Impact of intravenous immunoglobulin on the dopaminergic system and immune response in the acute MPTP mouse model of Parkinson’s disease
Bibliographic record
Abstract
Intravenous immunoglobulin (IVIg) is a blood-derived product, used for the treatment of immunodeficiency and autoimmune diseases. Since a range of immunotherapies have recently been proposed as a therapeutic strategy for Parkinson's disease (PD), we investigated the effects of an IVIg treatment in a neurotoxin-induced animal model of PD. Mice received four injections of MPTP (15 mg/kg) at 2-hour intervals followed by a 14-day IVIg treatment, which induced key immune-related changes such as increased regulatory T-cell population and decreased CD4(+)/CD8(+) ratio. The MPTP treatment induced significant 80% and 84% decreases of striatal dopamine concentrations (P < 0.01), as well as 33% and 40% reductions in the number of nigral dopaminergic neurons (P < 0.001) in controls and IVIg-treated mice, respectively. Two-way analyses of variance further revealed lower striatal tyrosine hydroxylase protein levels, striatal homovanillic acid concentrations and nigral dopaminergic neurons (P < 0.05) in IVIg-treated animals. Collectively, our results fail to support a neurorestorative effect of IVIg on the nigrostriatal system in the MPTP-treated mice and even suggest a trend toward a detrimental effect of IVIg on the dopaminergic system. These preclinical data underscore the need to proceed with caution before initiating clinical trials of IVIg in PD patients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".