The Role of Dysregulated Neuroinflammatory Molecular Pathways in Parkinson Disease: A Systematic Review
Bibliographic record
Abstract
Background: Parkinson disease (PD) is a prevalent neurodegenerative disorder affecting dopaminergic neurons in the substantia nigra (SN). Neuroinflammation has a vital role in PD pathophysiology. Objectives: This study assesses whether the neuroinflammatory molecular and signaling pathways could be associated with PD’s progression and clinical manifestations. Materials & Methods: PubMed, Web of Science, Embase, and Scopus databases were investigated from 2006 until December 2023 to find relevant studies. All observational studies written in English and reporting qualitative or quantitative information on the relationship between neuroinflammation and PD were included in this review. Results: Finally, 41 papers were involved in the systematic review. According to the involved studies, it is suggested that tumor necrosis factor-α, C-reactive protein, microsomal prostaglandin E synthase1, toll-like receptor-4 (TLR-4), CCL23, CCL25, TNF-receptor superfamily member 9, EV-derived cytokines, transforming growth factor alpha, vascular endothelial growth factor A, SH-SY5Y, TLR 2/4, miR-485-3p, leucine-rich repeat kinase 2, and α-synuclein may be upregulated in the PD patients. Also, the activity of astrocytes and microglial cells was reported to be increased in PD patients through different mechanisms. Conclusion: This study demonstrated that the neurodegeneration in PD could be initiated by α-synuclein protein aggregation and the activation of astrocytes and microglial cells, which leads to neuroinflammation characterized by inflammatory responses in neurons. Finally, chronic neuroinflammation could be the cause of dopaminergic neuronal death in SN. The impact of both single and all factors involved in neuroinflammation was assessed to plan further studies in a particular pathway to intercept the onset of inflammatory pathways in favor of therapeutic purposes.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".