Bibliographic record
Abstract
Chronic neuroinflammation is a pathological condition of numerous central nervous system (CNS) diseases such as Parkinson's disease, Alzheimer's disease, multiple sclerosis, amyotrophic lateral sclerosis and many others. Neuroinflammation is characterized by the microglia activation and concomitant production of pro-inflammatory cytokines leading to an increasing neuronal cell death. The decreased neuroinflammation could be obtained by using natural compounds, including flavonoids known to modulate the inflammatory responses. Among flavonoids, quercetin possess multiple pharmacological applications including anti-inflammatory, antitumoral, antiapoptotic and anti-thrombotic activities, widely demonstrated in both in vitro and in vivo studies. In this review, we describe the recent findings about the neuroprotective action of quercetin by acting with different mechanisms on the microglial cells of CNS. The ability of quercetin to influence microRNA expression represents an interesting skill in the regulation of inflammation, differentiation, proliferation, apoptosis and immune responses. Moreover, in order to enhance quercetin bioavailability and capacity to target the brain, we discuss an innovative drug delivery system. In summary, this review highlighted an important application of quercetin in the modulation of neuroinflammation and prevention of neurological disorders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".