Low-intensity pulsed ultrasound enhances neurite growth in serum-starved human neuroblastoma cells
Bibliographic record
Abstract
Introduction Low-intensity pulsed ultrasound (LIPUS) is a recognized tool for promoting nerve regeneration and repair; however, the intracellular mechanisms of LIPUS stimulation remain underexplored. Method The present study delves into the effects of varying LIPUS parameters, namely duty cycle, spatial average-temporal average (SATA) intensity, and ultrasound amplitude, on the therapeutic efficacy using SK-N-SH cells cultured in serum-starved conditions. Four distinct LIPUS settings were employed: (A) 50 mW/cm2, 40%, (B) 25 mW/cm2, 10%, (C) 50 mW/cm2, 20%, and (D) 25 mW/cm2, 10%. Results Immunochemistry analysis exhibited neurite outgrowth promotion in all LIPUS-treated groups except for Group D. Further, LIPUS treatment was found to successfully promote brain-derived neurotrophic factor (BDNF) expression and enhance the phosphorylation of extracellular signal-regulated kinase (ERK)1/2, protein kinase B (Akt), and mammalian target of rapamycin (mTOR) signaling pathways, as evidenced by western blot analysis. Discussion The study suggests that the parameter combination of LIPUS determines the therapeutic efficacy of LIPUS. Future investigations should aim to optimize these parameters for different cell types and settings and delve deeper into the cellular response mechanism to LIPUS treatment. Such advancements may aid in tailoring LIPUS treatment strategies to specific therapeutic needs.
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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) | 0.001 | 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".