Exploring the antimicrobial effects of a phenolic-rich extract from jabuticaba depulping waste against enterotoxigenic <i>Escherichia coli</i>
Bibliographic record
Abstract
This study evaluated the effects of a phenolic-rich extract from jabuticaba [Myrciaria jaboticaba (Vell.) Berg] depulping waste (PEJ) on the survival, antibiotic susceptibility, virulence, and cellular functions of various enterotoxigenic Escherichia coli (ETEC) strains. The minimum inhibitory concentration of PEJ against the five tested ETEC strains was 125 mg mL-1. PEJ at 125 and 250 mg mL-1 caused reductions in viable cell counts of ≥ 3 and ≥ 5 log CFU mL-1 in ETEC over 24 h, respectively. PEJ at subinhibitory concentrations (31.25 and 62.5 mg mL-1) reduced the viable cell counts of ETEC when exposed to in vitro gastrointestinal conditions, besides decreasing the biofilm formation, cell surface hydrophobicity, mucin adhesion, and swimming and swarming motility. PEJ (31.25 and 62.5 mg mL-1) increased the susceptibility of the tested ETEC strains to various clinically relevant antibiotics. The exposure to PEJ (62.5 and 125 mg mL-1) impaired the membrane permeability and enzymatic and efflux pump activities in ETEC cells. PEJ effectively reduces survival, increases antibiotic susceptibility, and attenuates virulence in ETEC. These effects could be linked to a PEJ multi-target action disturbing various cellular functions in ETEC cells. PEJ could be a candidate for developing innovative solutions to prevent and treat ETEC infections.
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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) | 0.000 | 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) | 0.001 | 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".