Genetic Ablation of the Platelet Activating Factor Receptor Does Not Impair Learning and Memory in Wild-Type Mice or Alter Amyloid Plaque Number in a Transgenic Model of Alzheimerâs Disease
Bibliographic record
Abstract
We have recently established that aberrant alkylacylglycerophosphocholine metabolism results in the increased tissue concentration of platelet activating factors (PAFs) in the temporal cortex of Alzheimer Disease (AD) patients and in TgCRND8 mice over-expressing mutant human amyloid precursor protein. PAF lipids activate a G-protein coupled receptor (PAFR) reported to be expressed by microglia and subsets of neurons in rat. It is not known whether this same expression pattern is recapitulated in mice however, as the expression has only been inferred by use of pharmacological PAFR antagonists, many of which impact on both PAFR-dependent and PAFR-independent signalling pathways. PAFR plays a role in long term potentiation (LTP) induction in rats. PAFR has also been implicated in behavioural indices of spatial learning and memory in rats. Contradictory reports using mice provide ambiguity regarding the role of PAFR in LTP induction in mice. To assess whether PAFR is expressed in murine neurons, I localized PAFR mRNA in wild-type C57BL/6 mice using PAFR KO mice as a negative control. I further showed that the loss of PAFR did not impair learning and memory although this assessment must be considered preliminary as the behavioural test employed was not optimized to detect changes in learning and memory of C57BL/6 mice over time adequately.Finally, I showed that the loss of PAFR in TgCRND8 mouse model of AD had no impact upon Aβ plaque number. My observations suggest that PAFR is restricted to microglial-like cells in mouse hippocampus and as such, it may not play a role in learning and memory.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".