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Record W4404214206 · doi:10.1016/j.bpsgos.2024.100415

Small Nucleolar RNAs and the Brain: Growing Evidence Supporting Their Role in Psychiatric Disorders

2024· review· en· W4404214206 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiological Psychiatry Global Open Science · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research Chairs
KeywordsSmall nucleolar RNANeuroscienceLong non-coding RNAPsychologyPsychiatryMedicineBiologyRNAGeneticsGene

Abstract

fetched live from OpenAlex

Noncoding RNAs comprise most of the transcriptome and represent an emerging area of research. Among them, small nucleolar RNAs (snoRNAs) have emerged as a promising target because they have been associated with the development and evolution of several diseases, including psychiatric disorders. snoRNAs are expressed in the brain, with some showing brain-specific expression that indicates specific roles in brain development, function, and dysfunction. However, the role of snoRNAs in conditions that affect the brain needs further investigation to be better understood. This scoping review summarizes existing literature on studies that have investigated snoRNAs in psychiatry and offers insight into potential pathophysiological mechanisms to be further investigated in future research.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0030.002
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 head0.065
GPT teacher head0.379
Teacher spread0.314 · 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