Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Nuclear and chromatin rearrangement associate to epigenome and gene expression changes in a model of in vitro adipogenesis and hypertrophy.

Hypertrophy of adipocytes represents the main cause of obesity. We investigated in vitro the changes associated with adipocyte differentiation and hypertrophy focusing on the nuclear morphometry and chromatin epigenetic remodelling. The 3 T3-L1 pre-adipocytes were firstly differentiated into mature adipocytes, then cultured with long-chain fatty acids to induce hypertrophy. Confocal and super-resolution stimulation emission depletion (STED) microscopy combined with ELISA assays allowed us to explore nuclear architecture, chromatin distribution and epigenetic modifications. In each condition, we quantified the triglyceride accumulation, the mRNA expression of adipogenesis and dysfunction markers, the release of five pro-inflammatory cytokines. Confocal microscopy revealed larger volume and less elongated shape of the nuclei in both mature and hypertrophic cells respect to pre-adipocytes, and a trend toward reduced chromatin compaction. Compared to mature adipocytes, the hypertrophic phenotype showed larger triglyceride content, increased PPARγ expression reduced IL-1a release, and up-regulation of a pool of genes markers for adipose tissue dysfunction. Moreover, a remodelling of both epigenome and chromatin organization was observed in hypertrophic adipocytes, with an increase in the average fluorescence of H3K9 acetylated domains in parallel with the increase in KAT2A expression, and a global hypomethylation of DNA. These findings making light on the nuclear changes during adipocyte differentiation and hypertrophy might help the strategies for treating obesity and metabolic complications.