Simultaneous extraction and detection of peptides, steroids, and proteins in small tissue samples
Bibliographic record
Abstract
The detection and quantification of hormones are important to assess the reproductive and stress status of experimental models and for the diagnosis of diseases in human and veterinary clinics. Traditionally, steroid, peptide, and protein hormones are analyzed in individual experiments using different extraction methodologies. With the new advancement on HPLC sorbents, the simultaneous measurement of hormones from different categories becomes possible. In this study, we present a novel sample processing strategy for the simultaneous extraction and detection of peptides, steroids, and proteins using high-resolution liquid chromatography tandem mass spectrometry. We demonstrate the sensitivity of our method for small tissues by acquiring data from brain, pituitary gland, and gonads of single zebrafish samples. This approach promises to shed light on the hormonal pathways and their interrelationships, providing knowledge on the integration of hormone systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".