Pre-clinical Studies Identifying Molecular Pathways of Neuroinflammation in Parkinson's Disease: A Systematic Review
Bibliographic record
Abstract
Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterized by neuroinflammation, formation of Lewy bodies, and progressive loss of dopaminergic neurons in the substantia nigra of the brain. In this review, we summarize evidence obtained by animal studies demonstrating neuroinflammation as one of the central pathogenetic mechanisms of PD. We also focus on the protein factors that initiate the development of PD and other neurodegenerative diseases. Our targeted literature search identified 40 pre-clinicalin vivoandin vitrostudies written in English. Nuclear factorkappaB (NF-kB) pathway is demonstrated as a common mechanism engaged by neurotoxins such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 6-hydroxydopamine (6-OHDA), as well as the bacterial lipopolysaccharide (LPS). The α-synuclein protein, which plays a prominent role in PD neuropathology, may also contribute to neuroinflammation by activating mast cells. Meanwhile, 6-OHDA models of PD identify microsomal prostaglandin E synthase-1 (mPGES-1) as one of the contributors to neuroinflammatory processes in this model. Immune responses are used by the central nervous system to fight and remove pathogens; however, hyperactivated and prolonged immune responses can lead to a harmful neuroinflammatory state, which is one of the key mechanisms in the pathogenesis of PD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".