Search Results (246)

Over the past two decades, interest in prone-position whole breast irradiation (WBI) as an effective and practical alternative to supine treatment has been growing a lot. Although solid scientific data has provided evidence of substantial dosimetric benefit with decreased toxicity, there is still conflict in the radiotherapy community over whether to adopt prone-position WBI as a valid alternative to supine radiotherapy (RT) in routine clinical practice. A large number of prone trials have been conducted to assess and address concerns related to prone treatment in large and pendulous breasts and in left and right breast cancer (BC), nodal irradiation, and its reproducibility with deep inspiration breath hold (DIBH) delivery with photons or protons. Appropriate atlases have been defined to improve prone nodal irradiation. Additionally, more comfortable customized immobilization couches have been constructed to permit IMRT beams and VMAT arrangements with modern LINACs. Although our search in literature databases shows a growing body of evidence from the past two decades on this issue, prone WBI is still underused. Given the paradox of the advances and benefits of this positioning and the lack of drive in the radiotherapy community towards its clinical implementation, the purpose of this comprehensive review is to evaluate the true advantages of this position in real life and contextualize it in scenarios like large breasts, left-sided breast cancer, and nodal irradiation to encourage its implementation in clinical practice. Full article

Internal Boxing (neijiaquan 內家拳) is an advanced form of the Daoist gymnastic exercise of daoyin (導引). However, how it achieves a paradigmatic shift from qi/blood regulation to deep-level transmutation of sexual energy still requires further exploration. Therefore, it is of great significance to look into how Internal Boxing inheres and integrates various techniques of “desire transmutation” (zhuanyu 轉欲) from internal alchemy (neidan 內丹), thereby transcending traditional daoyin, bringing about a significant systematic reconfiguration in the model of body cultivation practices in Daoist medicine. The traditional daoyin (i.e., “guiding and stretching”) practice emphasizes the regulation of qi/blood, but it remains limited in accounting for and producing the self-conscious transmutation of sexual energy. In contrast, Internal alchemy provides a different system of theory and techniques, which is centered on the concept of “transmutation of desires”, converting human desires into high-level life energy through a process of interaction between one’s internal spirit (xinshen 心神) and internal breathing (neixi 內息). This study thus examines the ways in which Internal Boxing integrates and reconfigures these techniques within its bodily training regimen. In the core of all these styles is the goal to refine the primordial essence (yuanjing 元精) by transitioning the method to induce the flow of vital energy from breathing to somatic movements. As a result, this study shows that the innovations of Internal Boxing reconfigure the qi/blood regulation model in the traditional daoyin practice, causing a systematic reconfiguration in the transmutation of sexual energy and, further, bridging the gap between daoyin and internal alchemy in both theory and practice. Furthermore, such innovations also develop a holistic view of the human body as marked by an emphasis on the “unity of pre-heaven (xiantian 先天) and post-heaven (houtian 後天) states”, which expands in both depth and breadth the theories of body cultivation practices in Daoist medicine. Full article

Mediastinal radiotherapy plays a central role in the treatment of several malignancies, particularly Hodgkin lymphoma and breast cancer. However, exposure to thoracic radiation is associated with long-term cardiovascular complications, among which valvular heart disease (VHD) is increasingly recognized. Radiation-induced VHD typically presents after a latency period of 10–20 years and is characterized by progressive valve fibrosis, thickening, and calcification, most commonly affecting the left-sided valves. Management of radiation-induced VHD generally follows standard guidelines but remains challenging due to extensive calcification and coexisting radiation-related cardiac or pulmonary injury. A history of thoracic radiotherapy is associated with increased perioperative risk and may negatively impact surgical outcomes, which often alters the risk–benefit balance and favors less invasive therapeutic approaches. Advances in the transcatheter approach have expanded treatment options for this high-risk population; however, data on long-term outcomes remain limited. Evolving dose-reduction techniques, such as deep-inspiration breath-hold, intensity-modulated radiotherapy, and proton therapy, together with predictive dosimetric models, aim to minimize future cardiac toxicity. Given the delayed onset and progressive nature of radiation-associated VHD, structured long-term surveillance is essential to enable early detection and timely intervention in cancer survivors at risk. Full article

