MSR1 and NEP Are Correlated with Alzheimer’s Disease Amyloid Pathology and Apolipoprotein Alterations
Bibliographic record
Abstract
In mouse models of amyloidosis, macrophage receptor 1 (MSR1) and neprilysin (NEP) have been shown to interact to reduce amyloid burden in the brain.The purpose of this study is to analyze these two gene products in combination with apolipoproteins and Aβ1-42 in the cerebrospinal fluid (CSF) and plasma of individuals at different stages of Alzheimer's disease (AD), as well as in autopsied brain samples from ROSMAP (Religious Orders Study and Memory and Aging Project).CSF/plasma levels of MSR1 and NEP were measured using the sensitive primer extension assay technology. CSF Aβ1-42 was assessed with ELISA, while CSF ApoE and ApoJ were measured with the Luminex's multiplex technology. Brain MSR1, APOE, and CLU (APOJ) mRNA levels were measured with RNA-Seq and contrasted to amyloid plaques pathology using CERAD staging.While plasma and CSF MSR1 levels are significantly correlated, this correlation was not observed for NEP. In addition to be highly correlated to one another, CSF levels of both MSR1 and NEP are strongly correlated with AD status and CSF Aβ1-42, ApoE, and ApoJ levels. In the cortical tissues of subjects from ROSMAP, MSR1 mRNA levels are correlated with CLU mRNA levels and the CERAD scores but not with APOE mRNA levels.The discrepancies observed between CSF/plasma levels of MSR1 and NEP with CSF Aβ1-42 and ApoE concentrations can be explained by many factors, such as the disease stage or the involvement of the blood-brain barrier breakdown that leads to the infiltration of peripheral monocytes or macrophages.
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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) | 0.000 | 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) | 0.001 | 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".