Factors confounding cytosolic calcium measurements in Jurkat E6.1 cells during exposure to ELF magnetic fields
Bibliographic record
Abstract
Reported changes in the cytosolic calcium concentration ([Ca2+](c)) as a result of exposure to extremely low frequency (ELF) magnetic fields (MF) have been equivocal. In this study, we examine the possibility that some of these differences are attributable to variability associated with the cell cycle, pH of the suspension medium, and response to a calcium agonist. We used a custom designed spectrofluorimeter to measure [Ca2+](c) in Indo 1-AM loaded Jurkat E6.1 cells suspended in conditioned RPMI 1640 medium containing 10% fetal bovine serum. Four exposures were examined: zero static MF (Null), 60 Hz 100 microT(peak) sinusoidal MF (AC), 78 microT static MF (DC), and the combination of the 60 Hz and the 78 microT static MF (AD + DC). A significant decrease in normalized [Ca2+](c) values between 375-495 s for the DC and AC + DC groups was found in comparison to the Null group. However, statistical analysis indicated that cell cycle and quality of the alpha-CD3 monoclonal antibody response were significant covariates, while pH was not a significant covariate. When the effect of these covariates was taken into account, all exposure groups were significantly different from the control. Our results suggest that ELF MF effects may not be seen unless correction is made for biological variability of each cell preparation with respect to cell cycle and [Ca2+](c) response to antigen stimulation.
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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 | 0.001 |
| 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".