This study investigates the impact of roadside building development and vehicle exhaust emissions on atmospheric deterioration in urban highway areas. By integrating satellite-based building coverage data with an equal-pollution vehicle conversion method (based on human toxicity potential), we establish a computational fluid dynamics framework to simulate pollutant dispersion. Key results reveal the following: (1) Street canyon morphology, particularly its geometric symmetry, dominates diffusion patterns. Wide canyons (aspect ratio = 3.3) reduce CO accumulation by over 30% compared to deep canyons (aspect ratio = 0.3), highlighting the role of built form in regulating pollution distribution. (2) Under idealized conditions, photocatalytic pavement mitigates pollutant concentrations at human breathing height by 28.7–56.7%, demonstrating the potential of uniformly applied material solutions. These findings provide a validated theoretical basis for optimizing urban road design and evaluating environmental policies, with considerations for spatial layout and material treatment. Full article

Introduction: Malocclusion and dysfunctional or atypical swallowing are two conditions that significantly affect the health and well-being of the stomatognathic system, so much so that they often interact, influencing each other, and the presence of one can cause the onset or aggravation of the other. In this regard, over the years studies have been carried out that tried to discover the correlation between atypical swallowing and malocclusion. The aim is to evaluate the prevalence of dysfunctional swallowing in patients with malocclusion, to examine the pathophysiological mechanisms linking malocclusion and dysfunctional swallowing, and above all to investigate what potential risk factors may be. Materials and Methods: A sample of 60 patients aged between 6 and 16 years was analyzed at the Department of Dentistry of the University of L’Aquila. Some characteristics of the subjects’ face and posture were analyzed both from a frontal and lateral point of view. An orthodontic, temporomandibular joint, and masticatory muscle diagnosis was made. In addition, an examination of oral structures and functions was performed that allowed breathing, swallowing, chewing, and phono-articulation to be assessed. Results: It was observed that all the children had atypical swallowing, with significant postural abnormalities of the tongue; in fact, only 5% had a correct posture of the tongue at rest. In the analysis of occlusal characteristics, it emerged that with regard to the transverse plane, 21.67% of subjects have a condition of No Cross, while 10% show a Unilateral Cross. Finally, 68.33% show a Bilateral Cross. As far as the anterior–posterior plane is concerned, most of the subjects, equal to 76.67%, are placed in Class I, while 23.33% are in Class II. Finally, in relation to the vertical plane, 63.33% of subjects have normal occlusion, while 25% suffer from deep bite and 11.67% from open bite. The sample, stratified by presence or absence of alerts, shows significant differences for atypical swallowing (p = 0.031), for the presence of Class II malocclusion (p = 0.002), for low lingual posture, (p < 0.001), and for labial incompetence (p = 0.001). The multivariate logistic regression model showed that the presence of atypical swallowing (OR 1.04, 95% CI 1.04–1.07, p = 0.029), open bite malocclusion (OR 1.09, 95% CI 1.01–1.18, p = 0.013), low lingual posture (OR 1.11, 95% CI 1.04–1.18, p = 0.002), and the presence of labial incompetence (OR 1.06, 95% CI 1.02–1.10, p = 0.029) were significant clinical risk factors independently associated with the presence of alerts. Conclusions: The data collected confirm that atypical swallowing is a key element in the development of malocclusions, with a strong impact on posterior crossbite, anterior overjet, and other occlusal discrepancies. Among the data collected in the diagnostic phase, patients who presented at least one significant alert were also considered and atypical swallowing, low lingual posture, open bite malocclusion, and the presence of labial incompetence were statistically significant. Full article

This study aims to monitor mask performance in operando using all-printed humidity sensor arrays based on BiFeO₃/BiOCl heterostructures. Two screen-printed 19-sensor arrays are fixed directly atop the mask, in order to analyze moisture levels in exhaled breath and extract performance indicators. This approach allows for an examination of the humidity saturation and absorption over time during operation. Accumulation of moisture within the mask can affect its performance, and factors like breath humidity, mask material, and ambient conditions influence this. Results show that the measured data follows an exponential decay, achieving correlation factors of over 0.9 for all tests. We also detect breathing differences through feature extraction, investigating the respiration rates and signal amplitudes for both normal and deep breathing. Furthermore, we animated the airflow in the mask in both 2D and 3D, allowing for the eventual detection of leaks for ill-fitting masks. This study introduces an innovative approach for the assessment of mask fit and longevity, contributing to improving mask efficacy and public health outcomes. Full article

