Genomic Instability in Liver Cells Caused by an LPS-Induced Bystander-Like Effect
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Bacterial infection has been linked to carcinogenesis, however, there is lack of knowledge of molecular mechanisms that associate infection with the development of cancer. We analyzed possible effects of the consumption of heat-killed E. coli O157:H7 cells or its cellular components, DNA, RNA, protein or lipopolysaccharides (LPS) on gene expression in naïve liver cells. Four week old mice were provided water supplemented with whole heat-killed bacteria or bacterial components for a two week period. One group of animals was sacrificed immediately, whereas another group was allowed to consume uncontaminated tap water for an additional two weeks, and liver samples were collected, post mortem. Liver cells responded to exposure of whole heat-killed bacteria and LPS with alteration in γH2AX levels and levels of proteins involved in proliferation, DNA methylation (MeCP2, DNMT1, DNMT3A and 3B) or DNA repair (APE1 and KU70) as well as with changes in the expression of genes involved in stress response, cell cycle control and bile acid biosynthesis. Other bacterial components analysed in this study did not lead to any significant changes in the tested molecular parameters. This study suggests that lipopolysaccharides are a major component of Gram-negative bacteria that induce molecular changes within naïve cells of the host.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".