Detection of in utero Exposure to Cannabis in Paired Umbilical Cord Tissue and Meconium by Liquid Chromatography-Tandem Mass Spectrometry
Bibliographic record
Abstract
Understanding levels of in utero drug exposure is important to properly customize the immediate, as well as ongoing, medical and social management needs of affected newborns. Here, we present the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the detection and quantification of 4 cannabinoid analytes in two neonatal matrices. The analytes targeted were Δ 9 -tetrahydrocannabinal (THC), 11-nor-9-carboxy-THC (THCA), 11-hydroxy-THC (11-OH-THC), and cannabinol (CBN). The matrices analyzed were umbilical cord tissue and meconium. A fifth analyte, cannabidiol (CBD), was also detected uniquely in meconium. Extracts were analyzed by LC-MS/MS in negative electrospray ionization mode. Paired meconium and umbilical cord samples (i.e., one specimen from each matrix collected from each single birth, n = 46 pairs) were tested to evaluate concentration and metabolite profiles. THCA was detected in all positive (containing one or more analytes) meconium samples (n = 32). CBN, THC, 11-OH-THC, and CBD were present in 57% (n = 26), 39% (n = 18), 24% (n = 11), and 20% (n = 9), respectively. Concentrations were lower in the umbilical cord samples for all analytes (i.e., 0.27–537 ng/g for meconium and 0.1–9 ng/g for umbilical cord). In umbilical cord THCA was also detected in all positive samples (n = 19) while THC, CBN, and 11-OH-THC were present in 24% (n = 11), 17% (n = 8), and 11% (n = 5), respectively. Testing neonatal matrices for cannabinoids could be used to support studies designed to detect newborns exposed to cannabis in utero , as well as provide data that could be examined for correlations with clinical and social outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".