Persistent chronic cough is a common outpatient symptom, typically caused by gastroesophageal reflux, asthma, or upper airway cough syndrome. Rare causes, including metastatic tumors, may present with nonspecific respiratory symptoms, particularly in patients with a history of malignancy. A 59-year-old man with a prior left frontal grade II solitary fibrous tumor (SFT) resected in 2012 presented with a 2-3-month history of dry cough. Initial empiric therapy for gastroesophageal reflux and post-nasal drip failed. Chest radiograph revealed multiple bilateral pulmonary nodules, and CT chest showed an 8 × 4.5 cm subcarinal mass. Bronchoscopy with transbronchial needle aspiration confirmed metastatic pulmonary SFT. The patient is now being followed by oncology for staging and treatment planning. Persistent cough that does not improve with empiric therapy warrants early imaging, especially in patients with prior malignancy. This case highlights that SFT can metastasize many years after initial treatment, presenting with common symptoms such as cough. Timely reassessment can prevent delays in diagnosis and management.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-responsive cough (CRC) accounts for the majority of the causes of chronic cough, and the diagnostic process is relatively complicated. To develop a predictive model for diagnosis of CRC, which is based on readily available clinical information. A retrospective dataset of 304 cases was used for training, and a prospective dataset of 131 cases was used for temporal single-center validation. Candidate predictors were screened via univariate analysis and refined using LASSO regression. A final multivariable model was constructed through stepwise logistic regression and presented as a clinical nomogram. The model's performance was rigorously evaluated in terms of discrimination (AUC-ROC), calibration (calibration curve), and clinical utility (decision curve analysis). The robustness of the model predictors was further supported by their consistency with the key variables identified by an optimal machine learning model. Of 20 candidate variables based on patient basic information and test results, 7 variables were selected as optimal predictors to establish a CRC prediction model for chronic cough, including reflux symptoms, history of allergic diseases, blood eosinophils, IgE, CRP, FEV1/FVC, and FeNO50. The model demonstrated good discrimination and calibration in both the training set (AUC = 0.900 [0.865,0.935], MAE = 0.017) and the validation set (AUC = 0.840 [0.771,0.909], MAE = 0.046). The results of decision curve analysis revealed that using our models to predict CRC in chronic cough would add more benefit than either the treat-all scheme or the treat-none scheme. The variable selection for the nomogram was further validated by the high degree of agreement with the feature importance rankings derived from the top-performing machine learning model (Random Forest). Our study has developed a nomogram model, along with a simplified version, based on easily accessible clinical information and pulmonary function tests. The model demonstrates strong predictive performance and good calibration, providing clinicians with a rapid aid for initiating targeted treatment.

Chronic pulmonary aspergillosis is an uncommon form of Aspergillus infection. It typically occurs in people with a weakened immune system and underlying lung disease. Our case demonstrates that pulmonary aspergillosis can occur in immunocompetent middle-aged men with a rare pleural effusion as the main imaging manifestation. A 45-year-old Chinese man with chronic exposure to wet crops presented to our emergency department with a two-year history of cough which worsened over the past 10 days. An initial chest CT showed right pleural effusion with compressive pulmonary atelectasis. Bronchoscopy was unremarkable, but next-generation sequencing of bronchoalveolar-lavage fluid revealed Aspergillus fumigatus. Chronic pulmonary aspergillosis was diagnosed based on typical personal contact history, clinical manifestations and microbiological results. The patient responded well to voriconazole and was discharged 20 days later with oral voriconazole medicine. The case illustrates that bloody pleural effusion could be a rare condition of chronic pulmonary aspergillosis.

Stelleropsis tianschanica Pobed. is a perennial herb belonging to the genus Stelleropsis in the family Thymelaeaceae of the order Myrtales, and possesses certain toxicity. In the Zhaosu region of Xinjiang, it is used medicinally as an expectorant and analgesic, and for the treatment of chronic bronchitis, cough, asthma, and skin diseases. However, beyond its therapeutic efficacy, the potential hepatotoxicity of Stelleropsis tianschanica Pobed. poses a threat to human health. Currently, research regarding its hepatotoxicity remains limited.

Occupational risk factors associated with traditional kiln technology are major drivers of respiratory symptoms and illnesses. The current research was designed to evaluate prevalence of selected disease and respiratory symptoms among workers at two types of brick kilns. A proportionate stratified random-sampling-based survey was conducted during 2024-2025; a total of 250 adult men were assessed using a translated version of American Thoracic Society Division of Lung Disease (ATS-DLD) questionnaire. Results of the study show that 60% of Bull's Trench Kiln (BTK) workers had diabetes mellitus, while 40% were suffering from chronic obstructive pulmonary disease and 44% reported chronic cough than Zig-Zag Kiln workers. A significant lower concentration of PM2.5 (23 ± 0.27 µg/m[3]), PM10 (27 ± 0.31 µg/m[3]) and Cu (0.025 ± 0.002 mg/kg) was observed in the environment of Zig-Zag Kilns (ZZKs). Adjusted generalized linear models (GLMs) with an identity link function showed that BTK workers in burning section had significantly lower FEV1/FVC (%) (B = -7.8; 95% CI: -8.9, -6.8; p < 0.001) than ZZK workers, reflecting a higher respiratory risk in traditional kilns. In conclusion, mandating ZZK adoption is a critical intervention to mitigate respiratory disease burden and heavy metal exposure among brick kiln workers.

Asthma and chronic obstructive pulmonary disease (COPD) are significant global health burdens with conventional diagnosis relying on resource-intensive spirometry. This paper presents a reproducible multimodal respiratory sound screening model combining complementary acoustic and clinical representations. The proposed method fuses handcrafted spectral-temporal features (MFCCs, chroma, spectral contrast, tonnetz, mel-spectrogram, tempogram) with precomputed cough and vowel embeddings and structured clinical metadata, processed via a class-weighted CatBoost ensemble on the standardized AIRS Kaggle benchmark dataset. The model achieves an overall accuracy of 90.3% with class-wise F1-scores of 0.945 (Healthy), 0.915 (Asthma), and 0.842 (COPD). Systematic ablation experiments confirm the importance of multimodal fusion (-7.8% accuracy without full feature fusion), the attention mechanism (-4.9%), and data augmentation (-6.7%). Additional metrics such as, Matthews Correlation Coefficient (MCC = 0.856) and Cohen's Kappa (κ = 0.849) - confirm robust classification under class imbalance. Structured multimodal feature fusion with gradient boosting enables scalable, reproducible respiratory disease screening applicable to telemedicine. Future work should address prospective validation on diverse, multi-institutional clinical cohorts.

Airway clearance techniques (ACTs) are guideline-recommended in bronchiectasis, while contemporary patterns and determinants of ACTs adoption remain unclear in China.