Neuronal reprogramming improves brain health in TgAD and aging rats
Bibliographic record
Abstract
BACKGROUND: Cognitive decline, however, is not driven by disturbances in a few synapses in isolation, but by an aberration in the functioning of the entire neuronal network, which is assembled through interactions between excitatory, inhibitory and neuromodulatory cells. As disease progresses, loss of specific populations of neurons exacerbates cognitive function and renders a point of no return for present therapeutic interventions. Furthermore the neuroinflammatory response within the brain contributes to ongoing neuronal damage, and thus strategies to limit toxic inflammatory responses should benefit also slow disease progression. Our overall goal is to utilize neuronal reprogramming of toxic astrocytes into neurons as a means of decreasing neuroinflammation, stabilizing neuronal network function and ultimately cognition. METHOD: To elicit neuronal reprograming, proneural transcription factors (TFs) ASCL-1 or mutant ASCL-1 SA6 were expressed in 14- and 18-month-old TgF344 AD and their non-transgenic littermate rats using an AAV2/5 vector under a GFAP promoter. TFs were unilaterally injected into the hippocampal hilus and rats were monitored for behavior at seven weeks post TF delivery before pathological examination. Pathological and cognitive function was examined 7 weeks post viral injection. RESULT: We demonstrate a significant improvement in cognitive function 7 weeks after neuronal reprogramming, with a subsequent improvement in astrogliosis, microgliosis, neuronal density with a concomitant decrease in amyloid plaque load. CONCLUSION: Neuronal reprogramming decreases neuroinflammation and increases survival of neurons within the hippocampus of both TgF344 AD and aging F344 non-transgenic littermates. We demonstrate the return to a more homeostatic brain environment post-neuronal reprogramming in middle-aged and older rats.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".