Animal farming is associated with exposure to organic dust containing allergens, endotoxins, and alive microbial organisms. Microbial Volatile Organic Compounds (MVOCs) produced mainly by fungi and bacteria pollute the air and accumulate particularly in confined animal buildings, posing serious health hazards to workers, including veterinarians. The exposure can lead to rhinitis, chronic bronchitis, asthma, and chronic obstructive pulmonary disease (COPD). In a range of cross-sectional studies, wheeze and asthma were associated with exposure to swine, dairy cattle, horse, and sheep, but also to more specific exposures like manure. Occupational asthma has been reported in agricultural workers, including veterinarians. We report two cases of veterinarians who developed asthma in relation to prolonged and intense contact with sheep. No specific allergens could be found, MVOCs were considered as the causative agents.

The expiratory time constant (TC), reflecting the rate of lung emptying, has emerged as a marker of early small airway disease. Although TC is traditionally used in mechanical ventilation, several calculation methods are also applicable to spirometry. This study aimed to evaluate the feasibility of four spirometry-based approaches for determining TC and to compare their sensitivity to detect airway obstruction. In this multicenter, cross-sectional study of 17,988 adult flow/volume curves, the TC was directly measured (TCM: time to exhale 63

The processing of personal data is one of the key challenges of digitized healthcare. In allergology in particular—a field with an interdisciplinary focus and often long-term treatment processes—the handling of sensitive health data is of special importance. This review examines the relevance of data protection in allergological practice and research. The focus is on the legal framework, particularly the General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG), as well as their practical implications for allergological care. Key data protection principles—such as data minimization, purpose limitation, transparency, storage limitation, and access control—may conflict with the medical need for continuous, long-term, and interdisciplinary data use. This applies in particular to electronic health records, telemedicine procedures, mobile health applications, cloud services, and the documentation of long-term therapies such as allergen-specific immunotherapy. Further challenges arise from a lack of interoperability, insufficient staff training, and increased requirements for differentiated access management. Based on current studies and real-world examples, this review presents legal, technical, and organizational measures. These include differentiated access rights, pseudonymization for research purposes, end-to-end encryption, and digital consent tools that enable flexible and patient-centered consent. Data protection in allergology is not only a legal obligation but also a central prerequisite for quality, trust, and innovation in patient care. Future developments such as big data, artificial intelligence, and international collaborations require robust data protection concepts that bridge the gap between the reality of medical care and regulatory requirements.

In COPD, acute exacerbations (AECOPD) are major contributors to morbidity and mortality, emphasising the need for effective stratification of individuals at high risk. Quantitative computed tomography (qCT) refers to the extraction of numerical data from CT images to objectively characterise anatomical and functional features, and it has been increasingly investigated in COPD assessment. We systematically reviewed the literature to evaluate the use of qCT for the diagnosis and prognosis of AECOPD. Of 362 screened records, 18 studies were included in this review. No studies were identified that used qCT features to diagnose AECOPD in CT scans made at the point of exacerbation. 11 studies reported qCT features identified in CT scans performed in stable COPD that are associated with frequent AECOPD and seven studies developed and tested models integrating CT features for the prediction of AECOPD. Across these studies, greater emphysema extent, thicker bronchial walls and increased air trapping were associated with higher exacerbation risk. However, current evidence is limited by heterogeneity in study design and lack of prospective validation. This review highlights the potential of quantitative CT analysis, which may be further enhanced by the integration of automated software, to support the development of imaging biomarkers for AECOPD. Future research is needed to refine qCT features for diagnosing AECOPD and establish CT-based tools that can predict AECOPD.