Effects of Tissue Type and the Dose-Death Interval on the Detection of Acute Ketamine Exposure in Bone and Marrow with Solid-Phase Extraction and ELISA with Liquid Chromatography-Tandem Mass Spectrometry Confirmation
Bibliographic record
Abstract
Ketamine exposure was detected in skeletal tissues by ELISA and liquid chromatography-tandem mass spectrometry (LC-MS-MS). Rats (n = 9) received ketamine hydrochloride acutely (75 mg/kg, i.p.) and were euthanized within 15, 30, or 90 min. Drug-free control animals (n = 3) were also euthanized. Extracted femora were separated into epiphyseal and diaphyseal fragments, with marrow isolated from the medullary cavity. Bone was ground and incubated in methanol. Extracts were dried and reconstituted in phosphate buffer (0.1 M, pH 7.3), and marrow was homogenized in alkaline solution. Both then underwent solid-phase extraction. Extracts were assayed by ELISA, with data expressed in terms of relative decrease in absorbance (%DA, drug-positive tissues vs. matrix-matched drug-free controls) and binary classification test sensitivity (S). Generally, %DA decreased in the order of marrow > epiphyseal bone > diaphyseal bone, and was negatively correlated with dose-death interval (DDI). Measured S values were 100% in ELISA analysis of extracts of all tissue types. Sensitivity values were computed from LC-MS-MS data using a 5 ng/mL cutoff. Sensitivity values for ketamine detection were 100%, 0–100% and 0%, at the 15, 30, and 90 min DDI, respectively, and sensitivity values for norketamine detection were 0–66%, 0–66%, and 0% at the 15, 30, and 90 min DDI, respectively. These results suggest that the tissue type sampled and DDI may influence the sensitivity of detection of ketamine 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) | 0.001 | 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) | 0.001 | 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".