Maritime disasters pose substantial social and economic challenges and often require complex, resource-intensive search and rescue operations to minimize loss of life and damage to infrastructure. This study proposes a sustainable and quantitative framework for planning and managing underwater search and rescue operations in strong tidal current environments, with reference to the Sewol ferry disaster. Hydrodynamic current predictions over a 31-day period were analyzed to determine tidal-induced diving cycles and to estimate the depth-specific diveable time (DAT) under safe operating limits of 1 knot for a self-contained underwater breathing apparatus (SCUBA) and 1.5 knots for surface-supplied diving systems (SSDSs). Two representative dive profiles were developed: a no-decompression SCUBA plan for 26 m hull diving and a staged-decompression SSDS plan for 48 m seabed diving, considering oxygen toxicity and nitrogen narcosis limits. Workable time (WAT) analysis indicated SCUBA as optimal for hull tasks (WAT/DAT = 0.83), whereas the SSDS provided extended efficiency for deep-water operations. A redeployment model based on surface interval constraints reduced diver staffing requirements by approximately 28%. The proposed framework enhances the sustainability and resilience of marine disaster response by optimizing diver safety, operational efficiency, and resource management, contributing to sustainable marine safety systems and long-term emergency preparedness. Full article

Snoring, a common sleep-disordered breathing phenomenon, impairs sleep quality for both the sufferer and any bed partner. While mild snoring primarily disrupts sleep continuity, severe cases often indicate obstructive sleep apnea (OSA), a disorder affecting 9–17% of the global population, linked to significant comorbidities and socioeconomic burden (see Introduction for supporting data). Here, we propose a low-cost, real-time snoring detection and intervention system that integrates a multiple-spectrum deep learning framework with an Internet of Things (IoT)-enabled smart pillow. The modified Parallel Convolutional Spatiotemporal Network (PCSN) combines three parallel convolutional neural network (CNN) branches processing Constant-Q Transform (CQT), Synchrosqueezing Wavelet Transform (SWT), and Hilbert–Huang Transform (HHT) features with a Long Short-Term Memory (LSTM) network to capture spatial and temporal characteristics of sounds associated with snoring. The smart pillow prototype incorporates two Micro-Electro-Mechanical System (MEMS) microphones, an ESP8266 off-shelf board, a speaker, and two vibration motors for real-time audio acquisition, cloud-based processing via Arduino cloud, and closed-loop haptic/audio feedback that encourages positional changes without fully awakening the snorers. Experiments demonstrated that the modified PCSN model achieves 98.33% accuracy, 99.29% sensitivity, 98.34% specificity, 98.3% recall, and 98.32% F1-score, outperforming existing systems. Hardware costs are under USD 8 and a smartphone app provides authorized users with real-time visualization and secure data access. This solution offers a cost-effective and accurate approach for home-based OSA screening and intervention. Full article

Purpose: This study aimed to compare QoL outcomes among patients undergoing active breathing control (ABC), voluntary deep inspiration breath hold (vDIBH), and surface-guided radiation therapy (SGRT). Methods: This was a non-randomized, three-arm clinical trial in which 55 patients were sequentially allocated to ABC (n = 19), SGRT (n = 20), or vDIBH (n = 16). QoL was assessed using the European Organization for Research and Treatment of Cancer QoL questionnaire (EORTC QLQ-C30) at baseline, treatment completion, and 6–8 weeks post-treatment. Linear regression was used to compare changed scales in QoL domains across groups. A p-value of <0.05 was considered statistically significant. Results: Baseline QoL scores were high across all groups, with physical functioning being the highest-rated domain and global health status the lowest. Fatigue, pain, and insomnia were the most highly reported symptoms at all time points. At 6–8 weeks, social functioning improved significantly in SGRT compared to vDIBH (16.67 vs. −12.50, p = 0.0053). Patients in the vDIBH group reported significantly increased pain compared to ABC at 6–8 weeks (p = 0.0240). No other significant differences were observed in QoL changes between the groups. Conclusions: The three breath-hold techniques maintained overall QoL with no differences between the groups, except for pain between vDIBH and ABC and social functioning for vDIBH and SGRT both at 6–8 weeks of follow-up. Despite the limitations of this study, each breath-hold technique has demonstrated comparable impact on QoL in patients with left-sided breast cancer and each could be used as a viable option with respect to QoL. Full article

