The transition from normal lung anatomy to Fibrosis in IPF
Bibliographic record
Abstract
Rationale: This study explores the transition from normal lung anatomy to minimal and established fibrosis in IPF. Methods: Preoperative thoracic Multi Detector Computed Tomography (MDCT) scans of patients with severe IPF were used to identify regions of minimal and established fibrosis. The fibrosis statuses were registered on postoperative MDCT scans of one explanted lung specimen from the treatment of double lung transplantations. This registration made it possible to compare 42 samples of (6/lung × 7 lungs) of control lung tissues to 27 samples of minimal and 27 samples of established fibrosis samples using a combination of microCT, histology and next generation sequencing (RNAseq). Results: The transition from control lung anatomy to minimal fibrosis was associated with a sharp reduction of terminal bronchioles, increased visibility of the surviving small airways, the appearance of fibroblastic foci, increased infiltration of the tissue by CD4 and CD8 T cells, B cells and macrophages, which could form tertiary lymphoid organs. Further, the RNAseq analysis showed that the transition from normal to minimal fibrosis is associated with both the up-regulation of T cell costimulatory genes, such as IFNG, TIGIT, TLRs, and the down-regulation of the immune checkpoint genes, such as PDL1. Further, tissue repair genes, including FGF, TGFß, and PLAU, are also up-regulated during this transition. Conclusions: These results show the major histologic features of IPF that appear during the transition from normal anatomy to minimal fibrosis, whereas the further the transition from minimal to established fibrosis is dominated by the progressive deposition of dense fibrotic connective tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".