Low-Field Magnetic Stimulation Restores Cognitive and Motor Functions in the Mouse Model of Repeated Traumatic Brain Injury: Role of Cellular Prion Protein
Bibliographic record
Abstract
Traumatic brain injury (TBI)/concussion is a growing epidemic throughout the world. Memory and neurobehavioral dysfunctions are among the sequelae of TBI. Dislodgement of cellular prion protein (PrPc) and disruption of circadian rhythm have been linked to TBI. Low-field magnetic stimulation (LFMS) is a new noninvasive repetitive transcranial magnetic stimulation (rTMS) technique that generates diffused and low-intensity magnetic stimulation to deep cortical and subcortical areas. The role of LFMS on PrPc, proteins related to the circadian rhythm, and behavior alterations in a repeated TBI mouse model were studied in the present study. TBI was induced to the mice (right hemisphere) using weight-drop method, once daily for 3 days. LFMS treatment was given for 20 min once daily for 4 days (immediately after each TBI induction). The results showed that LFMS-treated TBI mice significantly improved cognitive and motor function as evidenced by open field exploration, rotarod, and novel location recognition tasks. In addition, a significant increase in PrPc and decreased glial fibrillary acidic protein levels were observed in cortical and hippocampal regions of LFMS-treated TBI mice brain compared with sham-treated TBI mice, while neuronal nuclei level was significantly increased in cortical region. In LFMS-treated mice, a decrease in proteins related to circadian rhythm were observed, compared with sham-treated TBI mice. The results obtained from the study demonstrated the neuroprotective effect of LFMS, which may be through regulating PrPc and/or proteins related to circadian rhythm. Thus, the present study suggests that LFMS may improve the subject's neurological condition following TBI.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".