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Record W4402270940 · doi:10.26685/urncst.644

Usage of Psilocybin to Treat Huntington's Disease: A Research Protocol

2024· article· en· W4402270940 on OpenAlexaff
Cynthia Duan, Navid Farkhondehpay, Viana Safa, Emma Yuan

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsilocybinHuntington's diseaseProtocol (science)DiseaseComputer sciencePsychologyMedicineHallucinogenPsychiatryInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Introduction: Huntington’s Disease (HD) is a progressive, neurodegenerative disease that causes significant amounts of neuron death in the brain. It is a genetic disorder, resulting from the over-repetition of the CAG sequence in the gene that codes for the huntingtin protein. Currently, there are no viable cures or treatments to slow or stop the progression of this disorder, making it a target candidate for treatment research. However, given the difficulty and complexity of in treating the genetic cause of HD in adults, a more viable approach may involve treating the resulting neurodegeneration with neuroprotective compounds, such as psilocybin. As such, our study proposes the usage of psilocybin to treat HD due to its neuroprotective, neurotrophic, and neuroplastic effects, resulting in a decreased rate of neuron loss and increased synaptic density. Methods: We propose an in-vivo experiment using several groups of zQ175 knock-in (KI) mice, which will mimic HD in the mice. Following 8 weeks, the mice’s brains will be extracted at different time intervals to analyze the progression of neuronal death using histology and immunohistochemistry, which should inform us about the progression of HD in the different groups of mice. Moreover, throughout this experiment, the motor control of the mice will be observed using the rotarod test, the raised beam test, and the footprint test. Data from these tests will act as behavioral markers for HD, providing an alternate source of information on the progression of HD in the mice. Expected Results: KI mice are expected to have lower rates of neuronal death, higher amounts of synaptic density, and higher scores on average across the three motor behavior tests, compared to the non-treated KI mice. Discussion: These results could provide insight into potential treatments for slowing the progression of HD. If successful, possible next steps could be to determine the efficacy of psilocybin in clinical trials for HD. Conclusion: This study is expected to provide information on the usage of psilocybin as a treatment for HD. If the expected results are obtained, psilocybin may help improve the quality of life for those afflicted with HD.

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)0.019
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0010.006
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 head0.149
GPT teacher head0.553
Teacher spread0.404 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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