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Record W6889068889 · doi:10.25394/pgs.22592626

Edmond Rogers Dissertation, Elucidating pathological correlations between traumatic brain injury and Alzheimer’s Disease

2023· dissertation· en· W6889068889 on OpenAlexfundno aff

Bibliographic record

VenueOpen MIND · 2023
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersSchool of Medicine, Indiana UniversityNational Institutes of HealthPetroleum Technology Alliance CanadaUniversity of North TexasDirectorate for Biological SciencesNational Center for Advancing Translational SciencesPurdue University
KeywordsDiseaseNeurodegenerationSubclinical infectionTraumatic brain injuryPathologicalRodent modelPoison control

Abstract

fetched live from OpenAlex

Traumatic Brain Injuries (TBI) are a major cause of disability and death in the United States. One of the greatest consequences of the disease is the resulting long-term damage, especially in milder injury cases where the damage is initially subclinical and thus lacking acutely observable manifestations that over time can compound significantly. Among these chronic issues, Alzheimer’s Disease (AD) is one of the most serious. While multiple studies demonstrate an increased likelihood of developing neurodegenerative diseases in response to TBI, the underlying mechanisms remain undefined and no current treatment options are available. Multiple hypotheses have been postulated based on various animal and clinical models, which have contributed a great deal to our current knowledge base and implicated several targets of interest in this pathway (i.g. oxidative stress, inflammation, disruptions in proteostasis). While extremely valuable, these in vivo procedures and analyses are physiologically and ethically complex: there is currently no model capable of separating and visualizing TBI-induced sub-cellular damage in the moments (seconds) immediately following injury, and the subsequent associated long-term changes (AD). In addition, no mechanistic study has been performed to link mechanical-trauma independently with neurodegeneration initiation via protein aggregation. It is clear that additional investigative tools are needed to rectify these intricate issues, and while in vitro methodologies generally offer the type of resolution required, no such model replicates these phenomena. Therefore, we introduce the “TBI-on-a-chip” in vitro concussive model, with a series of concomitant targeted-experiments to address this urgent, currently unmet need. This dissertation work describes the development of our cellular trauma model, featuring a multi-disciplinary approach that provides investigatory opportunities into cellular mechanics, molecular biology, functional alterations (electrophysiology), and morphology, in both primary and secondary injury. Utilizing this model, we directly observe evidence of impact-induced electrical/functional and biochemical consequences, in addition to isolating oxidative stress as a key, contributing component. Taken together, these collective efforts suggest that oxidative stress may be a viable target for both acute and chronic potential therapeutic interventions.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.006

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 head0.094
GPT teacher head0.391
Teacher spread0.297 · 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
GenreOther

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

Explore more

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