Catechin to Catechol Biotransformation in Lactobacillus Hilgardii and Paracasei: Detection and Optimization
Bibliographic record
Abstract
Type II diabetes (T2D) constitutes approximately 90 percent of diabetes cases in Canada. If left untreated, the high blood sugar that results from the illness can precipitate stroke, blindness, and other complications. Many current oral medications for T2D have severe side-effects, but catechol, a natural biomolecule, may be an alternative. Although only trace amounts of catechol occur in fruits and vegetables, one of its biosynthetic pathways starts with the compound catechin, which is common in berries and green tea. Bacteria native to the human gut can degrade catechin into catechol, so we aimed to optimize this biotransformation in Lactobacillus hilgardii and paracasei. Since many environmental factors affect bacterial growth, we also sought to optimize the number of experiments using a statistical method called design of experiment (DOE). To our knowledge, this is the first application of a DOE to bacterial growth. We grew L. hilgardii and L. paracasei at 35 °C while varying carbon dioxide levels, glucose levels, and the density of bacteria at which we fed cultures with catechin. We monitored colonies' growth by measuring their optical density with a UV-Vis spectrometer, and we fed them once this measurement matched the value indicated in the DOE. For each experiment, we incubated three samples for 24 hours, and one for 48. We then quantified the amount of catechol produced using high-performance liquid chromatography. Preliminary results suggest that catechol production varies based on the strain and environment, and L. hilgardii biotransformation seems more efficient in slightly anaerobic conditions than aerobic ones.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".