Low-Field Magnetic Stimulation Alleviates MPTP Intoxicated Alterations on Motor Function and Dopaminergic Neurons in Male Mice
Bibliographic record
Abstract
Recent studies show that repetitive transcranial magnetic stimulation (rTMS) improves cognitive and motor functions in patients with Parkinson’s Disease (PD). Gamma rhythm low-field magnetic stimulation (LFMS) is a new non-invasive rTMS technique that generates diffused and low-intensity magnetic stimulation to deep cortical and subcortical areas. To investigate potential therapeutic effects of LFMS in PD, we subjected an experimental mouse model to LFMS (as early treatment). We examined the LFMS effect on motor functions as well as neuronal and glial activities in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced male C57BL/6J mice. Mice received MPTP injection (30 mg/kg, i.p., once daily for 5 days) followed by LFMS treatment, 20 minutes each day for 7 days. LFMS treatment improved motor functions compared to the sham-treated MPTP mice. Further, LFMS significantly improved tyrosine hydroxylase (TH) and decreased glial fibrillary acidic protein (GFAP) levels, in substantia nigra pars compacta (SNpc) and non- significantly in striatal (ST) regions. LFMS treatment improved neuronal nuclei (NeuN) level in SNpc. Our findings suggest that early LFMS treatment improved the neuron survival and in turn, motor functions in MPTP intoxicated mice brain. Further investigation is required to clearly define the molecular mechanisms by which LFMS improves motor and cognitive function 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".