An agonistic anti-signal regulatory protein α antibody for chronic inflammatory diseases
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Signal regulatory protein (SIRPα) is an immune inhibitory receptor expressed by myeloid cells to inhibit immune cell phagocytosis, migration, and activation. Despite the progress of SIRPα and CD47 antagonist antibodies to promote anti-cancer immunity, it is not yet known whether SIRPα receptor agonism could restrain excessive autoimmune tissue inflammation. Here, we report that neutrophil- and monocyte-associated genes including SIRPA are increased in inflamed tissue biopsies from patients with rheumatoid arthritis and inflammatory bowel diseases, and elevated SIRPA is associated with treatment-refractory ulcerative colitis. We next identify an agonistic anti-SIRPα antibody that exhibits potent anti-inflammatory effects in reducing neutrophil and monocyte chemotaxis and tissue infiltration. In preclinical models of arthritis and colitis, anti-SIRPα agonistic antibody ameliorates autoimmune joint inflammation and inflammatory colitis by reducing neutrophils and monocytes in tissues. Our work provides a proof of concept for SIRPα receptor agonism for suppressing excessive innate immune activation and chronic inflammatory disease treatment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it