Factors affecting ultraviolet-A photon emission from <i>β</i>-irradiated human keratinocyte cells
Bibliographic record
Abstract
The luminescence intensity of 340±5 nm photons emitted from HaCaT (human keratinocyte) cells was investigated using a single-photon-counting system during cellular exposure to (90)Y β-particles. Multiple factors were assessed to determine their influence upon the quantity and pattern of photon emission from β-irradiated cells. Exposure of 1 x 10(4) cells/5 mL to 703 μCi resulted in maximum UVA photoemission at 44.8 x 10(3)±2.5 x 10(3) counts per second (cps) from live HaCaT cells (background: 1-5 cps); a 16-fold increase above cell-free controls. Significant biophoton emission was achieved only upon stimulation and was also dependent upon presence of cells. UVA luminescence was measured for (90)Y activities 14 to 703 μCi where a positive relationship between photoemission and (90)Y activity was observed. Irradiation of live HaCaT cells plated at various densities produced a distinct pattern of emission whereby luminescence increased up to a maximum at 1 x 10(4) cells/5 mL and thereafter decreased. However, this result was not observed in the dead cell population. Both live and dead HaCaT cells were irradiated and were found to demonstrate different rates of photon emission at low β activities (⩽400 μCi). Dead cells exhibited greater photon emission rates than live cells which may be attributable to metabolic processes taking place to modulate the photoemissive effect. The results indicate that photon emission from HaCaT cells is perturbed by external stimulation, is dependent upon the activity of radiation delivered, the density of irradiated cells, and cell viability. It is postulated that biophoton emission may be modulated by a biological or metabolic process.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".