Radiofrequency Radiation and Gene/Protein Expression: A Review
Bibliographic record
Abstract
Mobile telecommunications have developed considerably in recent years. With the proliferation of wireless technologies, there is much public anxiety about the potential health impact associated with exposure to radiofrequency (RF) radiation from these novel products. Contradictory scientific evidence, often reported in the popular media, has further fueled public concern. Some epidemiological studies have reported that ipsilateral use of a mobile phone is associated with an increased risk for brain tumors, while other studies have reported an association between brain tumor risk and mobile phone use for longer than 10 years. However, other large epidemiological studies have failed to find similar associations. Despite the existence of national and international RF-radiation exposure guidelines, there are increasing public demands for precaution with respect to human exposure to RF radiation. Since current epidemiological evidence is insufficient to make a definitive judgment on the health risks of low-level RF radiation exposure, laboratory evidence assessing biological plausibility and theoretical mechanisms of interaction are important. A number of studies have reported that RF radiation may induce alterations in gene/protein expression in a variety of cells/tissues that may be associated with potentially harmful health outcomes, while other studies have shown no clear effects related to RF radiation. This review focuses on the current scientific evidence related to changes in protein and gene expression induced by low-level RF radiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".