The Role of Treg-expressed Mmp12 During the Resolution of Acute Lung Injury 2111
Bibliographic record
Abstract
Abstract Description Foxp3+ regulatory cells (Tregs) are vital in facilitating the resolution of lung injury through anti-inflammatory and pro-repair mechanisms. Prior Mock lab work demonstrated that Tregs isolated from the lung during LPS-induced acute lung injury (ALI) have a twenty-fold increase in expression of the transcript, Mmp12, compared to baseline pulonmary Tregs. Using the Cre-lox system, we generated a new strain of mice with a deletion in Mmp12 only in Tregs (Mmp12ΔTreg). Lungs from wild-type mice (WT) and Mmp12ΔTreg mice were collected at a resolving timepoint in two ALI models: LPS (day 7) and mouse-adapted strain of influenza PR8 (day 15). There was no distinct phenotype with the parameters of lung injury in the Mmp12ΔTreg mice compared to their parent strain in either of our injury models. However, at LPS resolution, the Mmp12ΔTreg mice had increased numbers of neutrophils and B cells compared to the WT; in our influenza model, we also see increased numbers of neutrophils at resolution. TAILS proteomic analysis on the BALs at LPS resolution highlights several proteins with neo-N-terminal enriched in the wild type, but NOT the Mmp12ΔTreg mice. Pathway analysis based on protein cleavage shows differences in inflammatory pathways between the two strains. The effect of Mmp12 deficient Tregs results in changes in immune kinetics during ALI. Ongoing efforts are further identifying Treg Mmp12-specific effects that impact the lung microenvironment during resolution. Funding Sources NHLBI supported research: R01HL152077. Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".