Expression Profiling Reveals Novel Hypoxic Biomarkers in Peripheral Blood of Adult Mice Exposed to Chronic Hypoxia
Bibliographic record
Abstract
Hypoxia induces a myriad of changes including an increase in hematocrit due to erythropoietin (EPO) mediated erythropoiesis. While hypoxia is of importance physiologically and clinically, lacunae exist in our knowledge of the systemic and temporal changes in gene expression occurring in blood during the exposure and recovery from hypoxia. To identify these changes expression profiling was conducted on blood obtained from cohorts of C57Bl-10 wild type mice that were maintained at normoxia (NX), exposed for two weeks to normobaric chronic hypoxia (CH) or two weeks of CH followed by two weeks of normoxic recovery (REC). Using stringent bioinformatic cut-offs (0% FDR, 2 fold change cut-off), 230 genes were identified and separated into four distinct temporal categories. Class I) contained 1 transcript up-regulated in both CH and REC; Class II) contained 202 transcripts up-regulated in CH but down-regulated after REC; Class III) contained 9 transcripts down-regulated both in CH and REC; Class IV) contained 18 transcripts down-regulated after CH exposure but up-regulated after REC. Profiling was independently validated and extended by analyzing expression levels of selected genes as novel biomarkers from our profile (e.g. spectrin alpha-1, ubiquitin domain family-1 and pyrroline-5-carboxylate reductase-1) by performing qPCR at 7 different time points during CH and REC. Our identification and characterization of these genes define transcriptome level changes occurring during chronic hypoxia and normoxic recovery as well as novel blood biomarkers that may be useful in monitoring a variety of physiological and pathological conditions associated with hypoxia.
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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".