Bibliographic record
Abstract
Inflammation of the central nervous system (CNS) (neuroinflammation) is now recognized to be a feature of all neurological disorders. In multiple sclerosis, there is prominent infiltration of various leukocyte subsets into the CNS. Even when there is no significant inflammatory infiltrates, such as in Parkinson or Alzheimer disease, there is intense activation of microglia with resultant elevation of many inflammatory mediators within the CNS. An extensive dataset describes neuroinflammation to have detrimental consequences, but results emerging largely over the past decade have indicated that aspects of the inflammatory response are beneficial for CNS outcomes. Benefits of neuroinflammation now include neuroprotection, the mobilization of neural precursors for repair, remyelination, and even axonal regeneration. The findings that neuroinflammation can be beneficial should not be surprising as a properly directed inflammatory response in other tissues is a natural healing process after an insult. In this article, we review the data that highlight the dual aspects of neuroinflammation in being a hindrance on the one hand but also a significant help for recovery of the CNS on the other. We consider how the inflammatory response may be beneficial or injurious, and we describe strategies to harness the beneficial aspects of neuroinflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".