A short-term bioreactor assay to assess the effect of essential oils on a microbiota derived from piglet’s intestinal content
Bibliographic record
Abstract
Abstract Background Modulating the microbiota is an emerging way to improve pig health. In-vitro bioreactor systems can be used to reproduce intestinal microbiota to study modulating avenues. In this study, a continuous feeding system to support a microbiota derived from piglet colonic contents, over 72 h, was developed. The microbiota from piglets was collected and used as inoculum. The culture media was derived from an artificial digestion of piglet feed. The microbiota diversity in time, the reproducibility between replicates and the diversity of the bioreactor microbiota compared to the inoculum was assessed. Essential oils were used as a proof of concept to assess the in vitro microbiota modulation. The microbiota diversity was assessed by 16S rRNA amplicon sequencing. Quantitative PCR was also used for total bacteria, lactobacilli and Enterobacteria. Results At the start of the assay, the bioreactor microbiota diversity was similar to the inoculum. Time and replication affected the bioreactor microbiota diversity. Between 48 and 72 h, no statistical variation of the microbiota diversity was observable. After a 48 h running period, thymol and carvacrol were added at 200 ppm or 1000 ppm for 24 h. No microbiota modification was observed by sequencing. Quantitative PCR results showed a significant growth of lactobacilli when thymol was used at 1000 ppm, where only a trend was observed with the 16S analysis. Conclusions This study presents a bioreactor assay that can be used as a tool for rapid screening of additives and suggests that the effects of essential oils on the microbiota are subtle, acting against a few bacterial genera.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".