Plasma cytokines profile in patients with Alzheimer’s and Parkinson’s Disease: a comparative study in terms of inflammation
Bibliographic record
Abstract
BACKGROUND: Neurodegenerative disorders such as Alzheimer's and Parkinson's disease inflict economic and health burdens on societies. Alzheimer's disease (AD), the most prevalent form of dementia, is accompanied by progressive degradation of memory, decision-making, and judgment. Parkinson's disease (PD) is characterized by resting tremor, rigidity, bradykinesia, and loss of balance. Extensive research has pinpointed inflammation as a cause of the onset and progression of both diseases. However, it has not been confirmed which one is more formidable in terms of inflammation. METHODS: To assess the extent of inflammation that is implicated in AD and PD and answer the question of which one is more inflammatory, serum levels of inflammatory biomarkers, including cytokines, chemokines, and prostaglandin E2 (PEG2), were measured in AD and PD patients as well as a healthy group. RESULTS: Our results showed a significant increase in IL-1α, IL-1β, IL-4, IL-6, IL-10, IL-12p70, IP-10, MCP-1, PEG2, and TNF-α in AD and PD patients compared with the control. Interestingly, IFN-γ did not manifest any significant difference in AD or PD patients compared with the control. CONCLUSION: As a hallmark of our results, it could be inferred that inflammation, as the underlying etiological cause, plays a more crucial role in PD compared with AD. Based on our results, it is proposed that anti-inflammatory remedies would be putatively more effective in PD rather than AD.
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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) | 0.001 | 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 | 0.001 | 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".