Transcriptomic Changes in Young Japanese Males After Exposure to Acute Hypobaric Hypoxia
Bibliographic record
Abstract
After the genomic era, the development of high-throughput sequencing technologies has allowed us to advance our understanding of genetic variants responsible for adaptation to high altitude in humans. However, transcriptomic characteristics associated with phenotypic plasticity conferring tolerance to acute hypobaric hypoxic stress remain unclear. To elucidate the effects of hypobaric hypoxic stress on transcriptional variability, we aimed to describe transcriptomic profiles in response to acute hypobaric hypoxia in humans. In a hypobaric hypoxic chamber, young Japanese males were exposed to a barometric pressure of 493 mmHg (hypobaric hypoxia) for 75 min after resting for 30 min at the pressure of 760 mmHg (normobaric normoxia) at 28°C. Saliva samples of the subjects were collected before and after hypobaric hypoxia exposure, to be used for RNA sequencing. Differential gene expression analysis identified 30 significantly upregulated genes and some of these genes may be involved in biological processes influencing hematological or immunological responses to hypobaric hypoxic stress. We also confirmed the absence of any significant transcriptional fluctuations in the analysis of basal transcriptomic profiles under no-stimulus conditions, suggesting that the 30 genes were actually upregulated by hypobaric hypoxia exposure. In conclusion, our findings showed that the transcriptional profiles of Japanese individuals can be rapidly changed as a result of acute hypobaric hypoxia, and this change may influence the phenotypic plasticity of lowland individuals for acclimatization to a hypobaric hypoxic environment. Therefore, the results obtained in this study shed light on the transcriptional mechanisms underlying high-altitude acclimatization in humans.
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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".