Bibliographic record
Abstract
The proprotein convertases (PCs) are secretory mammalian serine proteinases related to bacterial subtilisin-like enzymes. The family of PCs comprises nine members, PC1/3, PC2, furin, PC4, PC5/6, PACE4, PC7, SKI-1/S1P, and PCSK9 (Fig. 3.1). While the first seven PCs cleave after single or paired basic residues, the last two cleave at non-basic residues and the last one PCSK9 only cleaves one substrate, itself, for its activation. The targets and substrates of these convertases are very varied covering many aspects of cellular biology and communication. While it took more than 22 years to begin to identify the first member in 1989-1990, in less than 14 years they were all characterized. So where are we 20 years later in 2011? We have now reached a level of maturity needed to begin to unravel the mechanisms behind the complex physiological functions of these PCs both in health and disease states. We are still far away from comprehensively understanding the various ramifications of their roles and to identify their physiological substrates unequivocally. How do these enzymes function in vivo? Are there other partners to be identified that would modulate their activity and/or cellular localization? Would non-toxic inhibitors/silencers of some PCs provide alternative therapies to control some pathologies and improve human health? Are there human SNPs or mutations in these PCs that correlate with disease, and can these help define the finesses of their functions and/or cellular sorting? The more we know about a given field, the more questions will arise, until we are convinced that we have cornered the important angles. And yet the future may well reserve for us many surprises that may allow new leaps in our understanding of the fascinating biology of these phylogenetically ancient eukaryotic proteases (Fig. 3.2) implicated in health and disease, which traffic through the cells via multiple sorting pathways (Fig. 3.3).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".