Repetitive mild traumatic brain injury causes neuronal damage in the APP/PS1 mouse model of Alzheimer’s disease without an enduring impact on amyloid pathology, sleep, or epileptiform activity
Bibliographic record
Abstract
Abstract Traumatic Brain Injury (TBI) is a known risk factor for Alzheimer’s disease and related neurodegenerative diseases. Sleep disturbances and epileptiform abnormalities can appear after TBI and may contribute to the development of neuropathology. In this study, we characterized sleep, epileptiform activity, and neuropathology after repetitive mild traumatic brain injury (rmTBI) in a mouse model of Alzheimer’s disease. We used the Closed Head Impact Model of Engineered Rotational Acceleration (CHIMERA) to deliver rmTBI or sham (control) treatment to 6-month-old APP/PS1 mice (N=19). One month post-injury, we implanted electroencephalogram (EEG) and electromyographic (EMG) electrodes, recorded for 72 hours, and then collected brain tissue and blood plasma. Our assessment of sleep architecture showed that time spent in vigilance state was not affected by the rmTBI one month post-injury; however, power spectra analysis showed a shift towards higher frequencies in the rmTBI group during non-rapid eye movement (NREM) sleep. Epileptiform activity did not differ between sham and rmTBI. Compared to sham controls, the rmTBI group showed higher neurofilament light (NF-L), but not glial-fibrillary acidic protein (GFAP) in blood plasma and no change in Aβ pathology. These results indicate sustained neurological injury in the APP/PS1 mice one month after rmTBI without affecting amyloid deposition in the brain. Our study suggests that rmTBI can induce neural injury without causing enduring sleep disruption, seizures, and exacerbation of amyloidosis in the APP/PS1 mouse model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".