Search Results (1,208)

Exhaled breath analysis (EBA) is an advanced, non-invasive diagnostic technique that utilizes volatile organic compounds (VOCs) to detect and monitor various diseases. This review examines EBA’s historical development and current status as a promising diagnostic tool. It highlights the significant contributions of modern methods such as gas chromatography–mass spectrometry (GC-MS), ion mobility spectrometry (IMS), and electronic noses in enhancing the sensitivity and specificity of EBA. Furthermore, it emphasizes the transformative role of nanotechnology and machine learning in improving the diagnostic accuracy of EBA. Despite challenges such as standardization and environmental factors, which must be addressed for the widespread adoption of this technique, EBA shows excellent potential for early disease detection and personalized medicine. The review also highlights the potential of photonic crystal fiber (PCF) sensors, known for their superior sensitivity, in the field of EBA. Full article

In this study, a hybrid sensor-based electronic nose circuit was developed using eight metal-oxide semiconductors and 14 quartz crystal microbalance gas sensors. This study included 100 participants: 60 individuals diagnosed with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers. A total of 338 experiments were performed using breath samples throughout this study. In the classification phase of the obtained data, in addition to traditional classification algorithms, such as decision trees, support vector machines, k-nearest neighbors, and random forests, the fuzzy logic method supported by the optimization algorithm was also used. While the data were classified using the fuzzy logic method, the parameters of the membership functions were optimized using a nature-inspired optimization algorithm. In addition, principal component analysis and linear discriminant analysis were used to determine the effects of dimension-reduction algorithms. As a result of all the operations performed, the highest classification accuracy of 94.58% was achieved using traditional classification algorithms, whereas the data were classified with 97.93% accuracy using the fuzzy logic method optimized with optimization algorithms inspired by nature. Full article

Background/Objectives: There are many commercial mouthrinses, used for a variety of purposes, including antiseptic activity. The objective of this study was to determine the antibacterial activity of various TheraBreath™ oral rinses against the cariogenic bacterium, Streptococcus mutans, and saliva-derived microbial communities, and their antibiofilm activity against S. mutans in vitro biofilms. Methods: Bactericidal activity against planktonic S. mutans was assessed by colony counting after 30 and 2 min exposures to mouthrinses. Ten saliva samples were exposed to mouthrinses for 30 s and plated aerobically on blood agar and Mitis Salivarius agar. Mature biofilms of S. mutans were treated with mouthrinses for 15 min followed by fluorescent vitality staining and polysaccharide measurement, followed by crystal violet staining for measurement of total biofilm remaining. Statistical analysis was performed using Kruskal–Wallis with Dunn’s multiple comparisons test comparing all mean ranks (α = 0.05). Results: TheraBreath™ Fresh Breath, Healthy Smile, and Dry Mouth exhibited no significant antibacterial activity. TheraBreath™ Healthy Gums showed antibacterial activity against S. mutans and microbes from saliva samples similar to Listerine^® Naturals at all exposure times. Whitening Fresh Breath showed intermediate killing of S. mutans after 30 min in liquid but not after 2 min or against salivary microbes. Live/Dead fluorescence vitality staining showed that Healthy Gums and Whitening Fresh Breath had antibacterial activity against mature biofilms of S. mutans statistically similar to Listerine^® Naturals and Colgate^® Total; however, Whitening Fresh Breath did not have significant killing compared to PBS. Conclusions: TheraBreath™ Healthy Gums demonstrated similar antiseptic activity levels to other antiseptic-claiming commercial rinses. Whitening Fresh Breath was comparable but unable to kill in short exposure times. Full article

Current diagnostic methods for bacterial infections in critically ill patients, including ventilator-associated pneumonia (VAP), are time-consuming, while empirical antibiotic therapy contributes to rising resistance. Bacteria-derived volatile organic compounds (VOCs) are being explored as specific biomarkers for pathogen identification and treatment monitoring. This study expands knowledge of Escherichia coli metabolism by identifying VOCs produced by both multidrug-resistant and susceptible strains, characterizing their temporal profiles during growth, and assessing VOC profile changes after imipenem exposure. Reference strains and 21 clinical isolates (derived from BAL samples of VAP patients) were cultured under controlled conditions. Headspace VOCs were preconcentrated using multibed sorption tubes and analyzed by gas chromatography–mass spectrometry (GC-MS), with compound identities confirmed using external standards. Sampling at seven time points over 24 h cultures revealed three VOC emission patterns: continuous release, temporary maximum, and compound uptake. In total, 57 VOCs were identified from the susceptible strain and 41 from the resistant one, with dimethyl disulfide, 2-butenal, ethyl acetate, and furan elevated in the resistant strain. Imipenem addition altered VOC production in the susceptible strain, with levels of six compounds elevated and seven reduced, while resistant profiles remained stable. Clinical isolates produced 71 VOCs, showing greater metabolic diversity and highlighting the relevance of isolate-derived VOCs in future studies. Full article

