The Proadhesive Properties of Multimerin in Supporting Cellular Adhesion: Evidence for RGD and Non-RGD Dependent Adhesive Mechanisms.
Bibliographic record
Abstract
Abstract Multimerin is a large soluble protein, with an uncertain function, found in platelets, megakaryocytes, endothelium and extracellular matrix fibers but not in plasma. The observation that multimerin contains structural features of an adhesive protein, including an Arg-Gly-Asp (RGD) sequence, led us to investigate its ability to support adhesion of platelets, megakaryocytes, endothelial cells and other cell types. Multimerin had the ability to support the adhesion of both platelets and megakaryocytes and this required cellular activation and the multimerin RGD site. Studies of normal and Glanzmann platelets indicated that multimerin interacted with the major platelet integrin receptor, αIIbβ3 and radioimmunoprecipitation analyses confirmed that multimerin bound to αIIbβ3. Multimerin also supported adhesion of endothelial cells, neutrophils and other cells including smooth muscle cells, fibroblast cells, human embryonic kidney (HEK293) and epithelial cells. Unlike platelets, these cells do not express αIIbβ3; this indicated that other integrin or non-integrin receptors could be involved in cellular adhesion to multimerin. Comparisons of cell adhesion to wild-type and RGE-multimerin indicated that unlike platelets and megakaryocytes, some other cell types (e.g. endothelial cells, smooth muscle cells and neutrophils) were capable of adhering to RGE-multimerin. This suggested that cellular adhesion to multimerin occurs by both RGD and non-RGD dependent mechanisms. Finally, unlike platelets, megakaryocytes and neutrophils, adhesion of other cell types to multimerin did not require cellular activation. In conclusion, our data indicate multimerin has fairly broad proadhesive properties, involving RGD and non-RGD dependent mechanisms, and that cellular receptors including αIIbβ3 interact with multimerin to mediate its binding to activated platelets, endothelial cells and potentially other cell types.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".