55 Novel nanofibrous mats for effective delivery of sodium metabisulfite (SMBS) to the intestinal tract of pigs
Bibliographic record
Abstract
Abstract Deoxynivalnol (DON) occurs on many commonly used cereal grains. DON levels as low as 0.6 to 2.0 ppm in complete feed cause a reduction in feed intake and growth rate, damage to the intestinal epithelial cells and increased susceptibility to enteric pathogen challenge. Sodium metabisulfite (SMBS) is a sulfite reducing agent commonly added to animal feeds to improve protein digestibility by cleaving disulfide bonds. SMBS can destroy 70%-100% of DON in processed grains or feeds in vitro with 0.45%-0.9% levels at pH around 6.5 but not at acidic conditions. Therefore, little SMBS will remain intact in the small intestine, where an optimal pH environment exists for SMBS to detoxify DON. Thus, the aim of this study is to encapsulate SMBS into nanofiber mats by an electrospinning technology to deliver intact SMBS to the lower gut such as the small intestine to detoxify DON effectively. Electrospinning was implemented to prepare nanofiberous mats from a suspension of particulate SMBS in the solution of a pH sensitive polymer (Eudragit L100-55). The highest loading capacity and loading efficiency of SMBS achieved in the nanofiberous mats were 32.00% and 80.01%, respectively. In vitro release studies showed that 12.23%-19.48% of encapsulated SMBS was released in the simulated gastric fluid (SGF) in 2 h and 66.01%-95.86% of SMBS release was observed in the simulated intestinal fluid (SIF) within 1 h. Additionally, in an in vitro DON detoxification experiment using IPEC-J2 cells, the ability of DON detoxification was reflected by significantly increasing the cell viability of DON and encapsulated SMBS nanofiber group (82.99%) compared with the cell viability of DON group (62.19%) (P < 0.05). For the first time, we demonstrate successful encapsulation of particulate SMBS in a nano-vehicle (electrospun nanofiber), achieve > 50% release of SMBS in SIF, and effective detoxification of DON in an in vitro cell assay.
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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) | 0.000 | 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".