My road to Damascus: how I converted to the prohormone theory and the proprotein convertases
Bibliographic record
Abstract
My desire as a young endocrinologist to improve my clinical skills through a better knowledge of hormone chemistry led me to serendipitous discoveries and unexpected horizons. The first discovery, published in 1967, revealed that peptide hormones are derived from endoproteolytic cleavages of larger precursor polypeptides. It was the foundation of the prohormone theory. Initially thought to apply to a few hormones, the theory rapidly extended to many proteins, including neuropeptides, neurotrophins, growth and transcription factors, receptors, extracellular matrix proteins, bacterial toxins, and viral glycoproteins. Its endoproteolytic activation mechanism has become a fundamental cellular process, affecting many biological functions. It implied the existence of specific endoproteolytic enzymes. These proprotein convertases were discovered in 1990. They have been shown to play a wide range of important roles in health and disease. They have opened up novel therapeutic avenues. Inactivation of PCSK9 to reduce plasma cholesterol is currently the most promising. To make this good thing even better, I recently discovered in a French Canadian family a potent PCSK9 (Gln152His) mutation that significantly lowers plasma cholesterol and should confer cardiovascular longevity. The discovery helped me to complete the loop: "From the bedside to the bench and back to the bedside."
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".