Neuroinflammation Mechanisms and Phytotherapeutic Intervention: A Systematic Review
Bibliographic record
Abstract
Neuroinflammation is indicated in the pathogenesis of several acute and chronic neurological disorders. Acute lesions in the brain parenchyma induce intense and highly complex neuroinflammatory reactions with similar mechanisms among various disease prototypes. Microglial cells in the CNS sense tissue damage and initiate inflammatory responses. The cellular and humoral constituents of the neuroinflammatory reaction to brain injury contribute significantly to secondary brain damage and neurodegeneration. Inflammatory cascades such as proinflammatory cytokines from invading leukocytes and direct cell-mediated cytotoxicity between lymphocytes and neurons are known to cause "collateral damage" in models of acute brain injury. In addition to degeneration and neuronal cell loss, there are secondary inflammatory mechanisms that modulate neuronal activity and affect neuroinflammation which can even be detected at the behavioral level. Hence, several of health conditions result from these pathogenetic conditions which are underlined by progressive neuronal function loss due to chronic inflammation and oxidative stress. In the first part of this Review, we discuss critical neuroinflammatory mediators and their pathways in detail. In the second part, we review the phytochemicals which are considered as potential therapeutic molecules for treating neurodegenerative diseases with an inflammatory component.
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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 | 0.004 | 0.004 |
| 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) | 0.006 | 0.001 |
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".