Discrimination Between Patterns of Drug Exposure by Toxicological Analysis of Decomposed Skeletal Tissues. Part II: Amitriptyline and Citalopram
Bibliographic record
Abstract
Decomposed bone and plasma samples of rats exposed to amitriptyline (AMI) and citalopram (CIT) under different dosing patterns were analyzed. Wistar rats received one acute dose (120 mg AMI/kg and 40 mg CIT/kg; n = 5) or two doses (40 mg AMI/kg and 13 mg CIT/kg, n = 5) 40 min apart. After collection of perimortem blood, the rats were euthanized and placed outdoors to decompose to skeleton. Recovered bone was ground and subjected to methanolic extraction. Bone extracts and plasma samples underwent solid-phase extraction and were analyzed using ultra-high-performance liquid chromatography. Concentrations of drugs and the primary metabolites [nortriptyline (NORT), desmethylcitalopram (DMCIT) and didesmethylcitalopram (DDMCIT)] were expressed as mass-normalized response ratios (RR/m). Concentrations (RR/m) of AMI, CIT and metabolites did not differ significantly between exposure types in plasma and all bone types examined or for the pooled bone samples (P > 0.05). However, ratios of concentrations of NORT to those of AMI differed significantly between exposure patterns for all bone types except for rib (P < 0.05). Values of DMCIT/CIT differed significantly between exposure patterns in rib, pelvi and femora (P < 0.05). Values of DDMCIT/CIT did not differ significantly between exposure types (P > 0.05), while those of DDMCIT/DMCIT were significantly different for all bones except the vertebrae and rib (P < 0.05).
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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) | 0.000 | 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".