Fibrinolytic enzymes from a newly isolated marine bacterium<i>Bacillus subtilis</i>A26: characterization and statistical media optimization
Bibliographic record
Abstract
A fibrinolytic enzyme producing bacterium was isolated and identified as Bacillus subtilis A26 on the basis of the 16S rRNA gene sequence. The fibrin zymography analysis reveals the presence of at least three fibrinolytic enzymes. The crude enzyme exhibited maximal activity at 60 degrees C and pH 8.0. Medium composition and culture conditions for the enzyme production by B. subtilis A26 were optimized using two statistical methods. The Plackett-Burman statistical design was applied to find the key ingredients and conditions for the best yield of enzyme production. Five significant variables (hulled grain of wheat, casein peptone, NaCl, CaCl2, and initial pH) were selected for the optimization studies. The response surface methodological approach was used to determine the optimal concentrations and conditions. The optimized medium contained 40.0 g.L-1 hulled grain of wheat, 3.53 g.L-1 casein peptone, 4.0 g.L-1 CaCl2, 3.99 g.L-1 NaCl, 0.01 g.L-1 MgSO4, and 0.01 g.L-1 KH2PO4, pH 7.78. The medium optimization resulted in a 4.2-fold increased level of fibrinolytic production (269.36 U.mL-1) compared with that obtained with the initial medium (63.45 U.mL-1). A successful and significant improvement in the production of protease by the A26 strain was accomplished using inexpensive carbon substrate (hulled grain of wheat), allowing a significant reduction in the cost of medium constituents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".