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Record W4390198920 · doi:10.1002/alz.077461

Next generation humanized mice to investigate how Traumatic Brain Injury interacts with ApoE4 genetic risk factor to accelerate dementia

2023· article· en· W4390198920 on OpenAlexaff
Khashayar TK Khasheeipour, Arthur Brown, Marco A. M. Prado

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern UniversityRobarts Clinical Trials
Fundersnot available
KeywordsTraumatic brain injuryApolipoprotein EDementiaNeuroscienceMedicineDiseaseNeuroinflammationPathologicalAmyloid betaAmyloid precursor proteinAlzheimer's diseaseBioinformaticsPsychologyPathologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Traumatic brain injury (TBI) is among the leading causes of disability and has been strongly linked to Alzheimer’s disease (AD), the most prevalent cause of dementia. Patients with a history of repeated TBI can present pathologies and symptoms similar to AD: Tau protein neurofibrillary tangles, Amyloid Beta Plaques, and chronic inflammation. We propose that TBI triggers pathological processes that underlie AD development and thereby accelerates AD’s development and severity. Apolipoprotein E4 (ApoE4) protein has been thought to play a central role in several processes underlying AD development. ApoE4 is one of the biggest genetic risk factors for AD, which is also linked to worse short‐term and long‐term outcomes after TBI. However, how ApoE4 mechanistically affects the development of AD‐pathology after TBI has yet to be understood Method To explore this gap, we will use mice with humanized genes which develop AD like pathology with age (Late‐onset AD). The humanized genes include amyloid precursor protein (hAPP) and microtubule‐associated protein tau (hMAPT) ‐ precursors to amyloid beta plaques and neurofibrillary tangles, respectively ‐ in addition to two copies of apolipoprotein E4 (ApoE4) or the control ApoE3 allele. Half of these mice will experience three mild TBIs modeled after human concussions, while the other half will only be anesthetized. All mice will undergo behavioral testing to measure cognitive deficits and molecular analyses to characterize the timing and severity of AD pathology. Cognitive testing will be carried out using the rodent touchscreen Continuous Performance Test (rCPT), which are widely used with AD patients to test attentional processes. Result Preliminary results from rCPT before TBI, reveal no significant differences between genotypes and sex at 6‐month of age. Conclusion We aim to uncover how TBI interacts with ApoE4 protein to accelerate AD pathology at the molecular and behavioral levels. To maximize the clinical relevance of this project, we use mice with human genes implicated in AD, a clinically relevant noninvasive model of concussion, and touchscreen cognitive testing identical to touchscreen testing in humans. We aim to bridge the gap between animal models and clinical settings to identify future therapeutic targets for TBI and AD.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.196
GPT teacher head0.358
Teacher spread0.162 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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