked to nasopharyngeal Reynolds numbers (2400–6000) and turbulent energy dissipation (30–60%). Clinical efficacy persists despite variable pressures, reflecting synergistic mechanisms: inspiratory flow matching (40–50% reduction in work of breathing), dead-space clearance (CO₂ reduction, r = −0.77, p < 0.05), emergent pressure effects (10–20%), and thermal humidification (10–20%). Electrical impedance tomography reveals heterogeneous alveolar recruitment, with high-potential (54%) and low-potential (46%) phenotypes. Based on these mechanistic insights, this review proposes the term “emergent hydrodynamic pressure” to accurately describe HFNC’s transient, flow-dependent pressures. This terminology differentiates HFNC from conventional positive pressure systems and aligns language with the principles of fluid dynamics and respiratory physiology. Full article

Background: During an apnea, oxygen depletion occurs at all tissue levels, so apnea duration is influenced by the mammalian dive reflex, which includes a bradycardia resulting in reduced cardiac oxygen consumption. This study aimed to examine the relationships between heart rate (HR), peripheral estimation of O₂ (SpO₂), deltoid and respiratory muscle oxygenation (SmO₂), and apnea duration. Methods: The study included 10 breath-hold divers (BHD), 39 ± 10 years of age, with body height of 184.3 ± 3.5 cm, body mass of 84.0 ± 9.2 kg, and 16.2 ± 9.7 years of apnea experience. The BHD performed three preparatory apneas followed by three maximal apneas with 5 min of supine rest between each apnea. During all apneas (duration, 115–323 s; involuntary breathing movements (IBMs), 7–35), SmO₂ (measured via NIRS on intercostals (respiratory) and deltoid (locomotor) muscles), heart rate, and SpO₂ (measured via forehead sensor) were obtained. Results: The smallest disagreement in oxygen levels was between intercostal SmO₂ and SpO₂ during the easy-going phase (no IBMs), whereas deltoid desaturation values were more variable. During the struggle phase, Intercostal SmO₂, moderately, and Deltoid SmO₂, strongly, differed from SpO₂. Correlations between apnea duration and O₂ saturation showed that only Intercostal SmO₂ (r = −0.71; p = 0.03) was significantly related to apnea duration. There was also a significant correlation between HR and SpO₂ in the struggle phase (r = −0.58; p = 0.05). Conclusions: These findings suggest that during the struggle phase, SpO₂ and SmO₂ are not highly connected and that local and systemic oxygen levels in the blood are depleted at different rates. Furthermore, the HR response during the struggle phase affected only SpO₂, which indicates that lowering the heart rate may help prevent more rapid deoxygenation. Lastly, the intercostal trend of deoxygenation could be interpreted as respiratory muscle work, suggesting that the increased work of respiratory muscles may prolong apnea duration. Full article