Search Results (631)

Nasal obstruction is a common symptom of nasal conditions, with nasal resistance being a crucial physiological indicator for assessing severity. However, traditional rhinomanometry faces challenges with interference, limited automation, and unstable measurement results. To address these issues, this research designed a nasal resistance measurement system based on multi-sensor fusion of pressure and flow. The system comprises lower computer hardware for acquiring raw pressure–flow signals in the nasal cavity and upper computer software for segmenting and filtering effective respiratory cycles and calculating various nasal resistance indicators. Meanwhile, the system’s anti-interference capability was assessed using recall, precision, and accuracy rates for respiratory cycle recognition, while stability was evaluated by analyzing the standard deviation of nasal resistance indicators. The experimental results demonstrate that the system achieves recall and precision rates of 99% and 86%, respectively, for the recognition of effective respiratory cycles. Additionally, under the three common interference scenarios of saturated or weak breaths, breaths when not worn properly, and multiple breaths, the system can achieve a maximum accuracy of 96.30% in identifying ineffective respiratory cycles. Furthermore, compared to the measurement without filtering for effective respiratory cycles, the system reduces the median within-group standard deviation across four types of nasal resistance measurements by 5 to 18 times. In conclusion, the nasal resistance measurement system developed in this research demonstrates strong anti-interference capabilities, significantly enhances the automation of the measurement process and the stability of the measurement results, and offers robust technical support for the auxiliary diagnosis of related nasal conditions. Full article

Introduction: This study aimed to analyze exhaled breath condensate (EBC) for 8-oxoguanine (8-oxoGua), an oxidative stress biomarker among waterpipe (WP) smokers. Methods: In a within-subject pre-post exposure design, thirty waterpipe smokers completed two 45 min laboratory sessions. EBC was analyzed for 8-oxoGua before and after WP smoking. Median differences between time points (pre vs. post) were assessed using the Wilcoxon sign rank test, with significance defined as p < 0.05. Results: The analysis included 59 WP smoking sessions. Participants had a median age of 24 years (IQR: 21–25), with 62.1% being female. Most had a bachelor’s degree or less (62.1%), and over half were students (55.2%), while 34.5% were employed. The average age for first WP use was 18.6 years, with participants reporting a median of three WP smoking sessions per month. Results indicate a median increase in 8-oxoGua among participants from 5.4 ng/mL (IQR: 8.8) before the smoking session to 7.6 ng/mL after (IQR: 15.7; p < 0.001). Conclusions: This study is the first to examine 8-oxoGua in EBC. Findings provide strong evidence of WP smoking’s contribution to oxidative stress in the airways. It justifies the use of EBC to study the exposure to markers of oxidative stress with emerging tobacco use methods such as the waterpipe. Full article

Respiratory microbiota and lipids are closely associated with airway inflammation. This study aimed to analyze the correlations among the respiratory microbiome, the airway glycerophospholipid–sphingolipid profiles, and airway inflammation in patients with asthma. We conducted a cross-sectional study involving 61 patients with asthma and 17 healthy controls. Targeted phospholipidomics was performed on exhaled breath condensate (EBC) samples, and microbial composition was analyzed via the 16S rDNA sequencing of induced sputum. Asthma patients exhibited significant alterations in the EBC lipid profiles, with reduced levels of multiple ceramides (Cer) and glycerophospholipids, including phosphatidylethanolamine (PE) and phosphatidylcholine (PC), compared with healthy controls. These lipids were inversely correlated with the sputum interleukin-4 (IL-4) levels. Microbiome analysis revealed an increased abundance of Leptotrichia and Parasutterella in asthma patients, both positively associated with IL-4. Correlation analysis highlighted a potential interaction network involving PA, PE, ceramides, Streptococcus, Corynebacterium, Parasutterella, and Leptotrichia. Specific alterations in airway microbiota and phospholipid metabolism are associated with asthma-related inflammation, supporting the concept of a microbiota–phospholipid–immune axis and providing potential targets for future mechanistic and therapeutic studies. Full article

