Increased excitability of dentate gyrus mossy cells occurs early in life in the Tg2576 model of Alzheimer’s disease
Bibliographic record
Abstract
INTRODUCTION: Hyperexcitability in Alzheimer's disease (AD) emerge early and contribute to disease progression. The dentate gyrus (DG) is implicated in hyperexcitability in AD. We hypothesized that mossy cells (MCs), regulators of DG excitability, contribute to early hyperexcitability in AD. Indeed, MCs generate hyperexcitability in epilepsy. METHODS: Using the Tg2576 model and WT mice (∼1month-old), we compared MCs electrophysiologically, assessed c-Fos activity marker, Aβ expression and mice performance in a hippocampal-dependent memory task. RESULTS: Tg2576 MCs exhibit increased spontaneous excitatory events and decreased inhibitory currents, increasing the charge transfer excitation/inhibition ratio. Tg2576 MC intrinsic excitability was enhanced, and showed higher c-Fos, intracellular Aβ expression, and axon sprouting. Granule cells only showed changes in synaptic properties, without intrinsic changes. The effects occurred before a memory task is affected. DISCUSSION: Early electrophysiological and morphological alterations in Tg2576 MCs are consistent with enhanced excitability, suggesting an early role in DG hyperexcitability and AD pathophysiology. HIGHLIGHTS: ∘ MCs from 1 month-old Tg2576 mice had increased spontaneous excitatory synaptic input. ∘ Tg2576 MCs had reduced spontaneous inhibitory synaptic input. ∘ Several intrinsic properties were abnormal in Tg2576 MCs. ∘ Tg2576 GCs had enhanced synaptic excitation but no changes in intrinsic properties. ∘ Tg2576 MCs exhibited high c-Fos expression, soluble Aβ and axonal sprouting.
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 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 | 0.001 | 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".