Therapeutic approaches to inflammation in neurodegenerative disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: According to the neuroinflammatory hypothesis of neurodegenerative diseases, drugs with an anti-inflammatory mode of action should slow the disease progression. Here we review recent advances in our understanding of one such disorder, Parkinson's disease, in which anti-inflammatory drugs are now becoming a new therapeutic focus. RECENT FINDINGS: The involvement of inflammatory mechanisms in Parkinson's disease has been revealed through in-vitro and in-vivo experimental studies supported by pathological and epidemiological findings. Several of the demonstrated inflammatory mechanisms are shared by other neurodegenerative disorders but some Parkinson's disease-specific mechanisms have also emerged. These include inflammatory stimulation by interaction of alpha-synuclein with microglia and astrocytes and a suppressive action by nonsteroidal anti-inflammatory drugs on dopamine quinone formation. SUMMARY: It can be anticipated that a more detailed understanding of neuroinflammatory mechanisms in Parkinson's disease will lead to new cellular and molecular targets, which may, in turn, permit design of Parkinson's disease modifying drugs. Future treatment may involve combination therapies with drugs directed at both inflammatory and non-inflammatory mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".