15 - Effects of Low Field Magnetic Stimulation (LFMS) on Proliferation and Differentiation of Oligodendrocyte Progenitor Cells
Bibliographic record
Abstract
Objectives:This study studied the cellular mechanism of Low field magnetic stimulation (LFMS), a novel non-invasive brain stimulation technology for neuropsychiatric disorders.Background:Background and aims LFMS is a neuromodulation technology providing rapid mood improvement in patients with mood disorders. The exact mechanism of LFMS is unclear. Oligodendrocytes (OLs) play critical roles in regulating emotion and cognition. OL deficits have been associated with mood disorders and other psychiatric conditions. This aim of this study was to determine the effects of LFMS on the development of OL cells.Materials and Methods:Materials and methods: CG4 cells, an OL progenitor cell line, were treated with LFMS for 20 min daily for 5 days. The proliferation was measured by MTT and LDH levels; the differentiation of the CG4 cells was examined using cell biomarkers GFAP (astrocyte), Olig2 (OLs) and Ki-67 (cell proliferation), respectively. Key modulating factors for OL proliferation or differentiation (Olig1 and Olig2, p-Akt and p-ERK) were analyzed.Results and Conclusions:Results: LFMS treatment enhanced the OL proliferation through an activated p-ERK pathway. During differentiation, LFMS elevated the expression of Olig2, but decreased GFAP, suggesting that LFMS promoted CG4 cells developing into OLs, not astrocytes. Our findings suggest that LFMS altered OL development. This study provided evidence of the role of OLs in the treatment of mood disorders and a possible mechanism of LFMS in treating psychiatric disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".