Electromagnetic fields (UHF) increase voltage sensitivity of membrane ion channels; possible indication of cell phone effect on living cells
Bibliographic record
Abstract
The effects of ultra high frequency (UHF) nonionizing electromagnetic fields (EMF) on the channel activities of nanopore forming protein, OmpF porin, were investigated. The voltage clamp technique was used to study the single channel activity of the pore in an artificial bilayer in the presence and absence of the electromagnetic fields at 910 to 990 MHz in real time. Channel activity patterns were used to address the effect of EMF on the dynamic, arrangement and dielectric properties of water molecules, as well as on the hydration state and arrangements of side chains lining the channel barrel. Based on the varied voltage sensitivity of the channel at different temperatures in the presence and absence of EMF, the amount of energy transferred to nano-environments of accessible groups was estimated to address the possible thermal effects of EMF. Our results show that the effects of EMF on channel activities are frequency dependent, with a maximum effect at 930 MHz. The frequency of channel gating and the voltage sensitivity is increased when the channel is exposed to EMF, while its conductance remains unchanged at all frequencies applied. We have not identified any changes in the capacitance and permeability of membrane in the presence of EMF. The effect of the EMF irradiated by cell phones is measured by Specific Absorption Rate (SAR) in artificial model of human head, Phantom. Thus, current approach applied to biological molecules and electrolytes might be considered as complement to evaluate safety of irradiating sources on biological matter at molecular level.
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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".