Lyoprotection and stabilization of laccase extract from Coriolus hirsutus, using selected additives
Bibliographic record
Abstract
The development of stable lyophilized laccase, obtained from Coriolus hirsutus, using a wide range of temperature treatments and storage conditions, was investigated. Using selected lyoprotectants, including, dextran 6 kDa, sucrose and a mixture of sodium benzoate, potassium sorbate and sorbitol (BKSS) (1.5:1.0:98.5; w/w/w) resulted by 2.4, 1.4 and 1.8-fold increase in laccase activity after lyophilization as compared to the fresh enzyme, respectively, whereas the addition of mannitol preserved 98.2% of its activity. Using 2.5% (w/v) dextran (15–25 kDa) or mannitol appeared to be the most appropriate lyoprotectants for the laccase activity. The laccase stability of the lyophilized enzymatic extract was greatly enhanced with the presence of mannitol, with 96.2, 38.9 and 24.7% of residual activity after 4 weeks of storage at − 80, 4 and 25 °C, respectively. The inactivation constant (kinactivation) value and the amount required to decrease 50% of the laccase activity (C1/2) showed that Carbowax® polyethylene glycol (PEG)-8000 was the most appropriate additive for laccase activity, followed by glycerol and CuSO4. When the enzymatic extract was incubated at 50 °C in the presence of either CuSO4, PEG-8000 or glycerol, the time required to decrease 50% of the laccase initial activity (t50), were 52.9, 54.6, 50.2 h, respectively, as compared to that of the control trial of 38.9 h.
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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) | 0.000 | 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".