Relationships between oxidative stress, HIF‐1α transcription, erythropoietin and vascular endothelial growth factor during sustained hypoxia in humans
Bibliographic record
Abstract
The aim of this study was to investigate the relationships between reactive oxygen species (ROS), hypoxia inducible factor (HIF‐1α) transcription, and HIF‐1α target genes erythropoietin (EPO) and vascular endothelium growth factor (VEGF) in humans. Five healthy men (32±7 yrs, mean±SD) were exposed to 12 h of sustained hypoxia (P et O 2 =60 Torr). DNA oxidation (8‐Hydroxy‐2′‐deoxyguanosine, 8‐OHdG) oxidation protein products (AOPP), EPO and VEGF were measured in plasma and HIF‐1α mRNA was assessed in leucocytes before, and after 1, 2, 4, 6, 8, 10, 12 h of exposure to hypoxia. HIF‐1α mRNA amount increased during the first 2h of hypoxic exposure (+ 68%; p=0.03), then returned to baseline levels. VEGF increased at 4h (+121%; p=0.02) whereas EPO increased progressively from 4h to 12h (+114%; p<0.01). AOPP increased continuously from 4 h (+ 69%, p=0.04%) to 12h (+ 216 %, p=0.03) of hypoxic exposure while 8‐OHdG increased after 6 h (+ 78%, p<0.01) and remained elevated until 12h. During the “acute” increase phase of HIF‐1α (i.e., between 0 and 2h), 8‐OHdG was positively correlated with HIF‐1α (r=0.55, p=0.02). These findings demonstrate that hypoxia induces oxidative stress via an overproduction of ROS. Finally, this in vivo study in humans corroborates the previous in vitro findings demonstrating that ROS is involved in the stabilization of HIF‐1α transcription. Supported by Alberta Heritage Foundation for Medical Research, Heart & Stroke Foundation of Canada, Canadian Institutes of Health Research.
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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) | 0.001 | 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".