DynamoDetrend: Removing Nonlinear Decay Artifacts in Concurrent Multimodal Neurostimulation and Electrophysiology
Bibliographic record
Abstract
Traumatic brain injury (TBI) constitutes a significant neurological disorder globally, resulting in diverse levels of neural dysfunction.The overactivation of microglia-induced neuroinflammation is a critical factor that exacerbates secondary damage in TBI.Prior research has demonstrated that transcranial pulsed current stimulation (tPCS) possesses the potential to mitigate neuroinflammation and neurological deficits.Nevertheless, the precise mechanisms underlying this neuroprotective effect remain unclear.In this study, a mouse model of TBI was developed using controlled cortical impact, followed by daily 30-minute tPCS treatment for 5 days.The effects of tPCS on motor function, microglial activation, neuroregeneration, and neuronal apoptosis in mice with TBI were subsequently evaluated.In vivo experiments revealed that tPCS attenuated neuroinflammation and reduced brain tissue damage following TBI, while simultaneously enhancing neurorepair and the restoration of neurological function.The underlying mechanism is hypothesized to involve the upregulation of orexin-A (OX-A) expression, inhibition of the NF-kB signaling pathway, and promotion of neuroregeneration at the site of injury.In conclusion, our study indicates that tPCS can modulate the microglial phenotype by regulating the OX-A/OX1R-mediated NF-kB pathway, thereby inhibiting neuroinflammation and promoting neurorepair and regeneration to ultimately enhance neurological function post-TBI.These findings contribute to elucidating the neuroprotective mechanisms of tPCS, offering new insights for the rehabilitation of TBI 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) | 0.000 | 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 | 0.001 |
| 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".