HUMAN CIRCULATING MONOCYTES PHENOTYPES AND FUNCTIONS IN THE DEVELOPMENT OF ALZHEIMER’S DISEASE
Bibliographic record
Abstract
Alzheimer’ Disease is the most frequent neurocognitive disorder. The exact cause is not known however the neuroinflammation plays a key role. This neuroinflammation is more probably preceding the amyloid beta deposition in senile plaques. The innate immune system is playing a significant role in the neuroinflammation either in the brain (microglia) or in the periphery (monocytes). Our aim in the present work was to investigate the phenotypic and functional changes of monocytes in the progression of Alzheimer’s disease (AD). We evaluated four groups of subjects: healthy (HE), subjective memory complaint (SMC), amnestic Mild Cognitive Impairment (aMCI) and mildAD subjects (aged 60 to 85 years). We had 10 subjects per group. Monocytes were separated and studied by FACScan for their phenotypes and functions. Our results demonstrate that monocytes have a gradient of inflammatory phenotype (intermediate and non-classical) through the progression of the disease from HE to mAD subjects. The functions of monocytes are decreased through the progression of the disease. The differentiation of monocytes towards macrophages is skewed to the M1 phenotype. Our results demonstrate that monocytes may participate in the neuroinflammation as they cross the blood brain barrier and also as they become more and more inflammatory through the progression of the disease.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".