Background/Objectives: Cardiac magnetic resonance (CMR) imaging is a key tool for diagnosing cardiovascular disease, but its analysis remains time-consuming and dependent on expert interpretation, which can limit throughput and reproducibility. To address these challenges, we aim to develop an automated solution that streamlines CMR post-processing, enabling consistent, rapid, and quantitative assessment of cardiac structures and myocardial pathology. Methods: We introduce SPOT-Cardio, an AI-powered imaging analysis toolbox based on a 2D breath-held late gadolinium enhancement (LGE) imaging technology: SPOT. This acquisition combines BR- and BL-LGE images in a single scan, allowing simultaneous capture of high-contrast scar information and detailed cardiac anatomy. Using the resulting CMR images, deep learning models (based on 2D U-Net or MedFormer) were trained to segment cardiac structures and myocardial scars. The trained models and associated image-processing algorithms were then integrated into the open-source medInria platform and specifically within its cardiac-focused MUSICardio application. Results: SPOT-Cardio enables automatic segmentation of cardiac structures and myocardial scars, performs landmark-based regional localization, and extracts key biomarkers such as scar volume, extent, and transmurality. The resulting quantitative measures are presented in standardized bullseye plots accompanied by detailed clinical reports. Conclusions: With a one-click workflow and intuitive visualization, SPOT-Cardio reduces manual workload and supports more accessible and consistent cardiovascular assessment. By integrating advanced image acquisition with AI-based automation, it provides a practical and efficient solution for streamlined and quantitative CMR analysis. Full article

Fear of needles is common among child patients. It causes stress and can lead to difficulty in procedures and future treatment avoidance. Virtual reality (VR) has emerged as a promising tool to reduce pain and anxiety non-pharmacologically. However, a research gap exists regarding what VR content is most effective in decreasing periprocedural stress. This article reports a VR feasibility study conducted with 83 child patients aged 8–12 years during a cannulation procedure. It has a between-subjects design with four groups, comparing deep breathing and mindfulness-based relaxation in a virtual nature environment (VNE) to passive VNE and standard care. The results from both relaxation exercise groups have been previously reported. This follow-up article adds findings from passive VNE and control groups, comparing all four for effectiveness and patient experience. The key findings highlight that deep breathing was highly effective according to heart rate variability (HRV) data, but less enjoyable than the mindfulness-based relaxation, which achieved higher patient satisfaction but was less effective according to HRV. Passive VNEs were pleasant but did not cause measurable stress reduction. All VR interventions improved patient experience over standard care. Relaxation exercises in a VNE reduce periprocedural stress more efficiently than passive VNEs or standard care in pediatrics. Full article

Background: Adults with congenital heart disease (CHD) are living longer but face increasing comorbidities. Sleep is a key health determinant, yet objective data in CHD remain limited. This study compared sleep characteristics of adults with CHD and controls using wearable technology and a Health Application Programming Interface (API). Methods: A total of 175 CHD patients (33.1 ± 10.3 years, 49.2% women) and 52 controls (34.4 ± 12.4 years, 40.4% women) completed seven continuous days of wrist-worn Garmin Vivosmart^® 5 during routine follow-up at the TUM Klinikum Deutsches Herzzentrum. Sleep duration, phases, Sleep Scores, and weekday-weekend differences were analyzed, and multivariate models examined clinical and demographic predictors. Results: Total sleep duration and rapid eye movement (REM) sleep did not differ between groups. CHD patients had more deep sleep (83 ± 19 vs. 75 ± 16 min, p = 0.004) but lower Sleep Scores (74 ± 9 vs. 77 ± 9, p = 0.041). Within CHDs, deep sleep was higher on weekends than on weekdays (p = 0.033). Multivariate analyses showed no overall group effect, but age (p = 0.016), sex (p = 0.013), and body mass index (BMI; p < 0.001) significantly predicted sleep outcomes. Regression analyses in CHDs revealed female sex associated with longer REM sleep (p < 0.001), while higher BMI consistently predicted poorer outcomes. Disease severity was linked to lower Sleep Scores. Conclusions: Sleep in CHDs is broadly comparable to controls, but BMI, sex, and disease severity significantly shape outcomes. The additional variability between weekends and weekdays and a higher risk of sleep-disordered breathing, according to the literature, underscores that sleep is an underestimated target for prevention and clinical care in CHD. Full article