Background/Objectives: Coronavirus disease 2019 (COVID-19) is associated with gastrointestinal (GI) symptoms. Small intestinal bacterial overgrowth (SIBO) is emerging as a significant GI sequela post-COVID-19 infection. This study aimed to evaluate the prevalence and incidence of SIBO post-COVID-19 infection across different age groups and to identify associated risk factors in a global cohort. Methods: A retrospective study utilized the TriNetX database and included adult patients (≥18 years) diagnosed with SIBO following COVID-19 infection (1 January 2022–30 May 2024). A propensity score matching (1:1) was used to adjust for demographics and SIBO risk factors. Kaplan–Meier survival analysis assessed the SIBO incidence within 12 months. Results: Among 1,660,092 COVID-19 patients and 42,322,017 controls, SIBO was diagnosed in 353 COVID-19 patients without hydrogen breath tests (BT) and 78 with BT, compared to 3368 controls without BT and 871 with BT. Age-specific analysis demonstrated a clear, progressive increase in the SIBO incidence, becoming distinctly significant by 6 months and highest at 12 months post-infection. The highest risks were noted in ages 60–69 (0.011% vs. 0.004%, OR 2.6, p = 0.0003) and 70–79 (0.011% vs. 0.005%, OR 2.0, p = 0.0004), with younger age groups (30–49 years) also showing significantly increased risks. The medication analysis revealed strong associations with chronic opioid, proton pump inhibitor, and antidiarrheal medication. Conclusions: COVID-19 significantly increased the risk of SIBO, particularly within the first 12 months post-infection, across various age groups and, notably, in association with certain chronic medications. Clinical vigilance and targeted management strategies are recommended to mitigate long-term GI consequences. Full article

Background/Objectives: The COVID-19 pandemic highlighted the limitations of pulse oximetry in detecting occult hypoxemia. The superiority of the alveolar gas monitor (AGM) compared to pulse oximetry (SpO₂) in predicting respiratory deterioration among COVID-19-positive individuals has previously been demonstrated. Here, we combine COVID-19 and non-COVID-19 individuals as a combined cohort of participants to determine if the AGM has similar utility across a larger, more generalizable cohort. Methods: Adult patients (n = 75) at risk of respiratory deterioration in the emergency department (ED) underwent prospective assessments of their oxygen deficit (OD) and SpO₂, simultaneously measured during quiet breathing on the AGM. The OD and SpO₂ were then compared for their ability to predict the dichotomous outcome of the need for supplemental oxygen. The administration of supplemental oxygen was ordered by the clinical care team with no knowledge of the patients’ enrollment in this study. Results: In the logistic regression analysis, both SpO₂ and OD significantly predicted the need for supplemental oxygen among COVID-19-negative individuals. However, in the multivariable regression, only OD (p < 0.001) significantly predicted the need for supplemental oxygen, while SpO₂ (p = 0.05) did not in the combined cohort of COVID-19-negative and -positive individuals. Receiver operating characteristic (ROC) curve analysis demonstrated the superior discriminative ability of OD (area under ROC curve = 0.937) relative to SpO₂ (area under ROC curve = 0.888) to predict the need for supplemental oxygen. Conclusions: The noninvasive AGM, which combines the measurement of exhaled partial pressures of gas with SpO₂, outperforms SpO₂ alone in predicting the need for supplemental oxygen among individuals in the ED at risk of respiratory deterioration regardless of the etiology for their symptoms (COVID-19-positive or -negative). Full article