Background: Community-acquired pneumonia (CAP) is the leading cause of mortality in children from middle- to low-income countries; diagnosing CAP includes clinical evaluation, laboratory testing and pulmonary imaging. Lung ultrasound (LUS) is a sensitive, accessible, non-invasive, non-radiant method for accurately evaluating the lung involvement in acute diseases. Whether LUS findings can be correlated with CAP’s severity or sepsis risk remains debatable. This study aimed to increase the importance of LUS in diagnosing and monitoring CAP. We analyzed 102 children aged 1 month up to 18 years, hospital admitted with CAP. Mean age was 5.71 ± 4.85 years. Underweight was encountered in 44.11% of children, especially below 5 years, while overweight was encountered in 11.36% of older children and adolescents. Patients with CAP presented with fever (79.41%), cough (97.05%), tachypnea (18.62%), respiratory failure symptoms (20.58%), chest pain (12.74%) or poor feeding. Despite the fact that 21.56% had clinically occult CAP and six patients (5.88%) experienced radiologically occult pneumonia, CAP diagnosis was established based on anomalies detected using LUS. Conclusions: Detailed clinical examination with abnormal/modified breath sounds and/or tachypnea is suggestive of acute pneumonia. LUS is a sensitive diagnostic tool. A future perspective of including LUS in the diagnosis algorithm of CAP should be taken into consideration. Full article

Measurement of nitric oxide (NO) concentration in exhaled breath (FeNO) is a quantitative, non-invasive, simple, and safe method for assessing airway inflammation. It serves as a complementary tool to other methods for evaluating airway diseases. However, little is known about the typical NO levels in healthy individuals, including individual differences and the influence of measurement timing. Therefore, this study classified measurement times into four periods and statistically analyzed NO levels in healthy individuals. The mean values among groups were compared using repeated measures ANOVA on six participants. The analysis showed large individual variations in NO levels, resulting in no significant difference (p = 0.29). Notably, greater fluctuations were observed in the morning. These findings align with previous studies suggesting the influence of circadian rhythms and the redundancy of repeated measurements. This study highlights the need to consider timing and individual variability when using FeNO as a physiological marker in healthy populations. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

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

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and environmental data, the research identified key patterns and risks. Through three seasonal monitoring campaigns, the concentrations of SO₂ (sulphur dioxide), NO (nitric oxide), NO₂ (nitrogen dioxide), NO_x (nitrogen oxides), HONO (nitrous acid), HNO₃ (nitric acid), O₃ (ozone), NH₃ (ammonia), CH₃COOH (acetic acid), HCOOH (formic acid), and HCHO (formaldehyde) were determined using passive samplers, as well as temperature and relative humidity data loggers. In addition, two specific short-term monitoring campaigns focused on NH₃ were performed to evaluate the influence of visitor presence on indoor concentrations of the above compounds and environmental parameters. NH₃ and HCHO concentrations spiked during high visitor occupancy, with NH₃ levels doubling in crowded periods. Short-term NH₃ campaigns confirmed a direct correlation between visitor numbers and the above indoor concentrations, likely due to human emissions (e.g., sweat, breath) and off-gassing from materials. The indoor/outdoor ratios indicated that several pollutants originated from indoor sources, with ammonia and acetic acid showing the highest indoor concentrations. By measuring the number of visitors and microclimate parameters (temperature and humidity) every 3 s, we were able to precisely estimate the causality and the temporal shift between these quantities, both at small time scale (a few minute delay between peaks) and at medium time scale (daily average conditions due to the continuous inflow and outflow of visitors). Full article

