Cytokine dysregulation in amnestic mild cognitive impairment
Bibliographic record
Abstract
The pathophysiology of amnestic Mild Cognitive Impairment (aMCI) is largely unknown, although some papers found signs of immune activation. To assess the cytokine network in aMCI after excluding patients with major depression (MDD) and to examine the immune profiles of quantitative aMCI (qMCI) and distress symptoms of old age (DSOA) scores. A case-control study was conducted on 61 Thai aMCI participants and 60 healthy old adults (both without MDD). The Bio-Plex Pro human cytokine 27-plex test kit was used to assay cytokines/chemokines/growth factors in fasting plasma samples. aMCI is characterized by a significant immunosuppression, and reductions in T helper 1 (Th)1 and T cell growth profiles, the immune-inflammatory responses system, interleukin (IL)1β, IL6, IL7, IL12p70, IL13, GM-CSF, and MCP-1. These 7 cytokines/chemokines exhibit neuroprotective effects at physiologic concentrations. In multivariate analyses, three neurotoxic chemokines, CCL11, CCL5, and CXCL8, emerged as significant predictors of aMCI. Logistic regression showed that aMCI was best predicted by combining IL7, IL1β, MCP-1, years of education (all inversely associated) and CCL5 (positively associated). We found that 38.2% of the variance in the qMCI score was explained by IL7, IL1β, MCP-1, IL13, years of education (inversely associated) and CCL5 (positively associated). The DSOA was not associated with any immune data. An imbalance between lowered levels of neuroprotective cytokines and chemokines, and relative increases in neurotoxic chemokines are key factors in aMCI. Future MCI research should always control for the confounding effects of affective symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".