Identification of Metabolomic Biomarkers of Long-Term Stress Using NMR Spectroscopy in a Diving Duck
Bibliographic record
Abstract
Human-induced environmental changes that act as long-term stressors pose significant impacts on wildlife health. Energy required for maintenance or other functions may be re-routed towards coping with stressors, ultimately resulting in fluctuations in metabolite levels associated with energy metabolism. While metabolomics approaches are used increasingly to study environmental stressors, its use in studying stress in birds is in its infancy. We implanted captive lesser scaup (Aythya affinis) with either a biodegradable corticosterone (CORT) pellet to mimic the effects of a prolonged stressor or a placebo pellet. 1D 1H nuclear magnetic resonance (NMR) spectroscopy was performed on serum samples collected over 20 days after implant surgery. We hypothesized that CORT pellet-induced physiological stress would alter energy metabolism and result in distinct metabolite profiles in ducks compared with placebo (control). Quantitative targeted metabolite analysis revealed that metabolites related to energy metabolism: glucose, formate, lactate, glutamine, 3-hydroxybutyrate, ethanolamine, indole-3- acetate, and threonine differentiated ducks with higher circulatory CORT from controls on day 2. These metabolites function as substrates or intermediates in metabolic pathways related to energy production affected by elevated serum CORT. The use of metabolomics shows promise as a novel tool to identify and characterize physiological responses to stressors in wild birds.
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) | 0.000 | 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".