Stress induction in the bacteria<i>Shewanella oneidensis</i>and<i>Deinococcus radiodurans</i>in response to below-background ionizing radiation
Bibliographic record
Abstract
PURPOSE: The 'Linear no-threshold' (LNT) model predicts that any amount of radiation increases the risk of organisms to accumulate negative effects. Several studies at below background radiation levels (4.5-11.4 nGy h(-1)) show decreased growth rates and an increased susceptibility to oxidative stress. The purpose of our study is to obtain molecular evidence of a stress response in Shewanella oneidensis and Deinococcus radiodurans grown at a gamma dose rate of 0.16 nGy h(-1), about 400 times less than normal background radiation. MATERIALS AND METHODS: Bacteria cultures were grown at a dose rate of 0.16 or 71.3 nGy h(-1) gamma irradiation. Total RNA was extracted from samples at early-exponential and stationary phases for the rt-PCR relative quantification (radiation-deprived treatment/background radiation control) of the stress-related genes katB (catalase), recA (recombinase), oxyR (oxidative stress transcriptional regulator), lexA (SOS regulon transcriptional repressor), dnaK (heat shock protein 70) and SOA0154 (putative heavy metal efflux pump). RESULTS: Deprivation of normal levels of radiation caused a reduction in growth of both bacterial species, accompanied by the upregulation of katB, recA, SOA0154 genes in S. oneidensis and the upregulation of dnaK in D. radiodurans. When cells were returned to background radiation levels, growth rates recovered and the stress response dissipated. CONCLUSIONS: Our results indicate that below-background levels of radiation inhibited growth and elicited a stress response in two species of bacteria, contrary to the LNT model prediction.
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) | 0.001 | 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".