Associations of neuroinflammatory parameters with clinical features in patients with mild cognitive impairment and dementia
Bibliographic record
Abstract
Introduction Mild cognitive impairment (MCI) represent a state of cognitive function between normal aging and dementia and does not always progress to dementia. Neuroinflammation has a key role in the pathogenesis of neurodegeneration. Determining the associations of neuroinflammatory markers in the blood with clinical disease severity may be useful for early diagnosis of cognitive impairment and prediction of the development of severe dementia. Objectives The aim of our study was to compare the serum concentration of a panel of inflammatory markers in patients with MCI and dementia as well as their associations with clinical symptoms. Methods Patients were evaluated using Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Montreal Cognitive Assessment scales (MoCA), Clinical Dementia rating (CDR) and Hospital Anxiety Depression Scale (HADS). We determined the serum concentration of a panel of inflammatory markers (25 units) cytokines, chemokines, growth factors and several others on Mulliplex and prepared multivariate analysis to investigate associations between clinical features and serum concentration. Results Patients with dementia had lower scores on scales than the control and MCI groups. MCI patients were equal to the control group, except for the MMSE scale. EGF, eotaxin-1, GRO-α, IP-10, IL-8, MIP-1β, sCD40L, TNF-α, MDC and MCP-1, VEGF were differ between groups. Multivariate analysis identified some neuroinflammatory parameters associated with the severity of the disease. Conclusions We identified some neuroinflammatory parameters associated with dementia and MCI. Many of them have been poor described and data is contradictory. It is necessary to investigate these parameters as potential biomarkers of neurodegeneration in further studies. Disclosure No significant relationships.
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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) | 0.000 | 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) | 0.002 | 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".