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article

Background/Objectives: Infants admitted to neonatal intensive care units (NICUs) face challenges in achieving successful oral feedings. During oral feedings, these infants commonly present with suck–swallow–breathe incoordination, with approximately 30% developing dysphagia, leading to feeding aversion, prolonged hospitalization, and increased parental stress. Cold liquid feeding has demonstrated benefits in improving feeding safety in adults with dysphagia; however, its application in neonates is relatively limited. This study aimed to examine global neonatal feeding practices, with a specific emphasis on cold milk feeding as an intervention for dysphagia. Methods: A cross-sectional global electronic survey was distributed via professional society listservs and closed online professional group forums targeting neonatal providers and feeding therapists from June 2023 to June 2024. The survey assessed institutional feeding protocols, oral feeding practices, and the use of cold milk for infants with dysphagia. Responses were analyzed descriptively. Results: A total of 210 complete responses were received from level IV (51%), level III (42%), and level II (5%) NICUs. While 30% of the respondents were aware of cold milk feeding as a dysphagia intervention, only 15% of the total respondents reported using it in practice. Among the 32 institutions implementing cold milk practices, only one had an established protocol. Additionally, 72% reported having a feeding protocol in place, often incorporating cue-based tools. Most respondents (87.5%) did not allow oral feeding during nasal continuous positive airway pressure (nCPAP), whereas 78% permitted it during high-flow nasal cannula (HFNC) support. Conclusions: Although the awareness of cold milk feeding in neonates is increasing, its implementation remains limited and lacks standardization. Significant variability exists in oral feeding practices, particularly regarding feeding during respiratory support. This underscores the need for further research and evidence-based guidelines to ensure safe and consistent care for preterm infants. Full article

Objectives: This study aimed to endoscopically assess nasal patency in terms of adenoid obstruction and its mucous coverage, as well as nasal obstruction caused by the inferior nasal turbinate in children with vocal fold nodules. Methods: A retrospective study was conducted involving 54 children admitted to an ENT clinic due to hoarseness caused by vocal fold nodules from 2022 to 2024. The study analyzed medical history, the results of performed flexible nasofiberoscopy and tympanometry. Results: Children with vocal fold nodules snored and slept with open mouths less frequently than the control group of other patients admitted to the ENT outpatient clinic without voice disorders (p = 0.003 and 0.004, respectively). Pathological mucous coverage of the adenoid was observed more often (p = 0.02). The mean adenoid size in the A/C ratio was 52.1% compared to 63.4% in the control group (p = 0.01). Conclusions: Children with vocal fold nodules typically have smaller adenoids, fewer incidents of snoring and open-mouth breathing, but more frequent pathological nasal mucus. It was not possible to prove that the incorrect breathing path through the mouth, causing reduced humidity of the inhaled air, affects the formation of vocal fold nodules. Full article

Objectives: This study explored the health-related quality of life (HRQoL), psychological health, and patient-reported outcomes (PROs) of patients with amyotrophic lateral sclerosis (ALS) in China, providing insights for enhancing clinical care. Methods: A cross-sectional study was conducted among Chinese ALS patients between February and May 2024. Demographics, clinical characteristics, and PROs were assessed. HRQoL and psychological health were evaluated via the 5-item amyotrophic lateral sclerosis assessment questionnaire (ALSAQ-5) and the 4-item patient health questionnaire (PHQ-4), respectively. Spearman’s rank correlation, multiple linear regression, and the Kruskal–Wallis H test were used to analyze associations between clinical factors, HRQoL, and psychological health. Results: A total of 237 participants aged 46–65 years (63.3%) were included. The mean ALSAQ-5 score was 64.86±19.34, indicating an impaired HRQoL, whereas the mean PHQ-4 score (5.82 ± 4.10,) suggested varied degree of anxiety and depression. Age, disease duration, ALS severity, fatigue, stress, and pain severity, and respiratory support were significantly associated with HRQoL (p < 0.05). Age, stress severity, and pain severity were significant predictors of psychological distress (p < 0.01). Patients reported diagnostic delay, profound lifestyle changes (96.4%), fear of paralysis (84.8%), and death (49.8%). Most patients (80.6%) expressed a strong desire to stop ALS progression, prioritizing treatments that improve breathing, muscle weakness, swallowing, and mobility issues. Conclusions: ALS profoundly impacts patients’ HRQoL and psychological health. Integrating PROs into clinical care strategies is crucial for improving patient outcomes and guiding treatment priorities. Full article