Volatile organic compounds (VOCs) present in human exhaled breath have emerged as promising biomarkers for non-invasive disease diagnosis. However, traditional VOC detection technology that relies on large instruments is not widely used due to high costs and cumbersome testing processes. Machine learning-assisted gas sensor arrays offer a compelling alternative by enabling the accurate identification of complex VOC mixtures through collaborative multi-sensor detection and advanced algorithmic analysis. This work systematically reviews the advanced applications of machine learning-assisted gas sensor arrays in medical diagnosis. The types and principles of sensors commonly employed for disease diagnosis are summarized, such as electrochemical, optical, and semiconductor sensors. Machine learning methods that can be used to improve the recognition ability of sensor arrays are systematically listed, including support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and principal component analysis (PCA). In addition, the research progress of sensor arrays combined with specific algorithms in the diagnosis of respiratory, metabolism and nutrition, hepatobiliary, gastrointestinal, and nervous system diseases is also discussed. Finally, we highlight current challenges associated with machine learning-assisted gas sensors and propose feasible directions for future improvement. Full article

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder characterized by a reduction or complete interruption of airflow during sleep, with episodes lasting at least 10 s. In severe cases, blood oxygen saturation can drop significantly, reaching levels as low as 40%. The aim of this study was to compare CAD/CAM-assisted maxillomandibular advancement (MMA) with traditional surgical techniques in the treatment of obstructive sleep apnea (OSA). We conducted a retrospective analysis of patients who underwent maxillomandibular advancement (MMA) for obstructive sleep apnea (OSA), all operated on consecutively by the same surgeon between 2022 and 2024 at the Maxillofacial Surgery of Policlinico Hospital San Camillo-Forlanini, Rome, Italy. This study included 18 patients with severe obstructive sleep apnea syndrome (OSAS) who underwent maxillomandibular advancement (MMA) surgery. The patients had a mean age of 38 years; 11 were male and 7 were female. Patients were divided into two groups: Group A, treated using a CAD/CAM-assisted surgical approach (five male and four female), and Group B, treated with conventional surgical techniques (six male and three female). Results: The comparison between preoperative and postoperative CT scans, along with 3D reconstructions using dedicated software, demonstrated a significant increase in airway volume following the skeletal repositioning. Notably, airway volume increased from 19.25 ± 0.5 mm³ to 26.14 ± 1.264 mm³ in group A and 20.564 ± 0.71 mm³ to 25.425 ±1.103 mm³ in group B. Conclusion: No significant differences were observed between the CAD/CAM-assisted and conventional surgical techniques for maxillomandibular advancement (MMA) in the treatment of severe obstructive sleep apnea (OSA). Both approaches led to a reduction in the apnea–hypopnea index (AHI) and an increase in posterior airway space (PAS). However, the use of software and digital planning through CAD/CAM technology allows for greater precision and shorter operative times, making the procedure more efficient overall. Full article

Background: Respiratory rate (RR) is a key vital sign and one of the most sensitive indicators of physiological conditions, playing a crucial role in the early identification of clinical deterioration. The monitoring of RR using electrocardiography (ECG) and photoplethysmography (PPG) aims to overcome limitations of traditional methods in clinical settings. Methods: The proposed approach extracts RR from ECG and PPG signals using different morphological and temporal features from publicly available datasets (iAMwell and Capnobase). The algorithm was used to develop and test with a selection of relevant ECG (e.g., R-peak, QRS area, and QRS slope) and PPG (amplitude and frequency modulation) characteristics. Results: The results show promising performance, with the ECG-derived signal using the R-peak–based method yielding the lowest error, with a mean absolute error of 0.99 breaths/min in the iAMwell dataset and 3.07 breaths/min in the Capnobase dataset. In comparison, the RR PPG-derived signal showed higher errors of 5.10 breaths/min in the iAMwell dataset and 10.66 breaths/min in the Capnobase dataset, for the FM and AM method, respectively. Bland–Altman analysis revealed a small negative bias, approximately −0.97 breaths/min for the iAMwell dataset (with limits of agreement from −2.62 to 0.95) and −1.16 breaths/min for the Capnobase dataset (limits of agreement from −3.37 to 1.10) in the intra-subject analysis. In the inter-subject analysis, the bias was −0.84 breaths/min (limits of agreement from −1.76 to 0.20) for iAMwell and −1.22 breaths/min (limits of agreement from −7.91 to 5.35) for Capnobase, indicating a slight underestimation. Conversely, the PPG-derived signal tended to overestimate RR, resulting in higher variability and reduced accuracy. These findings highlight the higher reliability of ECG-derived features for RR estimation in the analyzed datasets. Conclusion: This study suggests that the proposed approach could guide the design of cost-effective, non-invasive methods for continuous respiration monitoring, offering a reliable tool for detecting conditions like stress, anxiety, and sleep disorders. Full article

