Treating murine inflammatory diseases with an anti-erythrocyte antibody
Bibliographic record
Abstract
Treatment of autoimmune and inflammatory diseases typically involves immune suppression. In an opposite strategy, we show that administration of the highly inflammatory erythrocyte-specific antibody Ter119 into mice remodels the monocyte cellular landscape, leading to resolution of inflammatory disease. Ter119 with intact Fc function was unexpectedly therapeutic in the K/BxN serum transfer model of arthritis. Similarly, it rapidly reversed clinical disease progression in collagen antibody-induced arthritis (CAIA) and collagen-induced arthritis and completely corrected CAIA-induced increase in monocyte Fcγ receptor II/III expression. Ter119 dose-dependently induced plasma chemokines CCL2, CCL5, CXCL9, CXCL10, and CCL11 with corresponding alterations in monocyte percentages in the blood and liver within 24 hours. Ter119 attenuated chemokine production from the synovial fluid and prevented the accumulation of inflammatory cells and complement components in the synovium. Ter119 could also accelerate the resolution of hypothermia and pulmonary edema in an acute lung injury model. We conclude that this inflammatory anti-erythrocyte antibody simultaneously triggers a highly efficient anti-inflammatory effect with broad therapeutic potential.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".