IDRs as Novel Immunomodulators (B208)
Bibliographic record
Abstract
Abstract Inimex Innate Defence Regulators (IDRs) are novel, synthetic immunomodulatory peptides that protect against infections by selectively activating the innate immune system while regulating inflammation. The multi-faceted effects of IDRs are mediated primarily by monocytes and macrophages. These cells respond to IDRs by selectively increasing the expression of cell surface receptors, and cytokines and chemokines (MCP-1, MCP-3 and CCL-5) which trigger the recruitment and activation of immune cells to the site of the infection. In addition, IDRs control inflammation by enhancing the expression of the anti-inflammatory cytokine IL-10, and down-regulating the release of pro-inflammatory cytokines TNF-α and IL-6 in response to pathogen-associated stimuli. As a result, IDRs selectively activate the immune system without concomitant up-regulation of inflammatory responses. This combination of IDR effects is a distinctive quality of these agents since they are able to maintain a balance between immunostimulatory and inflammatory responses to an invading pathogen. Haiyan Yang is the recipient of an NSERC Industrial R&D Fellowship. Funded in part by a grant from the FNIH and the CIHR through the Grand Challenges in Global Health initiative
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) | 0.000 | 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) | 0.004 | 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".