Hypersensitivity pneumonitis (HP) is an interstitial lung disease (ILD) resulting from an immune-mediated response in susceptible and sensitized individuals to a large variety of inhaled antigens. Chronic HP with a fibrotic phenotype is characterized by disease progression and a dismal prognosis. The aim of this study was to identify predictors of progression and mortality in patients with chronic HP in real clinical practice. Materials and methods: This retrospective, multicenter, observational study used data from a registry of 1355 patients with fibrosing ILDs. The study included 292 patients diagnosed with chronic HP based on the conclusion of a multidisciplinary discussion (MDD). Results: The patients were divided into groups with progressive (92 (30.3%) patients) and nonprogressive pulmonary fibrosis (200 (69.7%) patients). The most significant predictors of adverse outcomes were a DLco < 50% predicted, an SpO2 at the end of a six-minute walk test (6-MWT) < 85%, and a GAP score ≥ 4 points. Conclusion: Pulmonary fibrosis and a progressive fibrotic phenotype are common in patients with chronic HP. Early detection of the predictors of an adverse prognosis of chronic HP is necessary for the timely initiation of antifibrotic therapy.

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.

Cystic fibrosis (CF) is a hereditary disease characterized by the progression of respiratory disorders, especially in adult patients. The purpose of the study was to identify volatile organic compounds (VOCs) as predictors of respiratory dysfunction, chronic respiratory infections of Staphylococcus aureus, Pseudomonas aeruginosa, Burkholderia cepacia, and VOCs associated with severe genotype and highly effective modulator treatment (HEMT). Exhaled breath samples from 102 adults with CF were analyzed using PTR-TOF-MS, obtained during a forced expiratory maneuver and normal quiet breathing. Using cross-validation and building gradient boosting classifiers (XGBoost), the importance of VOCs for functional and clinical outcomes was determined. The presence of the previously identified VOCs indole, phenol, and dimethyl sulfide were metabolic outcomes associated with impaired respiratory function. New VOCs associated with respiratory disorders were methyl acetate, carbamic acid, 1,3-Pentadiene, and 2,3-dimethyl-2-butene; VOCs associated with the above mentioned respiratory pathogens were non-differentiable nitrogen-containing organic compounds m/z = 47.041 (CH5NO)+ and m/z = 44.044 (C2H5NH+), hydrocarbons (cyclopropane, propene) and methanethiol; and VOCs associated with severe CFTR genotype were non-differentiable VOC m/z = 281.053. No significant features associated with the use of HEMT were identified. Early non-invasive determination of VOCs as biomarkers of the severity of CF and specific pathogenic respiratory flora could make it possible to prescribe adequate therapy and assess the prognosis of the disease. However, further larger standardized studies are needed for clinical use.

The problem of long COVID-19 (COronaVIrus Disease 2019) has been highly relevant for the healthcare system in the last three years. The persistence of respiratory symptoms, radiological and functional changes in COVID-19 patients brings new challenges to the entire medical community. The aim of the study is to explore long-term clinical and functional changes in patients with severe COVID-19-associated lung injury, including assessment of functional and radiological abnormalities of the respiratory system, as well as persistent clinical symptoms a year after the acute phase of the disease. Methods. The study included 45 patients who were examined 3, 6 and 12 months after COVID-19 with severe lung damage (more than 50% according to chest CT in the acute phase of the disease). Patients underwent multispiral computed tomography of the chest organs, a comprehensive study of respiratory function (spirography, body plethysmography and diffusion test); the clinical symptoms were assessed. Results. Chest CT scans showed gradual regression of pathological changes during the follow-up. However, radiographic changes of varying severity persisted after 12 months of follow-up in 51% of patients. A year later, restrictive disorders persisted in 20% of patients and the diffusion capacity of the lungs was reduced in 69% of patients. At the same time, a statistically significant difference in the DL_СО level was observed between 3, 6 and 12 months. The severity of dyspnea decreased 1 year after hospitalization in 48% of patients. Conclusion. The obtained results demonstrate a gradual regression of both radiological and functional pathological changes during the 1st year. However, CT changes and deviations of the respiratory function persist in some patients, mainly in the form of a decrease in DL_СО, which necessitates further monitoring of this group of patients.

Background: Asthma and chronic obstructive pulmonary disease (COPD) are prevalent obstructive lung diseases with distinct inflammatory pathways but overlapping clinical features. Bronchodilator responsiveness (BDR) is a key diagnostic criterion, yet its metabolic determinants are poorly understood. Objective: This cross-sectional study investigated whether integrated profiling of exhaled volatile organic compounds (VOCs) and clinical biomarkers can differentiate BA, COPD, and health, and predict BDR. Methods: Exhaled breath from 160 BA patients, 128 COPD patients, and 254 healthy controls was analyzed in real-time using proton-transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) during tidal and forced expiration. Clinical assessment included spirometry, fractional exhaled nitric oxide (FeNO), blood eosinophil count, and total IgE. Machine-learning (XGBoost) was employed for feature selection and model development. Results: Distinct VOC signatures effectively discriminated disease groups from controls and from each other. The model for distinguishing asthma from healthy controls achieved an AUC of 0.747 during normal quiet breathing and 0.710 during forced exhale. For discriminating COPD from healthy controls, the model performance was higher, with an AUC of 0.821 for normal quiet breathing and 0.856 for forced exhale. A model integrating VOC profiles with clinical biomarkers (FeNO, eosinophils, IgE) demonstrated very high accuracy in internal validation in predicting BDR (AUC = 1.000 for tidal breathing; AUC = 0.970 for forced expiration). Specific mass spectral features (m/z 79, m/z 101) were significantly associated with a positive BDR test. Conclusions: This study delineates disease-specific VOC signatures and underscores the profound synergy between exhaled metabolomics and clinical immunology for identifying associations treatment response, advocating for the integration of real-time breath analysis into personalized management strategies for obstructive lung diseases.