Transdiaphragmatic intercostal hernias (TDIH) are rare, and occur when abdominal viscera herniate through diaphragmatic and intercostal defects. They are often associated with trauma, but spontaneous cases (e.g., related to coughing) have also been described. We report a case of a male in his seventies with chronic obstructive pulmonary disease and obesity who developed TDIH following severe coughing due to a respiratory infection. Imaging revealed a right-sided diaphragmatic hernia with colon and small intestines extending into the thoracic cavity and between ribs. The patient underwent a successful open repair with a Gore-Tex mesh reinforcement for both diaphragmatic and intercostal defects.

Eosinophilic esophagitis (EoE) is an immune-mediated inflammatory condition of the esophagus that is characterized by esophageal dysfunction and distinct histopathologic findings of elevated eosinophils in the esophageal mucosa. EoE can present in the aerodigestive patient with a variety of symptoms ranging from feeding difficulty to extraesophageal symptoms such as chronic cough. Routine evaluation for EoE should be performed in high-risk patients such as esophageal atresia/tracheoesophageal fistula and should be considered for those undergoing evaluation for laryngotracheal reconstruction surgery. When selecting treatments for EoE in aerodigestive patients, the clinician should consider limitations such as ability to take oral treatments and other nutritional considerations in the management of these fragile patients. Long-term monitoring of aerodigestive patients with EoE should include assessment of growth, feeding, nutrition, and follow-up for resolution of the primary presenting symptoms often requires continued involvement of the multidisciplinary team which includes a dietitian and feeding therapist.

Ataxia-telangiectasia is a rare, autosomal recessive primary immunodeficiency caused by mutations in the ATM gene on chromosome 11, which encodes a serine-threonine kinase essential for the recognition and repair of DNA double-strand breaks. The disease is characterized by progressive neurological impairment, immunological dysfunction, and an increased susceptibility to recurrent infections and malignancies. Pulmonary involvement represents a major source of morbidity and frequently arises from chronic infections, aspiration, and impaired airway clearance, ultimately leading to the development of bronchiectasis. The case of a 15-year-old adolescent with a history of recurrent aspiration pneumonias, neuropsychomotor developmental delay, and severe malnutrition is reported, who was admitted for evaluation of chronic productive cough, fever, and dysphagia. Comprehensive clinical assessment and ancillary investigations revealed recurrent respiratory infections, gastroesophageal reflux, and typical features of ataxia-telangiectasia, including cerebellar ataxia, oculomotor apraxia, and conjunctival telangiectasias. Additionally, bronchiectasis was identified as a secondary consequence of the underlying neurological and immunological impairment. This case highlights the diagnostic challenges posed by ataxia-telangiectasia in pediatric patients presenting with chronic respiratory symptoms and emphasizes the importance of early recognition of the underlying systemic disorder. A multidisciplinary approach is essential for accurate diagnosis and optimized management, aiming to address both the primary disease and its pulmonary complications.

Occupational lung diseases remain a significant public health concern in mining populations, particularly in high-exposure environments. This study examined occupational exposure profiles and respiratory health outcomes among surface and underground miners in Mpumalanga Province. A cross-sectional analytical design was employed among 239 mine workers. Data on socio-demographic characteristics, occupational exposures, behavioural factors, and respiratory outcomes were analysed using descriptive statistics, chi-square tests, and logistic regression models. Underground miners were significantly more likely to report high dust exposure (44.9% vs. 24.1%), poor ventilation (60.6% vs. 39.3%), and longer working hours (>8 h: 68.5% vs. 50.0%) compared to surface miners. They also reported a higher prevalence of respiratory symptoms, including chronic cough (45.7% vs. 25.9%), shortness of breath (41.7% vs. 23.2%), wheezing (34.6% vs. 18.8%), and diagnosed lung disease (23.6% vs. 9.8%). Multivariable analysis showed that underground mining (AOR = 1.92; 95% CI: 1.08-3.41), smoking (AOR = 1.78; 95% CI: 1.02-3.11), and high dust exposure (AOR = 2.89; 95% CI: 1.45-5.76) were independent predictors of chronic cough. A significant interaction between smoking and underground mining (AOR = 2.74; 95% CI: 1.32-5.68) further amplified respiratory risk. Additionally, underground miners demonstrated lower levels of knowledge (48.8% vs. 66.1%) and poorer preventive practices (44.1% vs. 64.3%). These findings highlight the combined influence of occupational and behavioural factors on respiratory health and highlight the need for integrated interventions to reduce the burden of occupational lung diseases.

