Mentors And The Butterfly Effect: Triggers For Discovering Signalling by Proteinases via Proteinase-Activated Receptors (PARs) And More
Bibliographic record
Abstract
The essential role of proteinases as regulatory digestive enzymes, recognized since the late 1800s, has been underscored by the discovery that more than 2% of the genome codes for proteinases and their inhibitors. Further, by the early 1970s it was appreciated that in addition to their digestive actions, proteinases can affect cell function: (1) by the generation or degradation of peptide hormones and (2) by the direct regulation of signalling by receptors like the one for insulin. It was the discovery in the 1990s of the novel G-protein-coupled 'proteinase-activated receptor' (PAR) family that has caused a paradigm shift in the understanding of the way that proteinases can regulate cell signalling. This overview provides a perspective for the discovery of the PARs and my laboratory's role in (1) understanding the molecular pharmacology of these fascinating receptors and (2) identifying the potential pathophysiological roles that the PAR family can play in inflammatory disease. In this context, the overview also portrays the essential impact that seemingly minor comments/insights provided by my lifelong mentors have had on kindling my intense interest in proteinase-mediated signalling. The 'butterfly effect' of those comments has led to an unexpectedly large impact on my own research directions. Hopefully my own 'butterfly comments' will also be heard by my trainees and other colleagues with whom I am currently working and will promote future discoveries that will be directly relevant to the treatment of inflammatory disease.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".