Pelvic floor muscles (PFMs) in women play a key role, and their proper functioning depends on the coordinated interaction with other anatomical structures, particularly the diaphragm and deep abdominal muscles, which together constitute the so-called core stabilizing unit. The aim of this study was to evaluate the effects of diaphragmatic breathing therapy on pelvic floor muscle function and stress levels in healthy women. The randomized, controlled, parallel-group trial (allocation 1:1) included 42 women aged 21–30 years who met the inclusion and exclusion criteria. The experimental group received diaphragmatic breathing therapy. The following assessment tools were used: Surface Electromyography (sEMG), the Sexual Satisfaction Questionnaire in Close Relationships (KSS) by M. Plopa, and the Perception of Stress Questionnaire (KPS) by M. Plopa and R. Makarowski. In the experimental group, a significant reduction in resting PFM activity was observed in the final stage of the measurement protocol, along with a tendency toward decreased activity during relaxation phases. A trend toward increased amplitude during phasic and tonic contractions was also noted, more pronounced after therapy than in the control group, although not statistically significant. No significant associations between stress dimensions and sexual satisfaction were found in the control group, whereas in the experimental group, higher worry, reduced sense of meaning, low agency and pessimism correlated with lower sexual satisfaction and difficulties achieving orgasm. These findings suggest that diaphragmatic breathing therapy may reduce resting pelvic floor muscle activity and perceived emotional stress. Full article

Biomedical signal analysis underpins modern healthcare by enabling accurate diagnosis, continuous physiological monitoring, and informed patient management. While deep learning excels at automated feature extraction and end-to-end modeling, classical ML remains essential for tasks requiring interpretability, data efficiency, and clinical transparency. This review synthesizes advances in ML methods including Support Vector Machines, Random Forests, and Decision Trees focusing on physiologically informed feature engineering, robust feature selection, and meaningful model interpretation. We provide guidelines for signal preprocessing, domain-specific feature extraction, and selection strategies across standard biomedical signals such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), Electrovestibulography (EVestG), and tracheal breathing sounds (TBSs). Reviewing TBS studies illustrates an end-to-end workflow highlighting common features and classifiers alongside practical challenges and solutions. Reported ML application performance ranges from 85 to 94% accuracy for EEG, ECG, and EMG, to 82% specificity for TBSs, emphasizing the trade-off between interpretability and predictive performance. Marginal accuracy gains alone do not constitute meaningful progress unless they enhance clinical insight, actionable decision-making, or model transparency. Finally, we compare ML with DL, discuss strengths and limitations, and provide recommendations and future directions for developing robust, interpretable, and clinically relevant biomedical ML. Full article

Breath analysis is a non-invasive diagnostic method that offers insights into both physiological and pathological conditions. Exhaled breath contains volatile organic compounds, which act as biomarkers for disease detection, allowing for the monitoring of treatments and the tailoring of medicine to individuals. Recent advancements in chemical sensing, mass spectrometry, and spectroscopy have improved the ability to identify these biomarkers; however, traditional statistical approaches often struggle to handle the complexities of breath data. Artificial intelligence (AI) and machine learning (ML) have revolutionized breath analysis by uncovering intricate patterns among volatile breath markers, enhancing diagnostic precision, and facilitating real-time disease identification. Despite significant progress, challenges remain, including issues with data standardization, model interpretability, and the necessity for extensive and varied datasets. This study reviews the applications of ML in analyzing breath volatile organic compounds, highlighting methodological shortcomings and obstacles to clinical validation. A thorough literature review was performed using the PubMed and Scopus databases, which included studies that focused specifically on the role of machine learning in disease diagnosis and incidence prediction via breath analysis. Among the 524 articles reviewed, 97 satisfied the specified inclusion criteria. The selected studies applied ML techniques, fell within the scope of this review, and emphasize the potential of ML models for non-invasive diagnostics. The findings indicate that traditional ML methods dominate, while ensemble methods are on the rise, and deep learning (DL) techniques (especially CNNs and LSTMs) are increasingly used for classifying respiratory diseases. Techniques for feature selection (such as PCA and ML-based methods) were frequently implemented, though challenges related to explainability and data standardization persist. Future studies should focus on enhancing model transparency and developing methods to further integrate AI into the clinical setting to facilitate early disease detection and advance precision medicine. Full article