Effect of the Novel Polysaccharide PolyGlycopleX® on Short-Chain Fatty Acid Production in a Computer-Controlled in Vitro Model of the Human Large Intestine
Bibliographic record
Abstract
Many of the health benefits associated with dietary fiber are attributed to their fermentation by microbiota and production of short chain fatty acids (SCFA). The aim of this study was to investigate the fermentability of the functional fiber PolyGlyopleX® (PGX®) in vitro. A validated dynamic, computer-controlled in vitro system simulating the conditions in the proximal large intestine (TIM-2) was used. Sodium hydroxide (NaOH) consumption in the system was used as an indicator of fermentability and SCFA and branched chain fatty acids (BCFA) production was determined. NaOH consumption was significantly higher for Fructooligosaccharide (FOS) than PGX, which was higher than cellulose (p = 0.002). At 32, 48 and 72 h, acetate and butyrate production were higher for FOS and PGX versus cellulose. Propionate production was higher for PGX than cellulose at 32, 48, 56 and 72 h and higher than FOS at 72 h (p = 0.014). Total BCFA production was lower for FOS compared to cellulose, whereas production with PGX was lower than for cellulose at 72 h. In conclusion, PGX is fermented by the colonic microbiota which appeared to adapt to the substrate over time. The greater propionate production for PGX may explain part of the cholesterol-lowering properties of PGX seen in rodents and humans.
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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".