MétaCan
Menu
Back to cohort
Record W2765919764 · doi:10.1016/j.jalz.2017.06.1407

[P3–195]: PROFILING EXOSOMAL MIRNAS IN EARLY ONSET ALZHEIMER's DISEASE CEREBROSPINAL FLUID

2017· article· en· W2765919764 on OpenAlexaff
Paul M. McKeever, Raphaël Schneider, Namita Multani, Foad Taghdiri, Robert A. Brown, Adam L. Boxer, Janice Robertson, Maria Carmela Tartaglia

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill UniversityUniversity Health NetworkUniversity of TorontoMontreal Neurological Institute and HospitalOccupational Cancer Research Centre
Fundersnot available
KeywordsmicroRNAMicrovesiclesBiologyExosomeCerebrospinal fluidEarly-onset Alzheimer's diseaseDiseaseMicroarrayGeneticsBioinformaticsMedicineDementiaGeneInternal medicineGene expressionNeuroscience

Abstract

fetched live from OpenAlex

Early onset Alzheimer's disease (EOAD) occurs before the age of 65 and represents 5–10% of all AD cases. EOAD and late onset AD share amyloid and tau pathology but there are important differences between the two with respect to age of onset, speed of progression and presenting symptoms. Since EOAD is only rarely caused by autosomal dominant mutation inheritance, there are currently no distinguishing biomarkers for EOAD alone. MicroRNAs (miRNAs) are short non-coding RNAs involved in the degradation or translational repression of several mRNA targets. MiRNAs are packaged into exosomes, released by cells into the extracellular milieu and biofluids, including cerebrospinal fluid (CSF), and are implicated in cell-to-cell communication. We hypothesized that uncovering significantly altered exosomal miRNAs in the CSF of EOAD patients would reveal novel biomarkers, provide insight into disease mechanism, and guide therapeutic development. CSF was obtained by lumbar puncture from patients diagnosed with EOAD (n=17, 60.88±4.62 years) and healthy controls (n=12; 67.08±7.83 years). The miRCURYExosome Isolation Kit (Exiqon) was used to isolate exosomes from 1.0mL of input CSF. Subsequently, the miRNA was purified from each exosomal prep and converted into cDNA. For the discovery phase of the study, each sample underwent real-time PCR on two 384 well plates with 752 miRNA primer sets (Exiqon Human Panels I and II), whereby candidate differentially expressed miRNAs were revealed. For the validation phase, candidate miRNAs are being confirmed by independent real-time PCR experiments. Preliminary data from the discovery phase revealed that of 52 miRNAs expressed in at least 75% of all samples, 7 were up-regulated and 10 were down-regulated in the exosomal CSF from EOAD compared with healthy controls (all p<0.05). To date, the down-regulation of miR-451a in EOAD has been validated (p<0.01). Computational modeling is being performed to assess whether multiple differentially expressed miRNAs can predict EOAD diagnosis. Also, a bioinformatics analysis is being conducted to identify putative mRNA targets and pathways altered in EOAD. This study identified novel differentially expressed exosomal miRNAs from CSF isolated from EOAD patients. These miRNAs may differentiate EOAD from other forms of AD, elucidate disease mechanism, and contribute novel targets for 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 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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 designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicExtracellular vesicles in diseaseFrench-language works237,207