Mechanisms of Neurorespiratory Toxicity Induced by Fentanyl Analogs—Lessons from Animal Studies
Bibliographic record
Abstract
In 2020, fentanyl and its analogs contributed to ~65% of drug-attributed fatalities in the USA, with a threatening increasing trend during the last ten years. These synthetic opioids used as potent analgesics in human and veterinary medicine have been diverted to recreational aims, illegally produced and sold. Like all opioids, central nervous system depression resulting from overdose or misuse of fentanyl analogs is characterized clinically by the onset of consciousness impairment, pinpoint miosis and bradypnea. However, contrasting with what observed with most opioids, thoracic rigidity may occur rapidly with fentanyl analogs, contributing to increasing the risk of death in the absence of immediate life support. Various mechanisms have been proposed to explain this particularity associated with fentanyl analogs, including the activation of noradrenergic and glutamatergic coerulospinal neurons and dopaminergic basal ganglia neurons. Due to the high affinities to the mu-opioid receptor, the need for more elevated naloxone doses than usually required in morphine overdose to reverse the neurorespiratory depression induced by fentanyl analogs has been questioned. This review on the neurorespiratory toxicity of fentanyl and analogs highlights the need for specific research focused on these agents to better understand the involved mechanisms of toxicity and develop dedicated strategies to limit the resulting fatalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".