Immunotherapeutic targeting of aging‐associated isoDGR motif in chronic lung inflammation
Bibliographic record
Abstract
Accumulation of damaged biomolecules in body tissues is the primary cause of aging and age-related chronic diseases. Since this damage often occurs spontaneously, it has traditionally been regarded as untreatable, with typical therapeutic strategies targeting genes or enzymes being ineffective in this domain. In this report, we demonstrate that an antibody targeting the isoDGR damage motif in lung tissue can guide immune clearance of harmful damaged proteins in vivo, effectively reducing age-linked lung inflammation. We observed age-dependent accumulation of the isoDGR motif in human lung tissues, as well as an 8-fold increase in isoDGR-damaged proteins in lung fibrotic tissues compared with healthy tissue. This increase was accompanied by marked infiltration of CD68+/CD11b + macrophages, consistent with a role for isoDGR in promoting chronic inflammation. We therefore assessed isoDGR function in mice that were either naturally aged or lacked the isoDGR repair enzyme. IsoDGR-protein accumulation in mouse lung tissue was strongly correlated with chronic inflammation, pulmonary edema, and hypoxemia. This accumulation also induced mitochondrial and ribosomal dysfunction, in addition to features of cellular senescence, thereby contributing to progressive lung damage over time. Importantly, treatment with anti-isoDGR antibody was able to reduce these molecular features of disease and significantly reduced lung pathology in vivo.
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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) | 0.000 | 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 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".