Effects of Melanized Bacteria and Soluble Melanin on the Intestinal Homeostasis and Microbiome In Vivo
Bibliographic record
Abstract
Radiation damage is associated with inflammation and immunity in the intestinal mucosa, including gut microbiota. Melanin has a unique capacity to coordinate a biological reaction in response to environmental stimuli, such as radiation exposure. Thus, melanin and melanized microbes have potential to be used for mitigation of injury induced by radiation. The purpose of the current study is to examine the safety of these agents for future targeting gut microbiome to prevent radiation-induced injury. We administered mice with soluble allomelanin and observed its effect on the intestinal physiology and body weight. We then established a melanized bacterial strain in probiotic E. coli Nissle. We measured the body weight of the mice treated with melanized E. coli Nissle. We showed the enhanced bacterial abundance and colonization of the melanized bacteria E. coli Nissle in the intestine. Melanized E. coli Nissle colonized the colon in less than 3 h and showed consistent colonization over 24 h post one oral gavage. We did not find significant changes of bodyweight in the mice treated with melanized bacteria. We did not observe any inflammation in the intestine. These results demonstrate the safety of soluble melanin and melanin-producing bacteria and will support the future studies to treat radiation-induced injuries and restore dysbiosis.
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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) | 0.001 | 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".