MétaCan
Menu
Back to cohort
Record W4392048822 · doi:10.1038/s41598-024-54676-8

Repeated microdoses of LSD do not alter anxiety or boldness in zebrafish

2024· article· en· W4392048822 on OpenAlexafffund
Ethan V. Hagen, Melike Schalomon, Yanbo Zhang, Trevor J. Hamilton

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsMacEwan UniversityWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsBoldnessZebrafishDanioAnxietyAnimal modelPhysiologyPsychologyBiologyMedicineInternal medicinePsychiatryGeneGenetics

Abstract

fetched live from OpenAlex

The therapeutic use of lysergic acid diethylamide (LSD) has resurfaced in the last decade, prompting further scientific investigation into its effectiveness in many animal models. Zebrafish (Danio rerio) are a popular model organism in medical sciences and are used to examine the repeated administration of pharmacological compounds. Previous zebrafish research found acute LSD altered behaviour and cortisol levels at high (250 µg/L) but not low (5-100 µg/L) levels. In this study, we used a motion tracking system to record and analyze the movement patterns of zebrafish after acute and repeated 10-day LSD exposure (1.5 µg/L, 15 µg/L, 150 µg/L) and after seven days of withdrawal. The open-field and novel object approach tests were used to examine anxiety-like behaviour, boldness, and locomotion. In the acute experiments we observed a significant decrease in high mobility with 1.5 µg/L, 15 µg/L, and 150 µg/L of LSD compared to the control and a decrease in velocity with 1.5 and 15 µg/L. In repeated experiments, there were no significant differences in the levels of anxiety, boldness, or locomotion between all LSD groups and controls immediately after 10-day treatment or after withdrawal.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.309
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations11
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicZebrafish Biomedical Research ApplicationsFrench-language works237,207