Co-ordinate regulation of growth factor receptors and lipid phosphate phosphatase-1 controls cell activation by exogenous lysophosphatidate
Bibliographic record
Abstract
The serum-derived lipid growth factors, lysophosphatidate (LPA) and sphingosine 1-phosphate (S1P), activate cells selectively through different members of a family of endothelial differentiation gene (EDG) receptors. Activation of EDG receptors by LPA and S1P provides a variety of signalling cascades depending upon the G-protein coupling of the different EDG receptors. This leads to chemotactic and mitogenic responses, which are important in wound healing. For example, LPA stimulates fibroblast division and S1P stimulates the chemotaxis and division of endothelial cells leading to angiogenesis. Counteracting these effects of LPA and S1P, are the actions of lipid phosphate phosphatases (LPP, or phosphatidate phosphohydrolases, Type 2). The isoform LPP-1 is expressed in the plasma membrane with its active site outside the cell. This enzyme is responsible for ‘ecto-phosphatase’ activity leading to the degradation of exogenous lipid phosphate mediators, particularly LPA. Expression of LPP-1 decreases cell activation by exogenous LPA. The mechanism for this is controversial and several mechanisms have been proposed. Evidence will be presented that the LPPs cross-talk with EDG and other growth factor receptors, thus, regulating the responses of the cells to lipid phosphate mediators of signal transduction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".