Nanoscale Optoregulation of Neural Stem Cell Differentiation by Intracellular Alteration of Redox Balance
Bibliographic record
Abstract
Regulation of stem cell (SC) fate, a decision between self‐renewal and differentiation, is of immense importance in regenerative medicine and has been proven to be a powerful stimulus regulating many cell functions influencing the SC fate. This study uses triphenylphosphonium‐functionalized gold nanoparticles (TPP‐AuNPs) to explore the interplay of intracellular electromagnetic (EM) exposure and the SC fate. Localized EM waves are generated inside neural stem cells (NSCs) to stimulate TPP‐AuNPs (AuNPs), targeting the mitochondria through inducing reactive oxygen species and differentiating these cells into neurons. Following laser irradiation of TPP‐AuNPs‐transfected NSCs, their differentiation to neurons is monitored by tracing the relevant markers both at the genetic and protein levels. The electrophysiology technique is further used to examine the functionality of neurons. The results confirm that TPP‐AuNPs subjected to electromotive forces have the potential to regulate cellular fate, although further investigations are still required to shed light on the mechanisms underlying the interaction of EM‐stimulated TPP‐AuNPs on cellular fate to design highly adjustable cell differentiation and reprogramming methods.
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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".