Edmond Rogers Dissertation, Elucidating pathological correlations between traumatic brain injury and Alzheimer’s Disease
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".