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Record W4322771664 · doi:10.3390/ph16030382

Mechanisms of Neurorespiratory Toxicity Induced by Fentanyl Analogs—Lessons from Animal Studies

2023· review· en· W4322771664 on OpenAlexaff
Karam Chamoun, L Chevillard, Aline Hajj, Jacques Callebert, Bruno Mégarbane

Bibliographic record

VenuePharmaceuticals · 2023
Typereview
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFentanylMedicinePharmacologyOpioid(+)-NaloxoneDopaminergicMorphineToxicityAnesthesiaDopamineReceptorInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.552
GPT teacher head0.550
Teacher spread0.002 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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