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Record W2920403745 · doi:10.1089/neu.2018.6256

MicroRNA Biomarkers in Cerebrospinal Fluid and Serum Reflect Injury Severity in Human Acute Traumatic Spinal Cord Injury

2019· article· en· W2920403745 on OpenAlexaff
Seth Tigchelaar, Rishab Gupta, Casey P. Shannon, Femke Streijger, Sunita Sinha, Stéphane Flibotte, Michael A. Rizzuto, John Street, Scott Paquette, Tamir Ailon, Raphaële Charest-Morin, Nicolas Dea, Charles G. Fisher, Marcel F. Dvorak, Sanjay S. Dhall, Jean‐Marc Mac‐Thiong, Stefan Parent, Christopher S. Bailey, Sean Christie, Kendall Van Keuren‐Jensen, Corey Nislow, Brian K. Kwon

Bibliographic record

VenueJournal of Neurotrauma · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsVictoria HospitalCentre Hospitalier Universitaire Sainte-JustineDalhousie UniversityPrevention of Organ FailureHôpital du Sacré-Cœur de MontréalInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsMedicineBiomarkerSpinal cord injuryCerebrospinal fluidTraumatic brain injuryInternal medicinePathologySpinal cordOncologyBioinformatics

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) is a devastating condition with variability in injury mechanisms and neurologic recovery. Spinal cord impairment after SCI is measured and classified by a widely accepted standard neurological examination. In the very acute stages post-injury, however, this examination is extremely challenging (and often impossible) to conduct and has modest prognostic value in terms of neurological recovery. The lack of objective tools to classify injury severity and predict outcome is a barrier for clinical trials and thwarts development of therapies for those with SCI. Biological markers (biomarkers) represent a promising, complementary approach to these challenges because they represent an unbiased approach to classify injury severity and predict neurological outcome. Identification of a suitable panel of molecular biomarkers would comprise a fundamental shift in how patients with acute SCI are evaluated, stratified, and treated in clinical trials. MicroRNA are attractive biomarker candidates in neurological disorders for several reasons, including their stability in biological fluids, their conservation between humans and model mammals, and their tissue specificity. In this study, we used next-generation sequencing to identify microRNA associated with injury severity within the cerebrospinal fluid (CSF) and serum of human patients with acute SCI. The CSF and serum samples were obtained 1-5 days post-injury from 39 patients with acute SCI (24 American Spinal Injury Association Impairment Scale [AIS] A, 8 AIS B, 7 AIS C) and from five non-SCI controls. We identified a severity-dependent pattern of change in microRNA expression in CSF and identified a set of microRNA that are diagnostic of baseline AIS classification and prognostic of neurological outcome six months post-injury. The data presented here provide a comprehensive description of the CSF and serum microRNA expression changes that occur after acute human SCI. This data set reveals microRNA candidates that warrant further evaluation as biomarkers of injury severity after SCI and as key regulators in other neurological 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.075
GPT teacher head0.421
Teacher spread0.346 · 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 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

Citations62
Published2019
Admission routes1
Has abstractyes

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