Preventing synaptic deficits in Alzheimer’s disease by inhibiting tumor necrosis factor alpha signaling
Bibliographic record
Abstract
The characterization of preclinical stages of Alzheimer's disease (AD) would provide a therapeutic window for prevention. One of the challenges of developing preventive therapy for AD is to identify early biomarkers for intervention studies. We have recently shown that in the TgCRND8 transgenic AD mouse model, increased hippocampal levels of the pro-inflammatory cytokine tumor necrosis factor alpha (TNFα) and enhanced excitatory synaptic transmission were early-onset changes that occurred weeks before amyloid plaque formation. Inhibiting TNFα before plaque formation not only normalized excitatory synaptic function, but also prevented the impairment of synaptic function 4 months later. In this review paper, we will examine the potential contributions of TNFα to the alteration of brain function in preclinical AD. The prospective use of TNFα inhibitors for preventing AD will be discussed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".