No neurorestorative effect of IVIg in a mouse model of Parkinson Disease (75.6)
Bibliographic record
Abstract
Abstract Immunotherapies have been proposed as a therapeutic strategy for the treatment of Parkinson disease (PD). Intravenous immunoglobulin preparation (IVIg) is used for the treatment of immunodeficiency and autoimmune diseases. Here, we investigated the effect of IVIg treatment on the dopaminergic (DAergic) nigrostriatal system after MPTP-induced denervation in mice. Mice received 4 MPTP injections (15 mg/Kg) at 2-hour intervals followed by a 14 days treatment with IVIg. IVIg injections led to a 21,6% increase in TReg cells in vehicle group (p<0.05) and minimal specific immune response. As expected, MPTP insult induced a significant 80% and 84% depletion of striatal dopamine content (p<0.01), as well as a 31% and 45% nigral DAergic neuron loss (p<0.001) in the Control and IVIg group, respectively. Moreover, two-way ANOVA analyses revealed a significant downregulating effect of IVIg on striatal tyrosine hydroxylase protein level (-16%, p<0.05) and on the number of nigral DAergic neurons (-29%, p<0.05), paralleling the toxic effect of MPTP. Collectively, our results provide no evidence of a neurorestorative effect of IVIg on the nigrostriatal system of the MPTP-treated mouse, and suggest that we need to proceed with caution before treating PD patients with IVIg.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".