Aquatic surface respiration improves survival during hypoxia in zebrafish (<i>Danio rerio</i>) lacking hypoxia-inducible factor 1-α
Bibliographic record
Abstract
Hypoxia-inducible factor 1-α (Hif-1α), an important transcription factor regulating cellular responses to reductions in O2, previously was shown to improve hypoxia tolerance in zebrafish (Danio rerio). Here, we examined the contribution of Hif-1α to hypoxic survival, focusing on the benefit of aquatic surface respiration (ASR). Wild-type and Hif-1α knockout lines of adult zebrafish were exposed to two levels (moderate or severe) of intermittent hypoxia. Survival was significantly compromised in Hif-1α knockout zebrafish prevented from accessing the surface during severe (16 mmHg) but not moderate (23 mmHg) hypoxia. When allowed access to the surface in severe hypoxia, survival times did not differ between wild-type and Hif-1α knockouts. Performing ASR mitigated the negative effects of the loss of Hif-1α with the knockouts initiating ASR at a higherPO2threshold and performing ASR for longer than wild-types. The loss of Hif-1α had little impact on survival in fish between 1 and 5 days post-fertilization, but as the larvae aged, their reliance on Hif-1α increased. Similar to adult fish, ASR compensated for the loss of Hif-1α on survival. Together, these results demonstrate that age, hypoxia severity and, in particular, the ability to perform ASR significantly modulate the impact of Hif-1α on survival in hypoxic zebrafish.
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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".