Targeting Hypoxia-Inducible Factor-1 (HIF-1) Signaling in Therapeutics: Implications for the Treatment of Inflammatory Bowel Disease
Bibliographic record
Abstract
In response to hypoxia, adaptive hypoxia-inducible factor-1 (HIF-1) signaling events are activated to increase oxygen transport, anaerobic energy production and protective pathways to minimize ischemic tissue damage. Although the activation and subsequent induction of gene transcription by HIF-1 is normally associated with hypoxia, it is now established that HIF-1 signaling can be triggered under inflammatory conditions. HIF-1 has been implicated in a number of inflammatory diseases including rheumatoid arthritis, allergic asthma, psoriasis and inflammatory bowel disease (IBD). In the gastrointestinal tract, HIF-1-regulated gene products, such as vascular endothelial growth factor, intestinal trefoil factor and CD73, have been shown to provide protection in animal models of intestinal inflammation. Given the importance of HIF-1 signaling in the aforementioned diseases, there exists considerable interest in the development of methods to modulate HIF-1 expression as well as down-stream signaling events. This review examines HIF-1 signaling with a special focus on the gastrointestinal tract. The patents pertaining to the modulation of HIF-1 signaling are summarized, and their relevance to the treatment of inflammatory bowel disease is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".