Background/Objectives: The advancing technological developments of recent decades have also changed the stress profile of pilots of high-performance aircraft (HPA) immensely. Pilots are exposed to different gravitational (G)-forces and are only able to fly with anti-G suits that compensate for the physiological loss of cerebral perfusion by applying external pressure to the body, and positive pressure breathing during G [PBG]. The present study therefore aims to investigate long-term effects of PBG on the lung capacity of fighter pilots. Methods: In a retrospective data analysis (1972–2024), the clinical findings of all German military pilots were analyzed. In total, 1838 subjects were included in the analysis, divided into three groups: HPA with PBG, HPA without PBG, and fixed-wing aircraft. Results: Lung function analysis showed that no significant decrease in FVC was found in the HPA group with PBG, but a decrease was found in the HPA group without PBG. FEV1 and FEV1/FVC decreased significantly in all groups. Multiple regression analyses indicated that the variables age and aircraft type were significant predictors of the changes in FVC and FEV1, but not for the Tiffeneau index. Conclusions: Our study showed that the lung function of HPA pilots who were exposed to both PBG and repeated increased G-forces did not deteriorate to a significantly greater extent compared with other pilots without these conditions; in some cases, it even deteriorated to a lesser extent. Overall, age has primarily been shown to be the predisposing factor for a deterioration in lung function parameters over time. Full article

High-performance musical instrument training is a demanding discipline that engages cognitive, neurological, and physical skills. Professional musicians invest substantial time and effort into mastering their repertoire and developing the muscle memory and reflexes required to perform complex works in high-stakes settings. While existing surveys have explored the use of music in therapeutic and general training contexts, there is a notable lack of work focused specifically on the needs of professional musicians and advanced instrumental practice. This topical review explores the potential of EEG-based brain–computer interface (BCI) technologies to integrate real-time feedback of biomechanic and cognitive features in advanced musical practice. Building on a conceptual framework of technology-enhanced musical practice (TEMP), we review empirical studies of broad contexts, addressing the EEG signal decoding of biomechanic and cognitive tasks that closely relates to the specified TEMP features (movement and muscle activity, posture and balance, fine motor movements and dexterity, breathing control, head and facial movement, movement intention, tempo processing, ptich recognition, and cognitive engagement), assessing their feasibility and limitations. Our analysis highlights current gaps and provides a foundation for future development of BCI-supported musical training systems to support high-performance instrumental practice. Full article

Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with multiple clinical and inflammatory phenotypes. The coexistence of bronchiectasis, known as bronchiectasis–COPD overlap (BCO), identifies a subgroup with increased morbidity and mortality. Non-invasive breath analysis using electronic noses (e-noses) has shown promise in identifying disease-specific volatile organic compound (VOC) patterns (“breathprints”). Our aim was to evaluate the ability of an e-nose to differentiate between COPD and BCO patients, and to assess its utility in detecting inflammatory endotypes (neutrophilic vs. eosinophilic). In a monocentric, prospective, real-life study, 98 patients were enrolled over nine months. Forty-two patients had radiologically confirmed BCO, while fifty-six had COPD without bronchiectasis. Exhaled breath samples were analyzed using the Cyranose 320 e-nose. Principal component analysis (PCA) and discriminant analysis were used to identify group-specific breathprints and inflammatory profiles. PCA revealed significant breathprint differences between BCO and COPD (p = 0.021). Discriminant analysis yielded an overall accuracy of 69.6% (AUC 0.768, p = 0.037). The highest classification performance (76.8%) was achieved when distinguishing eosinophilic COPD from neutrophilic BCO. These findings suggest distinct inflammatory profiles that may be captured non-invasively. E-nose technology holds potential for the non-invasive endotyping of COPD, especially in identifying neutrophilic BCO as a unique inflammatory entity. Breathomics may support early, personalized treatment strategies. Full article

