Posttranscriptional adaptations of the vascular endothelium to hypoxia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Remarkable new advances have been made in the field of posttranscriptional gene regulation over recent years. These include the revelation of noncoding RNAs, such as microRNAs, antisense transcripts and their interactions with RNA-binding proteins (RBPs) in the context of both health and disease settings, such as hypoxia. In particular, these discoveries bear much relevance to the field of vascular biology, which historically has focused upon transcriptional processes. Thus, the contributions of these posttranscriptional gene regulatory mechanisms to vascular and endothelial biology represent a newer concept that warrants discussion. RECENT FINDINGS: Recent studies have revealed two emerging themes that are critical to endothelial/vascular biology and function. First is the functional integration between the microRNA pathway and the cellular hypoxic response, which, in addition to specific microRNAs, involves key components of the microRNA biogenesis machinery. A key concept here is the regulation of a master transcriptional programme through posttranscriptional mechanisms. The second major theme involves the dynamic interactions between RBPs, microRNAs and antisense RNAs. The condition-dependent collaborations and competitions between these different classes of posttranscriptional regulators reveal a critical layer of control for gene expression. SUMMARY: Taken together, these findings bear significant diagnostic and therapeutic implications for vascular disease.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".