Background: Respiratory diseases (RDs), including asthma, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, remain a major global health challenge, contributing substantially to global morbidity and mortality. Conventional diagnosis relies heavily on clinicians' expertise to interpret respiratory sounds and radiographic images, a process that can be subjective, time-consuming, and prone to inter-observer variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled automated diagnostic approaches that can improve the efficiency, consistency, and scalability of respiratory disease detection. However, existing research remains fragmented across different data modalities. Methods: This review systematically analyzes recent studies on AI-based respiratory disease detection using both visual modalities (e.g., chest X-rays, computed tomography (CT) scans, and ultrasound) and audio modalities (e.g., cough and breath sounds). To provide a comprehensive perspective, the reviewed literature is organized using a unified taxonomy that categorizes existing approaches into three main groups: audio-based, visual-based, and audio-visual-based methods. In addition, two conceptual frameworks are proposed to illustrate representative pipelines for audio-based and visual-based respiratory disease classification. Results: The analysis reveals that most existing studies focus on single-modality approaches, while multimodal integration remains relatively underexplored. Only a limited number of studies combine audio and visual data within unified frameworks, primarily due to the scarcity of synchronized multimodal datasets collected from the same patients. The proposed taxonomy and conceptual frameworks provide a structured basis for comparing existing methods, identifying methodological trends, and highlighting key research gaps in multimodal respiratory disease detection. Conclusions: Future research should prioritize the development of multimodal datasets, robust evaluation protocols, and interpretable and lightweight AI models suitable for real-world clinical deployment. Advancing multimodal integration has the potential to significantly enhance the accuracy, reliability, and clinical applicability of AI-driven respiratory disease diagnosis systems.

Street-food grilling is a common occupation in Asia, yet the occupational health risks associated with cooking-generated polycyclic aromatic hydrocarbons (PAHs) exposure, occurring alongside plausible unmeasured co-exposures such as ambient heat and physical workload, remain under-researched. This study investigated the internal dose of PAH exposure and its association with early biological effects and physiological strain among grill restaurant workers. A cross-sectional study was conducted involving grill workers and 20 age/BMI-matched controls. Urinary 1-hydroxypyrene (1-OHP) was utilized as the primary exposure biomarker. The study assessed early biological effects such as oxidative stress (8-OHdG, F2-isoprostanes), lung epithelial integrity (CC16), and genotoxicity (BPDE-DNA adducts) via ELISA. Physiological parameters, including blood pressure and heart rate, were recorded to evaluate acute cardiovascular strain. Workers had significantly elevated urinary 1-OHP levels compared to controls (Hodges-Lehmann ratio = 3.66, 95% CI: 1.68-7.12, representing a 3.7-fold median increase), with exposure levels increasing proportionally to smoke proximity. Notably, workers demonstrated a significantly higher median resting heart rate (HL ratio = 1.13, 95% CI: 1.05-1.23; +12.9%) and systolic blood pressure (HL ratio = 1.09, 95% CI: 1.00-1.18; +8.9%) compared to their office-based peers. Although strong correlations were observed among biological effect biomarkers (rs = 0.42-0.63), there were no significant differences between groups for 8-OHdG, CC16, or BPDE-DNA adducts, suggesting that cardiovascular parameters reflect acute short-term responses, while genomic damage markers may require higher cumulative exposure thresholds to become detectable. The study revealed that grill restaurant workers face substantial internal PAH exposure and significant cardiovascular strain, occurring alongside plausible unmeasured co-exposures including ambient heat and physical workload. The prevalence of chronic cough and elevated heart rate is a critical early warning sign for occupational health. Our findings indicate that current general ventilation is inadequate, highlighting an urgent need for localized engineering controls and comprehensive health surveillance, including cardiovascular monitoring in the service sector.

Background/Objective: To identify respiratory pathology, automated cough counting is frequently proposed. A trial validating an early warning system for exacerbations in chronic obstructive pulmonary disease (COPD) patients was recently concluded successfully. This paper aims to review the critical design choices for converting a cough counter into a patient-friendly continual cough monitor. Furthermore, it provides a basis for a practical reliability metric for continual cough monitoring. Methods: Design choices made in the development of a cough-based alert mechanism called XACT are discussed. A practical approach for reliability assessment is outlined based on cough counts, day-to-day variation and specificity data. Results: In post hoc analysis, it is shown that the described approach enables differentiation between high-quality cough estimates and less reliable data. The approach is used to underpin an earlier cohort subdivision into patients with and without increased cough during exacerbation. Conclusions: The validated alert mechanism has various patient-oriented design choices (unobtrusiveness, privacy-preserving). The examples illustrate how to screen for potential issues in automated cough count data without resorting to laborious annotation. It creates a practical basis for confidence metrics of medical inferences made from cough data, e.g., exacerbation forecasts. The proposed concepts need further validation.

