Optimization of growth media for enhanced production of laccase by <i>Cryptococcus albidus</i> and its application for bioremediation of chemicals
Bibliographic record
Abstract
Cryptococcus albidus, isolated from the sediments of Century Pulp and Paper Mill, Lalkuan, Nainital, Uttarakhand, India, produced a copper containing oxidase, laccase, that was capable of degrading environmental pollutants. Bagasse was the most efficient inducer for laccase production. The Taguchi approach was used to optimize the growth media for five factors, i.e., pH, copper sulphate, carbon, nitrogen, and the inducer at four levels using an M-16 orthogonal array. The optimum conditions for laccase production were pH (6), CuSO4 (2 mmol/L), meat peptone (0·5%), glucose (0·1%), and bagasse (1·0%). After optimization, laccase production increased seven times from 32 to 219 IU/mg. The inducer (bagasse) had maximum effect on laccase production leading to 52% increase, while pH had minimum effect with 7% increase. Growth media with laccase activity (2 U/mL) was applied for the bioremediation of dyes, effluent, and chemical compounds. These experiments showed that the growth media with laccase activity (2 U/mL) produced by Cryptococcus albidus had good potential for bioremediation of toxic and recalcitrant compounds. Further, the laccase enzyme extracted from the growth media was fractionated by DEAE-cellulose ion-exchange chromatography, and the molecular weight of the enzyme determined by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS – PAGE) was found to be 64 kDa. The activity of laccase was confirmed by native PAGE, in which ABTS was used for staining gel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".