Background/Objectives: Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the relationship between CS and BPDs through regression analysis. Methods: A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K ≥ 40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self-evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. Results: Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. Regression analysis revealed significant relationships between respiratory and pain variables. K-PCS demonstrated significant negative relationships with MARM area (β = −0.437, R² = 0.191) and positive relationships with SEBQ (β = 0.528, R² = 0.279). In the subgroup with BPDs, strong regression relationships were found between MARM area and NPRS usual pain (β = −0.486, R² = 0.237) and K-PCS (β = −0.605, R² = 0.366). Multiple regression analysis showed that MARM area and SEBQ together explained 41.2% of variance in pain catastrophizing. The comprehensive muscle stiffness prediction model using CSI-K, K-PCS, and muscle tone showed remarkably high explanatory power (R² = 0.978). Conclusions: In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly predictable through regression models with pain intensity and pain cognition. These quantitative regression relationships between breathing mechanics, pain measures, and muscle properties provide clinical prediction tools and suggest the importance of assessing breathing patterns in CS management. Full article

Background: Anxiety and stress are common mental health issues that affect both psychological and physiological well-being as well as quality of life. The Tibetan Singing Bowl, which combines sound and vibration, is often used in meditation and relaxation and may offer therapeutic benefits. However, current research findings are scattered and lack systematic integration and quantitative validation. Methods: This study is a systematic review that included 14 quantitative studies from the past 16 years investigating the effects of Tibetan Singing Bowl interventions on adult psychological and physiological health. Data were sourced from six major databases and supplemented through citation tracking. Inclusion criteria were adults aged 18 and over, with interventions primarily involving Tibetan Singing Bowls, and reporting quantitative outcomes related to psychological indicators (e.g., anxiety and depressive symptoms) and physiological indicators (e.g., Heart Rate Variability and brainwave activity). Study quality was assessed using Joanna Briggs Institute (JBI) criteria, and findings were synthesized narratively to identify patterns and trends. Results: Study populations included general adults, individuals with emotional distress, and patients with cancer or chronic illnesses. Interventions ranged from single sessions to multiple courses, with some incorporating breathing or other practices. Most studies reported significant reductions in anxiety and depressive symptoms, improvements in well-being and quality of life, increases in Heart Rate Variability, and decreases in heart rate. Some studies also found increased Delta and Theta brainwave activity. Due to heterogeneity in study design and limited articles, no meta-analysis was conducted. Conclusions: Tibetan Singing Bowl interventions demonstrate potential for stress reduction and psychological well-being, offering a non-invasive, low-risk, and widely accepted complementary method supporting therapeutic processes, which can be suitable for clinical and community settings. Future research should focus on rigorously designed controlled trials and consider follow-up assessments to more accurately evaluate the effectiveness of TSB interventions. Full article

Objectives: We investigated the performance of a slow computed tomography (CT) protocol to reduce alignment errors arising from motion when using CT-on-rail (CTOR) for image guidance for patients receiving thoracic stereotactic body radiation therapy (SBRT). Methods: A Quasar lung phantom with a moving tumor was programmed with three breathing rates and three motion amplitudes. MIP and average 4DCT images were used for contouring and alignment, respectively. Ten CTOR images were obtained for each of the breathing rates and amplitudes, under both CT protocols. We used in-house CAT software for image guidance, centering the tumor in the lung window within the gross tumor volume contour. Longitudinal coordinate reproducibility was compared between the two protocols. We also retrospectively analyzed CBCT SBRT image guidance alignment data from 31 patients to evaluate the systematic error in the longitudinal direction between simulation and daily treatments. Results: The mean (standard deviation) alignments (mm) for the standard and slow CT protocol ranged from 0.7 (0.68) and 1.0 (0.0), respectively, for the 28 BPM breathing rate and 5 mm amplitude combination to 5.2 (2.0) and 1.6 (0.52) for the 8 BPM breathing rate and 15 mm amplitude combination. Our retrospective analysis of patient alignment data showed a notable systematic difference in the relative bone and gross tumor volume alignment between the simulation and daily cone beam CT datasets. The mean longitudinal difference was −0.19 cm (standard deviation, 0.17 cm; range, 0.28 cm to −1.14 cm). Therefore, the position of the vertebral body cannot be used as a surrogate for mean tumor position in the longitudinal direction. Longitudinal position must be accurately determined for each patient using multiple CT images. Conclusions: A slow CT protocol improved the alignment with slower breathing rates being more challenging. A 5 mm PTV is not sufficient for tumor motion greater than 9 mm. Averaging the coordinates from multiple CTOR images is recommended. Full article