Inhibiting tumor necrosis factor-α before amyloidosis prevents synaptic deficits in an Alzheimer's disease model
Bibliographic record
Abstract
Deficits in synaptic structure and function are likely to underlie cognitive impairments in Alzheimer's disease. While synaptic deficits are commonly found in animal models of amyloidosis, it is unclear how amyloid pathology may impair synaptic functions. In some amyloid mouse models of Alzheimer's disease, however, synaptic deficits are preceded by hyperexcitability of glutamate synapses. In the amyloid transgenic mouse model TgCRND8, we therefore investigated whether early enhancement of glutamatergic transmission was responsible for development of later synaptic deficits. Hippocampi from 1-month-old TgCRND8 mice revealed increased basal transmission and plasticity of glutamate synapses that was related to increased levels of tumor necrosis factor α (TNFα). Treating these 1-month-old mice for 4 weeks with the TNFα inhibitor XPro1595 prevented synaptic deficits otherwise apparent at the age of 6 months. In this mouse model at least, reversing the hyperexcitability of glutamate synapses via TNFα blockade before the onset of amyloid plaque formation prevented later synaptic deficits.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".