Bibliographic record
Abstract
Drug Discovery for Inflammatory Diseases was a 3-day conference organised by the IBC Life Sciences as part of their Drug Discovery Series. The meeting featured a keynote presentation by Edward Keystone (University of Toronto) on the new strategies in managing rheumatoid arthritis, followed by five themed sessions: protein kinase inhibition, cytokines, chemokines, Toll-like receptors/innate immunity and transcription factors. The programme included a good mix of speakers from academia and biotechnology, and covered a wide spectrum of topics, ranging from developing strategies to suppress immune responses in autoimmune and inflammatory diseases, to novel means of stimulating the immune system for vaccine development and cancer therapy. Fundamental basic science questions were addressed along with the new approaches to drug design and clinical studies. Overall, the meeting included over 25 presentations, and there was ample time to exchange ideas in an informal setting. This review will focus on five talks representing three different areas of research: the role of an unusual cytokine, leptin, in autoimmunity; targeting of Toll-like receptors in vaccine development and in cancer therapy; and the molecular dissection of glucocorticoid-mediated repression and of selective glucocorticoids with anti-inflammatory activities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".