COPD basal cells are primed towards secretory to multiciliated cell imbalance driving increased resilience to environmental stressors.

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.

Colorimetric Sensor Reading and Illumination Correction via Multi-Task Deep-Learning.

Colorimetric sensors represent an accessible and sensitive nanotechnology for rapid and accessible measurement of a substance's properties (e.g., analyte concentration) via color changes. Although colorimetric sensors are widely used in healthcare and laboratories, interpretation of their output is performed either by visual inspection or using cameras in highly controlled illumination set-ups, limiting their usage in end-user applications, with lower resolutions and altered light conditions. For that purpose, we implement a set of image processing and deep-learning (DL) methods that correct for non-uniform illumination alterations and accurately read the target variable from the color response of the sensor. Methods that perform both tasks independently vs. jointly in a multi-task model are evaluated. Video recordings of colorimetric sensors measuring temperature conditions were collected to build an experimental reference dataset. Sensor images were augmented with non-uniform color alterations. The best-performing DL architecture disentangles the luminance, chrominance, and noise via separate decoders and integrates a regression task in the latent space to predict the sensor readings, achieving a mean squared error (MSE) performance of 0.811±0.074[°C] and r2=0.930±0.007, under strong color perturbations, resulting in an improvement of 1.26[°C] when compared to the MSE of the best performing method with independent denoising and regression tasks.Clinical Relevance- The proposed methodology aims to improve the accuracy of colorimetric sensor reading and their large-scale accessibility as point-of-care diagnostic and continuous health monitoring devices, in altered illumination conditions.

Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.

Purpose: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease. Materials and Methods: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.3-28.6 weeks; 55 female and 48 male infants) with (n = 86) and without (n = 21) chronic lung disease (bronchopulmonary dysplasia [BPD]). Convolutional neural networks were developed for lung segmentation, and a three-dimensional reconstruction was used to calculate MRI features for lung volume, shape, pixel intensity, and surface. These features were explored as indicators of BPD and disease-associated lung structural remodeling through correlation with lung injury scores and multinomial models for BPD severity stratification. Results: The lung segmentation model reached a volumetric Dice coefficient of 0.908 in cross-validation and 0.880 on the independent test dataset, matching expert-level performance across disease grades. MRI lung features demonstrated significant correlations with lung injury scores and added structural information for the separation of neonates with BPD (BPD vs no BPD: average area under the receiver operating characteristic curve [AUC], 0.92 ± 0.02 [SD]; no or mild BPD vs moderate or severe BPD: average AUC, 0.84 ± 0.03). Conclusion: This study demonstrated high performance of DL models for MRI neonatal lung segmentation and showed the potential of automated MRI features for diagnostic assessment of neonatal lung disease while avoiding radiation exposure.Keywords: Bronchopulmonary Dysplasia, Chronic Lung Disease, Preterm Infant, Lung Segmentation, Lung MRI, BPD Severity Assessment, Deep Learning, Lung Imaging Biomarkers, Lung Topology Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Parraga and Sharma in this issue.