Efficient biodegradation of 3-phenoxybenzoic acid and pyrethroid pesticides by the novel strain <i>Klebsiella pneumoniae</i> BPBA052
Bibliographic record
Abstract
A novel Klebsiella pneumoniae strain (BPBA052) capable of degrading 3-phenoxybenzoic acid (3-PBA) was isolated from soybean rhizosphere soil. The strain was obtained by screening after enrichment, isolation, and purification using 3-PBA as the sole carbon and energy source. It could degrade 96.37% of 3-PBA (100 mg/L) within 72 h, and its growth and 3-PBA degradation followed kinetics models of logistic growth (XBPBA052 = 0.0883 × e0.0947t / [1 − 0.0792 × (1 − 0.0883 × e0.0947t)]; μm = 0.0947 h–1, X0 = 0.0883, and Xm = 1.1145) and first-order degradation (CBPBA052 = 101.8194 × e–0.0403t, k = 0.0403, t1/2 = 17.22 h), respectively. Based on Box–Behnken response surface analysis, the optimal temperature, pH, and 3-PBA concentration for K. pneumoniae BPBA052 were 35.01 °C, 7.77, and 150 mg/L, respectively. Moreover, pyrethroid pesticides (PPs) (such as β-cypermethrin, permethrin, bifenthrin, deltamethrin, and fenvalerate) and 3-PBA metabolites (including phenol, catechol, and protocatechuate) were efficiently utilized by BPBA052. We propose a novel microbial metabolic pathway for 3-PBA, based on metabolite identification; enzyme-degrading activity; and cloning of the phenol hydroxylase, catechol 1,2-dioxygenase, and protocatechuate 3,4-dioxygenase genes. This study provides a fundamental platform for further studies to reveal the mechanism of biodegradation of 3-BPA and show K. pneumoniae BPBA052 as a potential microbial resource for bioremediation of environments polluted with 3-PBA or PPs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".