Effect of <i>Lactobacillus plantarum</i> NCU116 Fermentation on <i>Asparagus officinalis</i> Polysaccharide: Characterization, Antioxidative, and Immunoregulatory Activities
Bibliographic record
Abstract
Lactic acid fermentation represents a novel method to produce bioactive functional ingredients, including polysaccharides. In this work, a selected lactic acid bacteria strain NCU116 was used to ferment Asparagus officinalis (asparagus) pulps. Two polysaccharides were subsequently separated from both unprocessed and fermented asparagus pulps, namely, asparagus polysaccharide (AOP) and fermented-AOP (F-AOP). The physicochemical and bioactive properties of AOP and F-AOP were characterized and investigated. High-performance anion-exchange chromatography showed that fermentation increased the proportions of rhamnose, galacturonic acid, and glucuronic acid in polysaccharides by 46.70, 114.09, and 12.75‰, respectively. High-performance size-exclusion chromatography revealed that fermentation decreased the average molecular weight from 181.3 kDa (AOP) to 152.8 kDa (F-AOP). Moreover, the fermentation reduced the particle size and changed the rheology property. In vitro, F-AOP displayed superior free radical scavenging properties compared to AOP, using 2,2-diphenyl-1-picryhydrazyl, hydroxyl, and superoxide anion radical scavenging assays. In vivo, F-AOP administration dose-dependently promoted a gradual shift from Th17-dominant acute inflammatory response (IL-17 and RORγt) to Th1-dominant defensive immune response (IFN-γ and T-bet). These results indicated that the Lactobacillus plantarum NCU116 fermentation was practical and useful to obtain promising bioactive polysaccharides.
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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) | 0.001 | 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".