Association between IL-17, IL-23 with neurocognitive scales in patients with Alzheimer’s disease
Bibliographic record
Abstract
Introduction Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. Evidence suggests that various cytokines, including interleukins (IL) IL-6, IL-10, IL-12 are actively involved in the pathogenesis of AD. The role of IL-17 and IL-23 is less clear. Objectives To investigate the correlations between IL-17, IL-23, and neurocognitive scales in patients with Alzheimer’s disease. Methods The study included 45 patients: 15 patients with Alzheimer’s disease and 30 patients without cognitive deficit (control group). Clinical and psychometrical methods were used: Mini Mental State Examination (MMSE) scale; Montreal Cognitive Assessment (MoCA), Frontal Assessment Battery (FAB), Alzheimer Disease Assessment Scale-cognitive (ADAS ̶ cog). Serum levels of cytokines of IL-17 and IL-23 were analyzed by sandwich ELISA on “Chem Well 2900” immunoanalyzer (Awareness Technology, USA). Results A significantly positive correlation was observed between IL-17 and IL-23 for all AD patients (r =0.723, p=0.002). A significant inverse correlation was observed between serum concentration of IL-17 and MoCA score (r=˗1.0, р≤.0001) and IL-23 and MMSE score (r=˗0.553, р=0.032) in all AD patients. However, no other significant correlations were found between IL-17 and the scores MMSE, FAB, ADAS ̶ cog and between IL-23 and the scores MoCA, FAB and ADAS ̶ cog. Conclusions Proinflammatory cytokines (such as IL-17 and IL-23) have been associated with cognitive impairment. However, the complicated relationships of the two cytokines with the pathogenesis of AD need to be further investigated in the future. 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 | 0.002 |
| 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 | 0.000 | 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".