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Record W4404166943 · doi:10.1093/brain/awae350

Exploiting blood-based biomarkers to align preclinical models with human traumatic brain injury

2024· review· en· W4404166943 on OpenAlexaff
Ilaria Lisi, Federico Moro, Edoardo Mazzone, Niklas Marklund, Francesca Pischiutta, Firas Kobeissy, Xiang Mao, Frances Corrigan, Adel Helmy, Fatima Nasrallah, Valentina Di Pietro, Laura B. Ngwenya, Luis Valmor Portela, Bridgette D. Semple, Andrea L.C. Schneider, Ramon Diaz Arrastia, David Menon, Douglas H. Smith, Cheryl L. Wellington, David J. Loane, Kevin Wang, Elisa R. Zanier

Bibliographic record

VenueBrain · 2024
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsTrinity CollegeInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersNational Institute of Neurological Disorders and StrokeMedical Research Council
KeywordsTraumatic brain injuryMedicineNeuroscienceIntensive care medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Rodent models are important research tools for studying the pathophysiology of traumatic brain injury (TBI) and developing new therapeutic interventions for this devastating neurological disorder. However, the failure rate for the translation of drugs from animal testing to human treatments for TBI is 100%. While there are several potential explanations for this, previous clinical trials have relied on extrapolation from preclinical studies for critical design considerations, including drug dose optimization, post-injury drug treatment initiation and duration. Incorporating clinically relevant biomarkers in preclinical studies may provide an opportunity to calibrate preclinical models to identical (or similar) measurements in humans, link to human TBI biomechanics and pathophysiology, and guide therapeutic decisions. To support this translational goal, we conducted a systematic literature review of preclinical TBI studies in rodents measuring blood levels of clinically used GFAP, UCH-L1, NfL, total-Tau (t-Tau) or phosphorylated-Tau (p-Tau) published in PubMed/EMBASE up to 10 April 2024. Although many factors influence clinical TBI outcomes, many of those cannot routinely be assessed in rodent studies (e.g. intracranial pressure monitoring). Thus we focused on blood biomarkers' temporal trajectories and discuss our findings in the context of the latest clinical TBI biomarker data. Of 805 original preclinical studies, 74 met the inclusion criteria, with a median quality score of 5 (25th-75th percentiles: 4-7) on the CAMARADES checklist. GFAP was measured in 43 studies, UCH-L1 in 21, NfL in 20, t-Tau in 19 and p-Tau in seven. Data from rodent models indicate that all biomarkers exhibited injury severity-dependent elevations with distinct temporal profiles. GFAP and UCH-L1 peaked within the first day after TBI (30- and 4-fold increases, respectively, in moderate-to-severe TBI versus sham), with the highest levels observed in the contusion TBI model. NfL peaked within days (18-fold increase) and remained elevated up to 6 months post-injury. GFAP and NfL show a pharmacodynamic response in 64.7% and 60%, respectively, of studies evaluating neuroprotective therapies in preclinical models. However, GFAP's rapid decline post-injury may limit its utility for understanding the response to new therapeutics beyond the hyperacute phase after experimental TBI. Furthermore, as in humans, subacute NfL levels inform on chronic white matter loss after TBI. t-Tau and p-Tau levels increased over weeks after TBI (up to 6- and 16-fold, respectively); however, their relationship with underlying neurodegeneration has yet to be addressed. Further investigation into biomarker levels in the subacute and chronic phases after TBI will be needed to fully understand the pathomechanisms underpinning blood biomarkers' trajectories and select the most suitable experimental model to optimally relate preclinical mechanistic studies to clinical observations in humans. This new approach could accelerate the translation of neuroprotective treatments from laboratory experiments to real-world clinical practices.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.171
GPT teacher head0.413
Teacher spread0.242 · 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 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

Citations28
Published2024
Admission routes1
Has abstractyes

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