Estrogen profiling in blood and brain: effects of season and an aggressive interaction in a songbird
Bibliographic record
Abstract
Abstract Neuroestrogens are synthesized in the brain and regulate social behavior and cognition. In the song sparrow ( Melospiza melodia ), 17β-estradiol (17β-E 2 ) promotes aggression, even during the non-breeding season, when circulating 17β-E 2 levels are low. Measuring estrogens is challenging due to their low concentrations and the limited sensitivity of many existing assays. Moreover, estrogens other than 17β-E 2 are often overlooked. Here, we developed a method for simultaneous measurement of eleven estrogens that is highly specific, sensitive, accurate, and precise. We used liquid chromatography tandem mass spectrometry (LC-MS/MS) with derivatization of estrogens by 1,2-dimethylimidazole-5-sulfonyl-chloride (DMIS) to enhance sensitivity. We included four 13 C-labeled internal standards, which corrected for matrix effects when measuring catecholestrogens and methoxyestrogens. This method greatly improves upon our prior protocol, which could measure only four estrogens and included only one deuterated internal standard. Then, the new method was applied to samples from male song sparrows exposed to either a simulated territorial intrusion (STI) or a control condition during the breeding season or non-breeding season. Only estrone and 17β-E2 were present in blood and brain, while the other nine estrogens in the panel were non-detectable. As expected, there was large regional variation in neuroestrogen levels and very low estrogen levels in blood. There was large seasonal variation, and estrogen levels were lower in the non-breeding season. Despite robust aggressive responses to the STI, estrogen levels did not differ between STI and control subjects in either season. In sum, our novel method enables ultrasensitive measurement of eleven estrogens and will be useful for studies of songbirds and other animals.
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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 | 0.000 | 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".