Peptides IPVP and LPIA Induce Insulin Secretion in a 2-Tiered Caco-2-STC-1/BRIN-BD11 Cell Model
Bibliographic record
Abstract
The insulin release effects of two peptides, IPVP and LPIA, derived from a brewers’ spent grain (BSG) protein hydrolysate, were investigated using a two-tiered Caco-2 and STC-1 (apical: intestinal and enteroendocrine cells, respectively)/BRIN-BD11 (basolateral: pancreatic cells) cell model. Both peptides significantly enhanced insulin secretion in BRIN-BD11 cells (29.64 ± 3.30 and 28.30 ± 1.98 pM insulin for LPIA and IPVP, respectively) following their inclusion on the apical side. However, the direct exposure of BRIN-BD11 cells to peptides did not induce significant changes in insulin secretion, suggesting an indirect mode of action. LPIA significantly increased glucagon-like peptide-1 (GLP-1) levels (43.83 ± 9.25 pM), a known enhancer of insulin release, after 2 h of incubation during Caco-2 and STC-1 cell coculture. Additionally, IPVP and LPIA inhibited dipeptidyl peptidase-IV (DPP-IV) activity in vitro with IC 50 values of 38.96 ± 1.26 μM and 31.20 ± 1.15 μM, respectively, and in situ using Caco-2 cells with IC 50 values of 58.42 ± 0.45 μM and 59.01 ± 6.54 μM, respectively. The inhibition was via a noncompetitive mixed-type mechanism, and they resisted DPP-IV degradation. These findings highlight the therapeutic potential of IPVP and LPIA in type 2 diabetes management via GLP-1- and DPP-IV-related pathways and warrant further molecular and clinical-level investigations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".