Measurement of free glucocorticoids: quantifying corticosteroid binding capacity and its variation within and among mammal and bird species
Bibliographic record
Abstract
Plasma glucocorticoid (CORT) levels are one measure of stress in wildlife and give us insight into natural processes relevant to conservation issues. Many studies use total CORT concentrations to draw conclusions about animals' stress state and response to their environment. However, the blood of tetrapods contains corticosteroid-binding globulin (CBG), which strongly binds most circulating CORT. Only free CORT (CORT not bound by CBG) leaves the circulation and exerts biological effects on CORT-sensitive tissues. Measuring free CORT concentrations provides insight to an animal's stress response that cannot be revealed by simply measuring total CORT. To calculate free CORT concentrations in plasma or serum samples, one needs three measurements: the binding affinity of CBG for CORT (which varies by species), the total CORT concentration in the sample and the maximum corticosteroid binding capacity (MCBC) of CBG in the sample. Here, we detail the measurement of CBG binding capacity. We compare and contrast the three main methods to measure MCBC: charcoal, cell harvester and dialysis. Each is defined by the means by which free and bound CORT are separated. We weigh the relative merits and challenges of each. We conclude that sample volume, species and taxon binding specificity, and availability of equipment are the primary considerations in selecting the appropriate separation method. For most mammals, the charcoal method is recommended. For birds, the harvester method has critical advantages over the charcoal method. The dialysis method is widely regarded as the gold standard and has lower equipment costs but is more time-intensive and costly in terms of radioactive isotope needed and is less suited to processing large numbers of samples. The binding capacity of CBG varies tremendously within and among the bird and marine mammal species studied, and we discuss the implication of this variation for understanding the role of stress in wildlife.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".