A Role for Bioelectric Effects in the Induction of Bystander Signals by Ionizing Radiation?
Bibliographic record
Abstract
The induction of "bystander effects" i.e. effects in cells which have not received an ionizing radiation track, is now accepted but the mechanisms are not completely clear. Bystander effects following high and low LET radiation exposure are accepted but mechanisms are still not understood. There is some evidence for a physical component to the signal. This paper tests the hypothesis that bioelectric or biomagnetic phenomena are involved. Human immortalized skin keratinocytes and primary explants of mouse bladder and fish skin, were exposed directly to ionizing radiation or treated in a variety of bystander protocols. Exposure of cells was conducted by shielding one group of flasks using lead, to reduce the dose below the threshold of 2mGy (60)Cobalt gamma rays established for the bystander effect. The endpoint for the bystander effect in the reporter system used was reduction in cloning efficiency (RCE). The magnitude of the RCE was similar in shielded and unshielded flasks. When cells were placed in a Faraday cage the magnitude of the RCE was less but not eliminated. The results suggest that liquid media or cell-cell contact transmission of bystander factors may be only part of the bystander mechanism. Bioelectric or bio magnetic fields may have a role to play. To test this further, cells were placed in a Magnetic Resonance Imaging (MRI) machine for 10 min using a typical head scan protocol. This treatment also induced a bystander response. Apart from the obvious clinical relevance, the MRI results further suggest that bystander effects may be produced by non-ionizing exposures. It is concluded that bioelectric or magnetic effects may be involved in producing bystander signaling cascades commonly seen following ionizing radiation exposure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".