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Record W2924008813 · doi:10.1038/s41398-019-0459-9

The emerging role of exosomes in mental disorders

2019· review· en· W2924008813 on OpenAlexafffund
Saumeh Saeedi, Sonia Israel, Corina Nagy, Gustavo Turecki

Bibliographic record

VenueTranslational Psychiatry · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health ResearchCanada Research ChairsGovernment of Canada
KeywordsSchizophrenia (object-oriented programming)MicrovesiclesMedicinePsychiatryMental healthPsychologyBiologymicroRNAGenetics

Abstract

fetched live from OpenAlex

Exosomes are a class of extracellular vesicles of endocytic origin, which are released by cells and are accessible in biofluids, such as saliva, urine, and plasma. These vesicles are enriched with small RNA, and they play a role in many physiological processes. In the brain, they are involved in processes including synaptic plasticity, neuronal stress response, cell-to-cell communication and neurogenesis. While exosomes have been implicated previously in cancer and neurodegenerative diseases, research regarding their role in mental disorders remains scarce. Given their functional significance in the brain, investigation in this field is warranted. Additionally, because exosomes can cross the blood-brain barrier, they may serve as accessible biomarkers of neural dysfunction. Studying exosomes may provide information towards diagnosis and therapeutic intervention, and specifically those derived from the brain may provide a mechanistic view of the disease phenotype. This review will discuss the roles of exosomes in the brain, and relate novel findings to current insights into mental disorders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.309
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations392
Published2019
Admission routes2
Has abstractyes

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