Background  Idiopathic subglottic stenosis is a rare condition characterized by a constriction of the airway passage without a known cause. While there may be many contributing factors, no definitive etiology has been established. Due to its rarity, this condition is frequently overlooked by healthcare providers, resulting in an inaccurate and lengthy diagnosis process. This results in detrimental effects for the patient given the symptoms of progressive dyspnea. Our objective was to retrospectively review patients diagnosed with idiopathic subglottic stenosis and analyze key characteristics about each patient. By examining these cases in conjunction with the current literature, we hope to raise awareness of idiopathic subglottic stenosis and support an earlier accurate diagnosis. In particular, female patients with audible turbulent air flow on exam and a lack of response to bronchodilators should be promptly considered for a diagnosis of subglottic stenosis.  Methods  This study retrospectively reviews 10 patients, 18 years or older, diagnosed with idiopathic subglottic stenosis at an otolaryngology clinic in Kansas. Patients were evaluated first with a physical examination that included stroboscopy and flow volume loops. In addition, key variables analyzed included patients' age, gender, other diagnosis/treatment tried, length of time until the stenosis was diagnosed, base peak expiratory flow, symptoms, observed signs, treatment, and improvement after treatment. The data from these variables were then compiled into a table, and mean values were computed.  Results In this study, all patients were Caucasian females between the ages of 26 and 78 years old, with a mean age of 46.7 years old. Ninety percent of the patients had been diagnosed previously with either asthma or chronic obstructive pulmonary disease (COPD) and were treated with corticosteroids as a result. The average time it took for these patients to be diagnosed from the onset of symptoms was approximately 36.8 months. The average base peak expiratory flow, gathered from pulmonary function testing, was 47% of the predicted value. They presented with a mixture of symptoms including dyspnea with exertion or at rest, stridor, a chronic cough, and dysphonia. In the office, the observed sign of turbulent airflow at rest was detected in nine out of the 10 patients. All patients underwent a biopsy, followed by laser excision of stenosis, steroid injection, and balloon dilation. All reported subjective relief in symptoms and, where available, showed objective improvement in pulmonary function testing and endoscopic appearance after the treatment.  Conclusions  A thorough history, a careful physical examination, pulmonary function testing, and visualization with endoscopy can significantly expedite the diagnosis of idiopathic subglottic stenosis. In addition, listening for abnormal upper airway sounds is crucial in guiding clinicians toward this diagnosis quickly.

Cough is a common and physiologically informative component of respiratory morbidity, but its potential for diagnosing and monitoring disease is not thoroughly investigated. This systematic review synthesised the literature on algorithmic and statistical models analysing cough acoustics for diagnosing or monitoring respiratory conditions. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, five databases (PubMed, Embase, Scopus, Web of Science and CENTRAL) were systematically searched for studies published from January 2010 to June 2025. Eligible studies performed quantitative acoustic feature analysis of human coughs using statistical, machine learning or deep learning models and reported diagnostic or prognostic performance. 89 studies from 34 countries were assessed, covering cough detection (n=31), disease classification (n=55) and disease severity prediction (n=3), reflecting potential applications in disease monitoring. Deep learning approaches, especially convolutional and recurrent networks, were predominant (n=56) and tended to achieve the higher accuracies, although machine learning ensemble methods and logistic regression also demonstrated strong performance, particularly with well-engineered features. Across different diseases, sensitivities and specificities were often reported to be ≥90%, notably for tuberculosis, asthma and COVID-19. However, methodological weaknesses were common, with only 11.2% of studies introducing external validation, 71.1-87.6% demonstrating high risk of bias (according to PROBAST-AI) and most based on small, homogeneous or crowdsourced cohorts with limited generalisability. These limitations contribute to inflated internal performance and uncertainty about real-world applicability. Cough acoustic biomarkers hold promise as an adjunctive tool for screening and longitudinal monitoring in low-resource environments. Nevertheless, widespread implementation will require large, multicentre validation, standardised calibration, bias control and incorporation into privacy-preserving workflows.