Bibliographic record
Abstract
We thank Drs Dulak and Jozkowicz for their interest in our paper [1]. They raise the question of whether induction of VEGF expression by NO in cultured smooth muscle cells (SMCs) may have contributed to the angiogenic effects of eNOS in our co-culture model. Although, we agree that this is a potential mechanism whereby NO might induce angiogenesis, we would like to point out that NO does not always increase angiogenic factor expression, and may even attenuate hypoxia-induced upregulation of VEGF by inhibition of HIF-1α in aortic SMCs and pulmonary artery EC or AP-1 in aortic SMCs [2,3]. In order to address this issue in our model, we have since measured VEGF production in response to eNOS overexpression in co-culture after 48 h of incubation. No detectable VEGF levels were observed in the co-culture of SMCeNOS with CPAEC, suggesting that eNOS overexpression did not increase VEGF expression in this model. However, SMCVEGF121 produced high levels of VEGF peptide detected by an ELISA kit (R&D Systems), (265±31 pg/ml per 105 cells, n = 4, each measurement in duplicate) which was not detected in the SMCCont co-cultures. These results confirm our earlier findings that NO generated by NO-donors facilitated EC migration and tube formation, without changing bFGF mRNA expression [4].
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".