Study of Catalytic Activity of Lipase and Lipase-Chitosan Complexes in Dynamics
Bibliographic record
Abstract
Pancreatic porcine lipase (PPL) is the unique enzyme in numerous biochemical processes for human and animals. Stability in time is the critical point for many enzymes in relation to their further applications. The effect of chitosan on the activity of PPL during 2 months was investigated. Materials and Methods: Potentiometric method was used to study the catalytic activity of enzymes that based on measuring of the potential an electrode immersed in a triacetin (substrate) solution by titration with 0.01 M NaOH. A laboratory pH-stat with combined pH electrode was used for the measurements of this potential in the presence of PPL or PPL-chitosan complexes. Standard experimental conditions: 40 ºC, pH 7.0. Results: The following PPL activity values (data given in % to the activity of free lipase) vs. time (in 10 min. intervals) were obtained: 100%, 97.4%, 89.9%, 82.9%, 77.9% and 75.9% after 10, 20, 30, 40, 50 and 60 min., respectively. The PPL catalytic activity decreased at about ¼ to ½ of the initial values after 7 or 14 days, 1 or 2 months storage, although there were differences in the decline dynamics. The complex PPL:Chit=50:1 has better stabilizing properties as compared to other complexes; does not strongly inhibit lipase and requires a small amount of carrier (chitosan) for its formation. Conclusions: The complex PPL:Chit=25:1 is less effective as the PPL:Chit=50:1 complex, but both can be applicable in some industrial processes.
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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 | 0.001 |
| 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".