Depletion of neutrophils reduces neuronal degeneration and inflammatory responses induced by quinolinic acid <i>in vivo</i>
Bibliographic record
Abstract
The use of anti-neutrophil serum (anti-PMN) to induce neutropenia has been assessed for neuroprotection, modulation of microgliosis and astrogliosis, effects on oxidative stress, and intactness of blood-brain barrier (BBB) following injection of the excitotoxin quinolinic acid (QUIN) into rat striatum. At 1 day following QUIN injection, considerable striatal neurodegeneration was measured (Fluoro-Jade B marker). At this time, marked microgliosis (OX-42 marker) and astrogliosis (GFAP marker) were evident in QUIN-injected striatum. Treatment of QUIN-injected animals with anti-PMN protected neurons (48% reduction of striatal neuron loss) and inhibited microgliosis (61% reduction) and astrogliosis (43% reduction) compared with QUIN injection alone. Anti-PMN treatment was effective in decreasing levels of superoxide anions (by 42%) compared with QUIN alone; in addition, expressions of the neutrophil enzyme myeloperoxidase and the neutrophil oxidant 3-chlorotyrosine were markedly reduced (by 79 and 72%, respectively) with neutrophil depletion. QUIN-induced leakiness in BBB was indicated by elevated striatal levels of the blood protein fibrinogen, a result confirmed using Evans blue dye; anti-PMN was effective in reducing BBB permeability. Measurements from QUIN-injected animals directly confirmed anti-PMN efficacy in diminishing numbers of circulating neutrophils. Longer term neuroprotection and reduced microgliosis were also observed at 7 days post-injection of anti-PMN; at this time, anti-PMN-treated rats also demonstrated an improved apomorphine-induced rotational performance. We conclude that anti-PMN treatment could serve as a novel strategy to prevent leakiness to BBB, reduce gliosis, and protect striatal neurons in excitotoxin-injected brain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".