Novel Tools for Visualizing Dynamics of Steroid Hormone Response
Bibliographic record
Abstract
Abstract Text Reproductive steroid hormones, such as estrogen, powerfully and dynamically link various aspects of physiology, behavior and disease with reproductive state. These hormones communicate with a cell via specialized receptors, which bind to response elements within DNA and lead to transcriptomic changes. Current methods to measure steroid hormone signaling rely on measuring a cell’s sensitivity to a given hormone- that is, they assess the presence of the steroid hormone receptor. However, these methods fail to account for variations in the responsiveness of a cell to a steroid- the level at which the steroid hormone-receptor complex ultimately binds to response elements within DNA. Therefore, current strategies leave us unable to accurately detect the dynamics of the transcriptional changes through which steroid hormones exert their powerful downstream effects within a cell. We propose a novel tool that will dynamically measure the responsiveness of a cell to steroid hormones, variations of which can examine individual cell types of interest. We have designed a custom AAV ERE (estrogen response element) reporter that has successfully demonstrated the ability to detect changes in estrogen responsiveness in the brain of female mice. A CRE-dependent portion of the tool reports successful cellular uptake within cell types of interest with red fluorescence, while a destabilized green fluorescent protein within the tool visualizes the presence and level of recent ERE-dependent estrogen signaling. The tool successfully detected changes in estrogen responsiveness in the physiological context of the estrus cycle of female mice, and revealed differences in responsiveness across different brain regions not directly proportional to the level of estrogen present. Given the initial success of the core elements of the reporter, work is underway to design and test variants of the tool that can detect dynamic changes in estrogen responsiveness in various tissue types and that will be compatible with other in vivo imaging methods. Additionally, variants are being created that will similarly detect changes in responsiveness for other steroid hormones including progesterone, testosterone, glucocorticoids, and mineralocorticoids. These tools will allow us to ask new questions, expanding our understanding of the dynamic features of steroid hormone signaling. Date of Presentation October 16, 2024
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".