Trace residue identification, characterization, and longitudinal monitoring of the novel synthetic opioid β‐U10, from discarded drug paraphernalia
Bibliographic record
Abstract
Empirical data regarding dynamic alterations in illicit drug supply markets in response to the COVID-19 pandemic, including the potential for introduction of novel drug substances and/or increased poly-drug combination use at the "street" level, that is, directly proximal to the point of consumption, are currently lacking. Here, a high-throughput strategy employing ambient ionization-mass spectrometry is described for the trace residue identification, characterization, and longitudinal monitoring of illicit drug substances found within >6,600 discarded drug paraphernalia (DDP) samples collected during a pilot study of an early warning system for illicit drug use in Melbourne, Australia from August 2020 to February 2021, while significant COVID-19 lockdown conditions were imposed. The utility of this approach is demonstrated for the de novo identification and structural characterization of β-U10, a previously unreported naphthamide analog within the "U-series" of synthetic opioid drugs, including differentiation from its α-U10 isomer without need for sample preparation or chromatographic separation prior to analysis. Notably, β-U10 was observed with 23 other drug substances, most commonly in temporally distinct clusters with heroin, etizolam, and diphenhydramine, and in a total of 182 different poly-drug combinations. Longitudinal monitoring of the number and weekly "average signal intensity" (ASI) values of identified substances, developed here as a semi-quantitative proxy indicator of changes in availability, relative purity and compositions of street level drug samples, revealed that increases in the number of identifications and ASI for β-U10 and etizolam coincided with a 50% decrease in the number of positive detections and an order of magnitude decrease in the ASI for heroin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".