Proteinases, their receptors and inflammatory signalling: the <scp>O</scp>xford <scp>S</scp>outh <scp>P</scp>arks <scp>R</scp>oad connection
Bibliographic record
Abstract
In keeping with the aim of the Paton Memorial Lecture to 'facilitate the historical study of pharmacology', this overview, which is my distinct honour to write, represents a 'Janus-like' personal perspective looking both backwards and forwards at the birth and growth of 'receptor molecular pharmacology' with special relevance to inflammatory diseases. The overview begins in the Oxford Department of Pharmacology in the mid-1960s and then goes on to provide a current perspective of signalling by proteinases. Looking backwards, the synopsis describes the fruitful Oxford Pharmacology Department infrastructure that Bill Paton generated in keeping with the blueprint begun by his predecessor, J H Burn. Looking forwards, the overview illustrates the legacy of that environment in generating some of the first receptor ligand-binding data and providing the inspiration and vision for those like me who were training in the department at the same time. With apologies, I mention only in passing a number of individuals who benefitted from the 'South Parks Road connection' using myself as one of the 'outcome study' examples. It is also by looking forward that I can meet the complementary aim of summarizing the lecture presented at a 'BPS 2014 Focused Meeting on Cell Signalling' to provide an overview of the role of proteinases and their signalling mechanisms in the setting of inflammation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".