Screening, Production and Optimization of L-Asparaginase From Soil Bacteria Isolated in Ibadan, South-western Nigeria
Bibliographic record
Abstract
L-Asparaginase producing bacteria were isolated from soil samples under optimum conditions using submerged fermentation. Their abilities to produce asparaginase at different pH, temperature, incubation temperature, utilization of different carbon and nitrogen sources as well as their specific conditions for optimal activity were also investigated. Streptococcus spp. D1, Bacillus polymyxa and Streptococcus spp. D2 showed optimum asparaginase production at pH 8 with activities of 11.6 U/ml, 8.8 U/ml and 7.9 U/ml respectively. The pH 7 was observed as optimum pH for Bacillus firmus (8.8 U/ml) while optimum pH 6 was observed for Bacillus circulans and Paenibacillus validus. Maximum L-asparaginase productivity was attained at a temperature of 45°Cby Bacillus firmus, Streptococcus sp. D1 and Bacillus circulans with activity of 4.6 U/ml, 5.6 U/ml and 3.8 U/ml respectively while 35°C incubation temperature was optimum for Paenibacillus validus and Bacillus polymyxa with enzyme activity of 6.2 U/ml and 6.1 U/ml respectively. Mannitol supported the maximum asparaginase production of Bacillus circulans, Streptococcus sp. D2 and Bacillus polymyxa while maltose was observed as the best carbon source for Streptococcus sp. D1 and Bacillus firmus; and sucrose for Paenibacillus validus. The optimum nitrogen source was casein for Bacillus circulans (7.57 U/ml) and Streptococcus spp. D1 (6.19 U/ml), Yeast extract for Bacillus polymyxa (7.037 U/ml)and Bacillus firmus(5.368 U/ml) while NaNO3 supported optimum L-asparaginase production for Streptococcus sp. D2 (6.006 U/ml) and Paenibacillus validus (4.754 U/ml). At optimum conditions, Bacillus polymyxa had the highest (4.835 U/ml) while Bacillus circulans had the least (2.981 U/ml) asparaginase activity. In all, the bacterial isolates prefers slightly alkaline to alkaline medium (pH 6-8) for optimum asparaginase production.
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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 | 0.001 |
| 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 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".