Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT–staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type–specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation.

Innovative high-content screening platform identified N -(2-butoxyphenyl)-3-(phenyl)acrylamides as novel antifibrotics.

Background: Large language models (LLMs) such as ChatGPT (OpenAI) are increasingly discussed as potential tools for patient education in health care. In radiation oncology, where patients are often confronted with complex medical terminology and complex treatment plans, LLMs may support patient understanding and promote more active participation in care. However, the readability, accuracy, completeness, and overall acceptance of LLM-generated medical content remain underexplored. Objective: This study aims to evaluate the potential of ChatGPT-4 as a supplementary tool for patient education in the context of lung cancer radiotherapy by assessing the readability, content quality, and perceived usefulness of artificial intelligence–generated responses from both clinician and patient perspectives. Methods: A total of 8 frequently asked questions about radiotherapy for lung cancer were developed based on clinical experience from a team of clinicians specialized in lung cancer treatment at a university hospital. The questions were submitted individually to ChatGPT-4o (version as of July 2024) using the prompt: “I am a lung cancer patient looking for answers to the following questions.” Responses were evaluated using three approaches: (1) a readability analysis applying the Modified Flesch Reading Ease (FRE) formula for German and the 4th Vienna Formula (WSTF); (2) a multicenter expert evaluation by 6 multidisciplinary clinicians (radiation oncologists, medical oncologists, and thoracic surgeons) specialized in lung cancer treatment using a 5-point Likert scale to assess relevance, correctness, and completeness; and (3) a patient evaluation during the first follow-up appointment after radiotherapy, assessing comprehensibility, accuracy, relevance, trustworthiness, and willingness to use ChatGPT for future medical questions. Results: Readability analysis classified most responses as “very difficult to read” (university level) or “difficult to read” (upper secondary school), likely due to the use of medical language and long sentence structures. Clinician assessments yielded high scores for relevance (mean 4.5, SD 0.52) and correctness (mean 4.3, SD 0.65), but completeness received slightly lower ratings (mean 3.9, SD 0.59). A total of 30 patients rated the responses positively for clarity (mean 4.4, SD 0.61) and relevance (mean 4.3, SD 0.64), but lower for trustworthiness (mean 3.8, SD 0.68) and usability (mean 3.7, SD 0.73). No harmful misinformation was identified in the responses. Conclusions: ChatGPT-4 shows promise as a supplementary tool for patient education in radiation oncology. While patients and clinicians appreciated the clarity and relevance of the information, limitations in completeness, trust, and readability highlight the need for clinician oversight and further optimization of LLM-generated content. Future developments should focus on improving accessibility, integrating real-time readability adaptation, and establishing standardized evaluation frameworks to ensure safe and effective clinical use.

Introduction: Environmental pollutants injure the mucociliary elevator, thereby provoking disease progression in chronic obstructive pulmonary disease (COPD). Epithelial resilience mechanisms to environmental nanoparticles in health and disease are poorly characterised.Methods: We delineated the impact of prevalent pollutants such as carbon and zinc oxide nanoparticles, on cellular function and progeny in primary human bronchial epithelial cells (pHBECs) from end-stage COPD (COPD-IV, n=4), early disease (COPD-II, n=3) and pulmonary healthy individuals (n=4). After nanoparticle exposure of pHBECs at air–liquid interface, cell cultures were characterised by functional assays, transcriptome and protein analysis, complemented by single-cell analysis in serial samples of pHBEC cultures focusing on basal cell differentiation.Results: COPD-IV was characterised by a prosecretory phenotype (twofold increase in MUC5AC⁺) at the expense of the multiciliated epithelium (threefold reduction in Ac-Tub⁺), resulting in an increased resilience towards particle-induced cell damage (fivefold reduction in transepithelial electrical resistance), as exemplified by environmentally abundant doses of zinc oxide nanoparticles. Exposure of COPD-II cultures to cigarette smoke extract provoked the COPD-IV characteristic, prosecretory phenotype. Time-resolved single-cell transcriptomics revealed an underlying COPD-IV unique basal cell state characterised by a twofold increase in KRT5⁺(P=0.018) and LAMB3⁺(P=0.050) expression, as well as a significant activation of Wnt-specific (P=0.014) and Notch-specific (P=0.021) genes, especially in precursors of suprabasal and secretory cells.Conclusion: We identified COPD stage-specific gene alterations in basal cells that affect the cellular composition of the bronchial elevator and may control disease-specific epithelial resilience mechanisms in response to environmental nanoparticles. The identified phenomena likely inform treatment and prevention strategies.

Our study aimed to identify preoperative predictors for perioperative allogenic blood transfusion (ABT) in patients undergoing major lung cancer resections in order to improve the perioperative management of patients at risk for ABT. Patients admitted between 2014 and 2016 in a high-volume thoracic surgery clinic were retrospectively evaluated in a cohort study based on a control group without ABT and the ABT group requiring packed red blood cell units within 15 days postoperatively until discharge. The association of ABT with clinically established parameters (sex, preoperative anemia, liver and coagulation function, blood groups, multilobar resections) was analyzed by contingency tables, receiver operating characteristics (ROC) and logistic regression analysis, taking into account potential covariates. 60 out of 529 patients (11.3%) required ABT. N1 and non-T1 tumors, thoracotomy approach, multilobar resections, thoracic wall resections and Rhesus negativity were more frequent in the ABT group. In multivariable analyses, female sex, preoperative anemia, multilobar resections, as well as serum alanine-aminotransferase levels, thrombocyte counts and Rhesus negativity were identified as independent predictors of ABT, being associated with OR (95% Confidence interval, p-value) of 2.44 (1.23–4.88, p = 0.0112), 18.16 (8.73–37.78, p < 0.0001), 5.79 (2.50–13.38, p < 0.0001), 3.98 (1.73–9.16, p = 0.0012), 2.04 (1.04–4.02, p = 0.0390) and 2.84 (1.23–6.59, p = 0.0150), respectively. In patients undergoing major lung cancer resections, multiple independent risk factors for perioperative ABT apart from preoperative anemia and multilobar resections were identified. Assessment of these predictors might help to identify high risk patients preoperatively and to improve the strategies that reduce perioperative ABT.

Background/Aim: The aim of the study was to identify predictors of long-term survival and propose an improved risk stratification in patients with pulmonary germ-cell metastases admitted for pulmonary metastasectomy. Patients and Methods: Thirty-four patients admitted to the Division of Thoracic Surgery Munich, Germany, from 04/1994 until 09/2017 were retrospectively analyzed. The impact of clinical parameters on survival was calculated using Kaplan–Meier, multivariate Cox regression analysis and receiver-operator curves. Results: Ten-year overall survival was 75.3%. Elevated American Society of Anesthesiologists score, metachronous metastasis, embryonal histology, intrathoracic lymph node involvement, brain metastases and thoracic wall infiltration were significant predictors of reduced survival. With the independent predictors (embryonal histology, metachronous metastasis and thoracic wall infiltration), a germinal non-seminomatous lung metastasis risk of death score (GLUMER) was calculated, accurately predicting survival (area under curve=0.8839, p=0.0023). Conclusion: In patients with pulmonary germ-cell metastases, intrathoracic lymph node involvement, embryonal carcinoma, metachronous metastasis and thoracic wall infiltration represent negative predictors of long-term survival. The GLUMER score might represent a promising tool for use in adapted follow-up care in high-risk patients.