High hydrostatic pressure enhances whey protein digestibility to generate whey peptides that improve glutathione status in CFTR‐deficient lung epithelial cells
Bibliographic record
Abstract
Whey protein isolates (WPI) may provide anti-inflammatory benefits to cystic fibrosis (CF), which could be mediated via peptides, as proteolytic digests of WPI enhance intracellular glutathione (GSH) concentrations. The objectives of this study were to investigate whether high hydrostatic pressure can (i) improve the in vitro digestibility of WPI; and (ii) generate low molecular weight (< 1 kDa) peptides from WPI hydrolysates that exert GSH-enhancing and anti-inflammatory properties in wild type and mutant CF transmembrane conductance regulator (CFTR) tracheal epithelial cells. Hydrostatic pressure processing enhanced the in vitro digestibility of WPI to proteolytic enzymes resulting in altered peptide profiles as assessed by CZE and GC-MS. The exposure of mutant CFTR cells to low molecular weight (< 1 kDa) peptides isolated from WPI hydrolysates exposed to pressure processing (pressurized WPI hydrolysates, pWPH), showed increased intracellular levels of reduced GSH and total GSH relative to treatment with peptides obtained from native WPI hydrolysates (nWPH). A tendency for decreased interleukin-8 secretion was associated with the pWPH and nWPH treatments in mutant CFTR cells, which was not observed in wild type cells. Hydrostatic pressure processing of whey proteins appears to enhance their impact on cellular GSH status in cells with the mutant CFTR condition.
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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".