CD206<sup>+</sup>macrophages are relevant non-invasive imaging biomarkers and therapeutic targets in experimental lung fibrosis
Bibliographic record
Abstract
Background Interstitial lung diseases (ILDs) include a large number of diseases associated with progressive pulmonary fibrosis (PPF), including idiopathic pulmonary fibrosis (IPF). Despite the rarity of each of the fibrotic ILDs individually, they cumulatively affect a considerable number of patients. PPF is characterised by an excessive collagen deposition leading to functional decline. Objectives Therapeutic options are limited to nintedanib and pirfenidone which are only able to reduce fibrosis progression. CD206-expressing M2 macrophages are involved in fibrosis progression, and whether they may be relevant therapeutic targets or biomarkers remains an open question. Results In our study, CD206 + lung macrophages were monitored in bleomycin-induced lung fibrosis in mice by combining flow cytometry, scRNAseq and in vivo molecular imaging using a single photon emission computed tomography (SPECT) radiopharmaceutical, 99m Tc-tilmanocept. The antifibrotic effect of the inhibition of M2 macrophage polarisation with a JAK inhibitor, tofacitinib, was assessed in vivo. We demonstrate that CD206-targeted in vivo SPECT imaging with 99m Tc-tilmanocept was able to accurately detect and quantify the increase in CD206 + macrophages from early to advanced stages of experimental fibrosis and ex vivo in lung biopsies from patients with IPF. CD206-targeted imaging also specifically detected a decrease in CD206 + lung macrophages on nintedanib and tofacitinib treatment. Importantly, early in vivo imaging of CD206 + macrophages allowed the prediction of experimental lung fibrosis progression as well as nintedanib and tofacitinib efficacy. Conclusions These findings indicate that M2 macrophages may be relevant theranostic targets for personalised medicine for patients with PPF.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".