Elevational variation in heart mass and suppression of hypoxia-induced right ventricle hypertrophy in Andean leaf-eared mice ( <i>Phyllotis</i> )
Bibliographic record
Abstract
ABSTRACT In lowland mammals that ascend to high elevation, hypoxia-induced changes in the pulmonary circulation can give rise to hypoxic pulmonary hypertension (HPH) and associated right-ventricle (RV) hypertrophy. Some mammals that are native to high elevation have evolved a means of attenuating HPH, demonstrating how environmental adaptation may sometimes counteract the effects of ancestral acclimatization responses. Here, we examine elevational variation in heart mass and measures of RV hypertrophy in four closely related species of leaf-eared mice (genus Phyllotis ) that are broadly co-distributed across a steep elevational gradient on the Western slope of the Andes. There was a positive relationship overall between heart mass and elevation that reflected proportional changes in both the right and left ventricles. Thus, elevation-related increases in overall heart mass were not generally attributable to RV hypertrophy, suggesting that this group of predominantly highland species have evolved a means of avoiding HPH and/or attenuating the cardiac response to HPH. To gain insight into possible transcriptional mechanisms, we examined patterns of transcriptomic variation in the right ventricles of Phyllotis vaccarum from two geographically distinct highland populations (both from elevations >5000 m) that exhibit strikingly different levels of RV hypertrophy. Suppression of RV hypertrophy is associated with differential expression of key regulatory genes involved in striated muscle, immune processes, and the inflammatory response. Analysis of co-expression modules identified a promising set of candidate genes for mediating the development of RV hypertrophy at extremely high elevations.
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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 | 0.001 | 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".