Molecular Biology of Gastrointestinal Peptides and Growth Factors: Relevance to Intestinal Adaptation
Bibliographic record
Abstract
New approaches towards understanding regulation of growth and adaptation of the small intestine are made possible by the isolation and characterization of genes and complementary DNAs (cDNAs) encoding gastrointestinal peptides, growth factors and their receptors. Nucleotide sequencing provides prerequisite structural information. Analyses of gene expression by quantitation and localization of mRNAs provide information about correlations between local alterations in peptide or receptor synthesis and intestinal growth. Analyses of intestinal growth in transgenic animals that overexpress or underexpress growth factor or receptor genes provides direct information about peptide effects on growth. Our recent studies with genes and cDNAs encoding proglucagon and the growth hormone dependent insulin-like growth factor 1 (IGF-I) represent examples of these approaches. Sequences of proglucagon and IGF-I cDNAs provide the primary structures of the peptide precursors. Analyses of proglucagon mRNA during adaptive growth after small bowel resection indicate that increases in proglucagon gene transcription or mRNA stability underly previously observed increases in serum enteroglucagons during adaptive growth. Analyses of IGF-I mRNAs in intestine indicate that small intestine expresses only a subset of the IGF-I mRNAs expressed in liver due to utilization of specific promotors and/or exon splicing mechanisms. Oligomers derived from the 3' end of the rat IGF-I gene detect a novel intestinal specific IGF-I related mRNA that shows an aboral decline in abundance from duodenum to colon and is upregulated in a number of situations of adaptive growth. Transgenic mice that overexpress growth hormone or IGF-I are under analysis to establish the effects of growth hormone and IGF-I on intestinal growth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".