Detection of Acute Diazepam Exposure in Bone and Marrow: Influence of Tissue Type and the Dose‐Death Interval on Sensitivity of Detection by ELISA with Liquid Chromatography Tandem Mass Spectrometry Confirmation*
Bibliographic record
Abstract
Enzyme-linked immunosorbent assay (ELISA) and liquid chromatography tandem mass spectrometry (LC/MS/MS) were used to detect diazepam exposure in skeletal tissues of rats (n = 15) given diazepam acutely (20 mg/kg, i.p.), and killed at various times postdose. Marrow, epiphyseal, and diaphyseal bone were isolated from extracted femora. Bone was cleaned, ground, and incubated in methanol. Marrow underwent ultrasonic homogenization. Extracts and homogenates were diluted in phosphate buffer, and then underwent solid-phase extraction and ELISA. Relative sensitivity of detection was examined in terms of relative decrease in absorbance (ELISA) and binary classification sensitivity (ELISA and LC/MS/MS). Overall, the data showed differences in relative sensitivity of detection of diazepam exposure in different tissue types (marrow > epiphyseal bone > diaphyseal bone), which is suggestive of heterogenous distribution in these tissues, and a decreasing sensitivity with increasing dose-death interval. Thus, the tissue type sampled and dose-death interval may contribute to the probability of detection of diazepam exposure in skeletal tissues.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".