Proteomic analysis of microparticles produced by wound healing myofibroblasts cells
Bibliographic record
Abstract
Production of microparticles (MP) by myofibroblasts (Wmyo) during wound healing process is a new finding in the field of cellular communication. To determine the proteome of MP, the 2D‐DIGE method was performed over MP produced by 6 different populations of Wmyo. Each gel allowed us to analysed MP extravesicular proteins and total protein content, due to the use of different Cydye labelling methods. Fluorescent labelling of each gel were acquired using Typhoon Variable Mode Imager system and all images were analysed by Delta2D software. We were looking for highly expressed and extremely conserved proteins. With these parameters and Saffold Viewer software, 133 spots were sent to mass spectrometry analysis for further characterisation. 292 different proteins were identified with a 95% probability of being peptides unique to one protein. This wide variety of proteins included several enzymes such as fructose‐biphosphate aldolase A, L‐lactate dehydrogenase and ATP synthase. Lactadherin, Annexin A2, filamin‐A serpin H1 and elongation factors were also revealed in all samples studied. A large proportion of all the protein listed corresponded to cytoskeleton‐related protein. Knowledge of the protein content of MP used by Wmyo to communicate between each other will enable a better comprehension of the mechanism of wound healing process. Research Support: Thecell network‐FRQS.
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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) | 0.000 | 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".