The Prohormone Theory and the Proprotein Convertases: It Is All About Serendipity
Bibliographic record
Abstract
When I became a physician and an endocrinologist in the early 1960s, peptide hormone sequencing was still in its infancy; it was also far removed from my immediate interests. Through chance encounters with prominent teachers and mentors, I later became increasingly convinced that elucidation of the primary sequence of peptide hormones is key to understanding their production as well as their functions in human health and disease. My interest for pituitary hormones led me to discover that the sequence of β-melanocyte-stimulating hormone was contained within that γ and β-lipotropins and could be released from the latter by limited endoproteolysis. This prohormone theory became the leitmotiv of my career as a clinician/scientist. Through serendipity and the efforts of many laboratories including mine, this theory has now been widely confirmed, extended to various precursor proteins and implicated in many diseases. It has led to our discovery of the proprotein convertases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".