Effects of age on mesocorticolimbic testosterone levels and androgen receptors in male rats
Bibliographic record
Abstract
Androgens, such as testosterone (T), are steroid hormones that exert effects on several tissues, including the brain, through interaction with androgen receptors (ARs). In the brain, androgens are traditionally known for modulation of reproductive behaviors mediated by classical regions rich in ARs. However, there is growing recognition of androgen involvement in higher-order cognitive processes, such as executive functions, which are mediated by non-classical brain regions like the prefrontal cortex (PFC), nucleus accumbens (NAc), and ventral tegmental area (VTA), which are part of the mesocorticolimbic system. In males, executive functions and serum T levels decline with age, but it is unclear how age impacts mesocorticolimbic ARs, and also mesocorticolimbic T levels. In these regions, ARs are present, but often at lower abundances per cell, and are difficult to detect immunochemically. Given the lack of information about mesocorticolimbic ARs and T, and how both may be altered by age, the main goals of this thesis were to: (1) improve immunochemical visualization of ARs, (2) phenotype prefrontal AR-expressing cells, and (3) examine how aging affects levels of ARs and neural T. In brief, we use a male rat model to demonstrate superior detection of ARs through application of tyramide signal amplification (TSA), confirm that prefrontal AR-expressing cells are neuronal and not glial, and show region-dependent reductions in ARs and neural T levels with age. More specifically, we show an age-associated decline in serum T and neural T, but an increase in the ratio of neural T: serum T, suggesting partially compensatory T production may occur in the aging brain. We also show an age-associated decrease in the amount of ARs in the PFC, but not the NAc or VTA. We conclude that the observed declines in T and AR levels may contribute to age-related impairment in executive functions. Furthermore, our results also contribute to improved visualization and examination of mesocorticolimbic ARs, and ultimately, a better understanding of the role they play in cognitive processes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".