Background: Periodontitis is linked to sleep-disordered breathing (SDB), including snoring, with 50–75% of cases involving mouth breathing (MB). Standard treatment includes scaling and root planing (SRP). Oral appliance therapy (OAT) is used to treat snoring and SDB. OAT plus a mouth shield (OAT+) worn during sleep may reduce MB to enhance periodontal health. This study evaluated whether OAT+, as an adjunct to SRP, improves periodontal health by reducing periodontal pathogens and facilitating upper airway patency. Methods: Fourteen participants with mild–moderate periodontitis were randomized to receive SRP on one side of the mouth at baseline (T0). Pocket depth (PD), bleeding on probing (BOP), and plaque index (PI) were recorded, and bacterial DNA from periodontal pockets were analyzed via PCR at baseline (T0) and 12 weeks (T3). At 4 weeks (T1), all participants received a self-titrated myTAP^® OA, followed by a mouth shield at 8 weeks (T2). Sleep metrics, including respiratory disturbance index (RDI), were recorded using the NOX T3 at T0–T3. Results: BOP and deep PD levels exhibited slight improvements from the baseline for both SRP and non-SRP (OAT+ only) treated sites but did not achieve significance. BOP decreased significantly more from the baseline in the SRP than in the non-SRP group at T3 (p = 0.028); P. gingivalis’ presence declined on both sides (p = 0.0135). Other periodontal and bacterial parameters showed no significant differences between or within groups. Snoring (p = 0.011), MB (p = 0.025), and RDI (p = 0.019) significantly decreased with OAT+ at T3. Conclusions: In mild–moderate periodontitis patients who snore, OAT+ reduces snoring, MB, and obstructive events, serving as an adjunct to SRP with no negative clinical effects over the short term. The combined therapy yielded similar results to OAT+ alone, likely due to minimization of MB. Its capacity to improve the oral environment is worthy of further investigation. Full article

Context: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted. Full article

Lymphangioleiomyomatosis (LAM) is a rare progressive disease that affects women of reproductive age and is characterized by cystic lung destruction, airflow obstruction, and lymphatic dysfunction. Current diagnostic methods are costly or lack sufficient specificity, highlighting the need for novel non-invasive approaches. Exhaled breath analysis using real-time proton mass spectrometry (PTR-MS) presents a promising strategy for identifying disease-specific volatile organic compounds (VOCs). This cross-sectional study analyzed exhaled breath samples from 51 LAM patients and 51 age- and sex-matched healthy controls. PTR-time-of-flight mass spectrometry (PTR-TOF-MS) was employed to identify VOC signatures associated with LAM. Data preprocessing, feature selection, and statistical analyses were performed using machine learning models, including gradient boosting classifiers (XGBoost), to identify predictive biomarkers of LAM and its complications. We identified several VOCs as potential biomarkers of LAM, including m/z = 90.06 (lactic acid) and m/z = 113.13. VOCs predictive of disease complications included m/z = 49.00 (methanethiol), m/z = 48.04 (O-methylhydroxylamine), and m/z = 129.07, which correlated with pneumothorax, obstructive ventilation disorders, and radiological findings of lung cysts and bronchial narrowing. The classifier incorporating these biomarkers demonstrated high diagnostic accuracy (AUC = 0.922). This study provides the first evidence that exhaled breath VOC profiling can serve as a non-invasive additional tool for diagnosing LAM and predicting its complications. These findings warrant further validation in larger cohorts to refine biomarker specificity and explore their clinical applications in disease monitoring and personalized treatment strategies. Full article