Effect of Subthalamic Stimulation and Electrode Implantation in the Striatal Microenvironment in a Parkinson’s Disease Rat Model
Bibliographic record
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is considered the gold-standard treatment for PD; however, underlying therapeutic mechanisms need to be comprehensively elucidated, especially in relation to glial cells. We aimed to understand the effects of STN-microlesions and STN-DBS on striatal glial cells, inflammation, and extracellular glutamate/GABAergic concentration in a 6-hydroxydopamine (6-OHDA)-induced PD rat model. Rats with unilateral striatal 6-OHDA and electrodes implanted in the STN were divided into two groups: DBS OFF and DBS ON (5 days/2 h/day). Saline and 6-OHDA animals were used as control. Akinesia, striatal reactivity for astrocytes, microglia, and inflammasome, and expression of cytokines, cell signaling, and excitatory amino acid transporter (EAAT)-2 were examined. Moreover, striatal microdialysis was performed to evaluate glutamate and GABA concentrations. The PD rat model exhibited akinesia, increased inflammation, glutamate release, and decreased glutamatergic clearance in the striatum. STN-DBS (DBS ON) completely abolished akinesia. Both STN-microlesion and STN-DBS decreased striatal cytokine expression and the relative concentration of extracellular glutamate. However, STN-DBS inhibited morphological changes in astrocytes, decreased inflammasome reactivity, and increased EAAT2 expression in the striatum. Collectively, these findings suggest that the beneficial effects of DBS are mediated by a combination of stimulation and local microlesions, both involving the inhibition of glial cell activation, neuroinflammation, and glutamate excitotoxicity.
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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 | 0.001 |
| 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 | 0.001 | 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".