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Record W4411670532 · doi:10.1038/s44303-025-00096-w

Aldehydic load as an objective imaging biomarker of mild traumatic brain injury

2025· article· en· W4411670532 on OpenAlexafffund
Alexia Kirby, Cian Ward, Clara S. Goulet, Ryan Daniel, Joseph Wai-Hin Leung, Ashwin Sharma, Mojmı́r Suchý, Cassandra M. Donatelli, Jing Wang, Emily M. Standen, Adam J. Shuhendler

Bibliographic record

Venuenpj Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institutes of Health ResearchGovernment of Ontario
KeywordsTraumatic brain injuryBiomarkerMedicineNeuroimagingNeurosciencePsychologyChemistryPsychiatryBiochemistry

Abstract

fetched live from OpenAlex

Mild traumatic brain injury (mTBI) is neurological impairment induced by biomechanical forces without structural brain damage, currently without an objective diagnostic tool. Downstream injury stems from oxidative damage leading to the production of neurotoxic aldehydes. A collagen-based 3D corticomimetic in vitro model of concussion was developed, confirming aldehyde production following impact. Total aldehyde levels were mapped in vivo following mTBI using a novel CEST-MRI contrast agent, ProxyNA 3 , in a new model of closed-head, awake, single-impact concussion in aged and young mice with aldehyde dehydrogenase 2 (ALDH2) deficiency. ProxyNA 3 -MRI was performed before impact, and on days two- and seven- post-impact. MRI signal enhancement significantly increased at two days post-injury prior to astrocyte activation at seven days post-injury. The data suggest that advanced age and ALDH2 deficiency contribute to increased aldehydic load following mTBI. Overall, ProxyNA 3 was capable of mapping concussion-associated aldehydes, supporting its application as an objective diagnostic tool for concussion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.329
Teacher spread0.306 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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