Defective Fibrillar Collagen Organization by Fibroblasts Contributes to Airway Remodeling in Asthma
Bibliographic record
Abstract
Abstract Rationale Histologic stains have been used as the gold standard to visualize extracellular matrix (ECM) changes associated with airway remodeling in asthma, yet they provide no information on the biochemical and structural characteristics of the ECM, which are vital to understanding alterations in tissue function. Objectives To demonstrate the use of nonlinear optical microscopy (NLOM) and texture analysis algorithms to image fibrillar collagen (second harmonic generation) and elastin (two-photon excited autofluorescence), to obtain biochemical and structural information on the remodeled ECM environment in asthma. Methods Nontransplantable donor lungs from donors with asthma (n = 13) and control (n = 12) donors were used for the assessment of airway collagen and elastin fibers by NLOM, and extraction of lung fibroblasts for in vitro experiments. Measurements and Main Results Fibrillar collagen is not only increased but also highly disorganized and fragmented within large and small asthmatic airways compared with control subjects, using NLOM imaging. Furthermore, such structural alterations are present in pediatric and adult donors with asthma, irrespective of fatal disease. In vitro studies demonstrated that asthmatic airway fibroblasts are deficient in their packaging of fibrillar collagen-I and express less decorin, important for collagen fibril packaging. Packaging of collagen fibrils was found to be more disorganized in asthmatic airways compared with control subjects, using transmission electron microscopy. Conclusions NLOM imaging enabled the structural assessment of the ECM, and the data suggest that airway remodeling in asthma involves the progressive accumulation of disorganized fibrillar collagen by airway fibroblasts. This study highlights the future potential clinical application of NLOM to assess airway remodeling in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".