Quantifying Biophoton Emissions From Human Cells Directly Exposed to Low-Dose Gamma Radiation
Bibliographic record
Abstract
Biophoton emission leading to bystander effects (BEs) was shown in beta-irradiated cells; however, technical challenges precluded the analysis of the biophoton role in gamma-induced BEs. The present work was to design an experimental approach to determine if, what type, and how many biophotons could be produced in gamma-irradiated cells. Photon emission was measured in HCT116 p53 +/+ cells irradiated with a total dose of 22 mGy from a cesium-137 source at a dose rate of 45 mGy/min. A single-photon detection unit was used and shielded with lead to reduce counts from stray gammas reaching the detector. Higher quantities of photon emissions were observed when the cells in a tissue culture vessel were present and being irradiated compared to a cell-free vessel. Photon emissions were captured at either 340 nm (in the ultraviolet A [UVA] range) or 610 nm. At the same cell density, radiation exposure time, and radiation dose, HCT116 p53 +/+ cells emitted 2.5 times more UVA biophotons than 610-nm biophotons. For the first time, gamma radiation was shown to induce biophoton emissions from biological cells. As cellular emissions of UVA biophotons following beta radiation lead to BEs, the involvement of cellular emissions of the same type of UVA biophotons in gamma radiation-induced BEs is highly likely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".