Posttranslational Processing of Peptide Precursors to Fragments
Bibliographic record
Abstract
In theory only one excitatory and one inhibitory transmitter should be sufficient to operate the nervous system. Therefore the recent discovery that, besides an already large number of well-established classical neurotransmitters, many small peptides are also capable of converting neural signals into physiological responses, has evoked both interest and doubt. There seem to be about two dozen peptide neurotransmitter candidates and the number is increasing rapidly. Peptides that may serve as neurotransmitters or neuromodulators have been found in both the central nervous system and in a wide variety of peripheral organs. These include angiotensin II, members of the gastrinl/ cholecystokinin family, as well as large peptide hormones, such as prolactin, growth hormone, insulin, glucagon, and many peptides derived from proopiomelanocortin (POMC), the common precursor to ACTH and β-endorphin in the pituitary. In peripheral tissues some of these peptides have been shown or were already known to be released in the circulation, where they could act as hormones. The cells responsible for the synthesis of these neuropeptides have been assigned to Pearse’s category of APUD (amine precursor uptake and decarboxylation) cells (). APUD cells constitute a system often referred to as the diffuse neuroendocrine system (). For this reason these peptides are often called neuroendocrine peptides
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".