Lentivirus-mediated calcitonin gene-related peptide transfection enhances endothelial differentiation of rat bone marrow mesenchymal stem cells
Bibliographic record
Abstract
AIM: To study the effect of calcitonin gene-related peptide(CGRP) gene transfection mediated by lentivirus on the differentiation of rat bone marrow mesenchymal stem cells(MSCs) to endothelial cells.METHODS: Rat bone marrow MSCs were isolated by density gradient centrifugation combined with adherence method.Recombinant lentivirus vector carrying CGRP gene(Lenti-CGRP) was transfected into the MSCs.The secretion of CGRP in culture supernatants of the transfected MSCs was detected using ELISA method.The cells at passage 3 were divided into three groups: CGRP group(MSCs transfected with Lenti-CGRP),CGRP + CGRP 8-37(an antagonist of CGRP receptor) group and control group(MSCs transfected with PBS).The differentiation of the MSCs was detected by immunocytochemical staining for CD31 and factor Ⅷ-related antigen.The proliferation of the cells was measured by cell counting,and the angiogenic ability of the cells was analyzed using Matrigel assay.RESULTS: The proportion of CD31-and factor Ⅷ-related antigen-positive cells in CGRP and CGRP + CGRP 8-37 groups was larger than that in control group(P 0.05).The numbers of the cells in CGRP and CGRP + CGRP 8-37 groups were significantly increased compared with control group(P 0.05).Lumen-like structures were observed in CGRP and CGRP + CGRP 8-37 groups.The above indexes in CGRP + CGRP 8-37 group were reduced compared with CGRP group.CONCLUSION: Transfection with CGRP gene induces rat bone marrow MSCs to differentiate into endothelial cells and enhances their proliferation,suggesting that CGRP may play a role in the regulation of angiogenesis.
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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 | 0.001 |
| 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".