Steroid profiling of glucocorticoids in microdissected mouse brain across development
Bibliographic record
Abstract
Corticosterone is produced by the adrenal glands and also produced locally by other organs, such as the brain. Local levels of corticosterone in specific brain regions during development are not known. Here, we microdissected brain tissue and developed a novel liquid chromatography tandem mass spectrometry method (LC-MS/MS) to measure a panel of seven steroids (including 11-deoxycorticosterone (DOC), corticosterone, and 11-dehydrocorticosterone (DHC) in the blood, hippocampus (HPC), cerebral cortex (CC), and hypothalamus (HYP) of mice at postnatal day (PND) 5, 21, and 90. In a second cohort of mice, we measured the expression of three genes that code for steroidogenic enzymes that regulate corticosterone levels (Cyp11b1, Hsd11b1, and Hsd11b2) in the HPC, CC, and HYP. There were region-specific patterns of steroid levels across development, including higher corticosterone levels in the HPC and HYP than in the blood at PND5. In contrast, corticosterone levels were higher in the blood than in all brain regions at PND21 and PND90. Brain corticosterone levels were not positively correlated with blood corticosterone levels, and correlations across brain regions increased with age. Local corticosterone levels were best predicted by local DOC levels at PND5, but by local DHC levels at PND21 and PND90. Transcripts for the three enzymes were detectable in all samples (with highest expression of Hsd11b1) and showed region-specific changes with age. These data demonstrate that individual brain regions fine-tune local levels of corticosterone during early development and that coupling of glucocorticoid levels across regions increases with age.
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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) | 0.001 | 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 | 0.001 |
| 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".