Feather pulp: a novel substrate useful for proton nuclear magnetic resonance spectroscopy metabolomics and biomarker discovery
Bibliographic record
Abstract
Noninvasive biomarkers of stress that are predictive of poultry health are needed. Feather pulp is highly vascularized and represents a potential source of biomarkers that has not been extensively explored. We investigated the feasibility and use of feather pulp for novel biomarker discovery using 1H-Nuclear Magnetic Resonance Spectroscopy (NMR)-based metabolomics. To this end, high quality NMR metabolomic spectra were obtained from chicken feather pulp extracted using either ultrafiltration (UF) or Bligh-Dyer methanol-chloroform (BD) methods. In total, 121 and 160 metabolites were identified using the UF and BD extraction methods, respectively, with 71 of these common to both methods. The metabolome of feather pulp differed in broiler breeders that were 1-, 23-, and 45-wk-of-age. Moreover, feather pulp was more difficult to obtain from older birds, indicating that age must be considered when targeting feather pulp as a source of biomarkers. The metabolomic profile of feather pulp obtained from 12-day-old broilers administered corticosterone differed from control birds, indicating that the metabolome of feather pulp was sensitive to induced physiological stress. A comparative examination of feather pulp and serum in broilers revealed that the feather pulp metabolome differed from that of serum but provided more information. The study findings show that metabolite biomarkers in chicken feather pulp may allow producers to effectively monitor stress, and to objectively develop and evaluate on-farm mitigations, including practices that reduce stress and enhance bird health.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".