Author response: Nanoscale dysregulation of collagen structure-function disrupts mechano-homeostasis and mediates pulmonary fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a devastating disease of the lung, which scars the tissue and gradually destroys the organ, ultimately leading to death. It is still unclear what exactly causes this scarring, but it is thought that increasing amounts of proteins in the space surrounding the cells of the lungs, the extracellular matrix, could play a role. These proteins, including collagen, normally form a ‘scaffold’ to stabilize cells, but if they accumulate uncontrollably, they can render tissues rigid. It has been assumed that these changes are a consequence of the disease. However, recent evidence suggests that the increased stiffness itself could stimulate cells to produce even more extracellular matrix, driving the progression of the disease. A better understanding of what exactly causes the tissue to become gradually stiffer may identify new ways to block the progression of IPF. Now, Jones et al. compared measurements of the tissue stiffness and the collagen structure taken from samples of patients with IPF. The results showed that the collagen fibres were faulty and had an abnormal shape. This suggests that these problems, rather than an increased amount of collagen, alter the flexibility of the lung tissue. Jones et al. also found that a specific family of proteins, which helps to connect the collagen fibres, was increased in the tissue of patients with IPF. When these proteins were blocked with a newly developed drug, the collagen structure returned to normal and the stiffness of the tissue decreased. As a consequence, the lung capacity improved. This suggests that treatment approaches that help to maintain a normal collagen structure, may in future prevent the stiffening of the lung tissue and so limit feed-forward mechanisms that drive progressive IPF. Moreover, it indicates that measurements of the structure of collagen rather than the its total concentration could serve as a more suitable indicator for the disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.220 | 0.072 |
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 source (direct Gemma or distilled Codex), 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".