Angiogenic signaling in the lungs of a metabolically suppressed hibernating mammal (<i>Ictidomys tridecemlineatus</i>)
Bibliographic record
Abstract
To conserve energy in times of limited resource availability, particularly during cold winters, hibernators suppress even the most basic of physiologic processes. Breathing rates decrease from 40 breaths/minute to less than 1 breath/min as they decrease body temperature from 37 °C to ambient. Nevertheless, after months of hibernation, these incredible mammals emerge from torpor unscathed. This study was conducted to better understand the protective and possibly anti-inflammatory adaptations that hibernator lungs may use to prevent damage associated with entering and emerging from natural torpor. We postulated that the differential protein expression of soluble protein receptors (decoy receptors that sequester soluble ligands to inhibit signal transduction) would help identify inhibited inflammatory signaling pathways in metabolically suppressed lungs. Instead, the only two soluble receptors that responded to torpor were sVEGFR1 and sVEGFR2, two receptors whose full-length forms are bound by VEGF-A to regulate endothelial cell function and angiogenesis. Decreased sVEGFR1/2 correlated with increased total VEGFR2 protein levels. Maintained or increased levels of key γ-secretase subunits suggested that decreased sVEGFR1/2 protein levels were not due to decreased levels of intramembrane cleavage complex subunits. VEGF-A protein levels did not change, suggesting that hibernators may regulate VEGFR1/2 signaling at the level of the receptor instead of increasing relative ligand abundance. A panel of angiogenic factors used to identify biomarkers of angiogenesis showed a decrease in FGF-1 and an increase in BMP-9. Torpid lungs may use VEGF and BMP-9 signaling to balance angiogenesis and vascular stability, possibly through the activation of SMAD signaling for adaptive tissue remodeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".