The Influence of Burst-Firing EMF on Forskolin-Induced Pheochromocytoma (PC12) Plasma Membrane Extensions
Bibliographic record
Abstract
Previous research has demonstrated that pheochromocytoma (PC12) cells treated with forskolin provides a model for the in vitro examination of neuritogenesis. Exposure to electromagnetic fields (EMFs), especially those which have been designed to mimic biological function, can influence the functions of various biological systems. We aimed to assess whether exposure of PC12 cells treated with forskolin to patterned EMF would produce more plasma membrane extensions (PME) as compared to PC12 cells treated with forskolin alone (i.e., no EMF exposure). In addition, we aimed to determine whether the differences observed between the proportion of PME of PC12 cells treated with forskolin and exposed to EMF were specific to the intensity, pattern, or timing of the applied EMF. Our results showed an overall increase in PME for PC12 cells treated with forskolin and exposed to Burst-firing EMF as compared to PC12 cells receiving forskolin alone. No other patterned EMF investigated were deemed to be effective. Furthermore, intensity and timing of the Burst-firing pattern did not significantly alter the proportion of PME of PC12 cells treated with forskolin and exposed to patterned EMF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".