Phaseolin type and heat treatment influence the biochemistry of protein digestion in the rat intestine
Bibliographic record
Abstract
The study aimed to investigate the in vivo digestion of Phaseolus vulgaris phaseolin types differing in their subunit pattern composition. Diets contained either casein as the sole source of protein or a mixture (1:1) of casein and pure Sanilac (S), Tendergreen (T) or Inca (I) phaseolin either unheated or heated. Rats were fed for 11 d with the experimental diets. Their ileal content and mucosa were collected and prepared for electrophoresis, Western blotting, densitometry and MS. Differences in digestion among native phaseolin types were observed for intact phaseolin at molecular weights (MW) of 47-50.5 kDa and for an undigested fragment at MW of 19-21.5 kDa in ileal digesta. In both cases, the concentration of these protein bands was lower for I phaseolin than for S or T phaseolin (P < 0.05). In the mucosa, the concentration of a protein band at MW of 20.5-21.5 kDa was lower for S phaseolin as compared to T or I phaseolin (P < 0.001). The presence of phaseolin subunits and their fragments was confirmed by Western blotting. MS analysis revealed the presence of undigested alpha and beta subunit fragments from phaseolin and endogenous proteins (anionic trypsin I and pancreatic alpha-amylase) in ileal digesta. Thermal treatment improved digestion (P < 0.01), acting on both dietary and endogenous protein components. In conclusion, this study provides evidence for differences in intestinal digestion among phaseolin types, S phaseolin being more resistant and I phaseolin more susceptible. These differences were affected by the origin of the phaseolin subunit precursor. Heat treatment enhanced phaseolin digestion.
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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".