Real‐time measurement of cytosolic free calcium concentration in Jurkat cells during ELF magnetic field exposure and evaluation of the role of cell cycle
Bibliographic record
Abstract
Extremely low frequency magnetic fields (ELF MF) have been reported to alter a number of cell signaling pathways, including those involved in proliferation, differentiation and apoptosis where cytosolic free calcium ([Ca(2+)](c)) plays an important role. To better understand the biological conditions under which ELF MF exposure might alter [Ca(2+)](c), we measured [Ca(2+)](c) by ratiometric fluorescence spectrophotometry during exposure to ELF MF in Jurkat E6.1 cells synchronized to different phases of the cell cycle. Suspensions of cells were exposed either to a near zero MF (Null) or a 60 Hz, 100 microT sinusoidal MF superimposed upon a collinear 78.1 microT static MF (AC + DC). An initial series of experiments indicated that the maximum increase in [Ca(2+)](c) above baseline after stimulation with anti-CD3 was significantly higher in samples exposed to AC + DC (n = 30) compared to Null (n = 30) with the largest difference in G2-M enriched samples. However, in a second study with G2-M enriched cells, samples treated with AC + DC (n = 17) were not statistically different from Null-treated samples (n = 27). Detailed analysis revealed that the dynamics in [Ca(2+)](c) before and after stimulation with anti-CD3 were dissimilar between Null samples from each study. From the results, we concluded (i) that the ELF MF increased [Ca(2+)](c) during an antibody-induced signaling event, (ii) that the ELF MF effect did not depend to a large degree on cell cycle, and (iii) that a field-related change in [Ca(2+)](c) signaling appeared to correlate with features in the [Ca(2+)](c) dynamics. Future work could evaluate [Ca(2+)](c) dynamics in relation to the phase of the cell cycle and inter-study variation, which may reveal factors important for the observation of real-time effects of ELF MF on [Ca(2+)](c).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".