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Addressing the Current Knowledge and Gaps in Research SurroundingLysergic Acid Diethylamide (LSD), Psilocybin, and Psilocin in RodentModels

2023· review· en· W4383301604 on OpenAlexafffund
Udoka C. Ezeaka, Hye Ji J. Kim, Robert B. Laprairie

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2023
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsDalhousie UniversityUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsilocybinLysergic acid diethylamideHallucinogenPharmacologyDosingAnxietyPsychologyDrugPsychiatryMedicineSerotoninInternal medicine

Abstract

fetched live from OpenAlex

Lysergic acid Diethylamide (LSD), psilocybin, and psilocin are being intensively evaluated as potential therapeutics to treat depression, anxiety, substance use disorder, and a host of other psychiatric illnesses. Pre-clinical investigation of these compounds in rodent models forms a key component of their drug development process. In this review, we will summarize the evidence gathered to date surrounding LSD, psilocybin, and psilocin in rodent models of the psychedelic experience, behavioural organization, substance use, alcohol consumption, drug discrimination, anxiety, depression-like behaviour, stress response, and pharmacokinetics. In reviewing these topics, we identify three knowledge gaps as areas of future inquiry: sex differences, oral dosing rather than injection, and chronic dosing regimens. A comprehensive understanding of LSD, psilocybin, and psilocin's in vivo pharmacology may not only lead to their successful clinical implementation but optimize the use of these compounds as controls or references in the development of novel psychedelic therapeutics.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.607
GPT teacher head0.590
Teacher spread0.017 · 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 designOther design
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

Citations1
Published2023
Admission routes2
Has abstractyes

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