Inter-Relationship between Low-Dose Hyper-Radiosensitivity and Radiation-Induced Bystander Effects in the Human T98G Glioma and the Epithelial HaCaT Cell Line
Bibliographic record
Abstract
Over the past several years, investigations in both low-dose hyper-radiosensitivity and increased radioresistance have been a focus of radiation oncology and biology research, since both conditions occur primarily in tumor cell lines. There has been significant progress in elucidating their signaling pathways, however uncertainties exist when they are studied together with radiation-induced bystander effects. Therefore, the aim of this work was to further investigate this relationship using the T98G glioma and HaCaT cell lines. T98G glioma cells have demonstrated a strong transition from hyper-radiosensitivity to induced radioresistance, and HaCaT cells do not show low-dose hypersensitivity. Both cell lines were paired using a mix-and-match protocol, which involved growing nonirradiated cells in culture media from irradiated cells and covering all possible combinations between them. The end points analyzed were clonogenic cell survival and live calcium measurements through the cellular membrane. Our data demonstrated that T98G cells produced bystander signals that decreased the survival of both reporter T98G and HaCaT cells. The bystander effect occurred only when T98G cells were exposed to doses below 1 Gy, which was corroborated by the induction of calcium fluxes. However, when bystander signals originated from HaCaT cells, the survival fraction increased in reporter T98G cells while it decreased in HaCaT cells. Moreover, the corresponding calcium data showed no calcium fluxes in T98G cells, while HaCaT cells displayed a biphasic calcium profile. In conclusion, our findings indicate a possible link between low-dose hyper-radiosensitivity and bystander effects. This relationship varies depending on which cell line functions as the source of bystander signals. This further suggests that the bystander mechanisms are more complex than previously expected and caution should be taken when extrapolating bystander results across all cell lines and all radiation doses.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".