Serum Immune-Related Proteins are Differentially Expressed during Hibernation in the American Black Bear
Bibliographic record
Abstract
Hibernation is an adaptation to conserve energy in the face of extreme environmental conditions and low food availability that has risen in several animal phyla. This phenomenon is characterized by reduced metabolic rate (∼25% of the active basal metabolic rate in hibernating bears) and energy demand, while other physiological adjustments are far from clear. The profiling of the serum proteome of the American black bear (Ursus americanus) may reveal specific proteins that are differentially modulated by hibernation, and provide insight into the remarkable physiological adaptations that characterize ursid hibernation. In this study, we used differential gel electrophoresis (DIGE) analysis, liquid chromatography coupled to tandem mass spectrometry, and subsequent MASCOT analysis of the mass spectra to identify candidate proteins that are differentially expressed during hibernation in captive black bears. Seventy serum proteins were identified as changing by ±1.5 fold or more, out of which 34 proteins increased expression during hibernation. The majority of identified proteins are involved in immune system processes. These included α2-macroglobulin, complement components C1s and C4, immunoglobulin μ and J chains, clusterin, haptoglobin, C4b binding protein, kininogen 1, α2-HS-glycoprotein, and apoplipoproteins A-I and A-IV. Differential expression of a subset of these proteins identified by proteomic analysis was also confirmed by immunodetection. We propose that the observed serum protein changes contribute to the maintenance of the hibernation phenotype and health, including increased capacities for bone maintenance and wound healing during hibernation in bears.
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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) | 0.000 | 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".