Guava Leaf Extract Exhibits Antimicrobial Activity in Extensively Drug-Resistant (XDR) Acinetobacter baumannii
Bibliographic record
Abstract
Currently, a global health crisis is being caused by microbial resistance, in which Acinetobacter baumannii plays a crucial role, being considered the highest-priority microorganism by the World Health Organization (WHO) for discovering new antibiotics. As a result, phytochemicals have emerged as a potential alternative to combat resistant strains, since they can exert antimicrobial activity through various mechanisms and, at the same time, represent a more natural and safe option. This study analyzes the antimicrobial effects of guava leaf extract in ten clinical isolates of extensively drug-resistant (XDR) A. baumannii, using the agar diffusion technique and the microdilution method to determine the minimum inhibitory concentrations (MICs). Additionally, possible improvements in antimicrobial activity after the purification of polyphenolic compounds and potential synergy with the antibiotic gentamicin are examined in this research. Moreover, the effect of the plant extract in cell line A549 derived from lung tissue was also evaluated. The extract exhibited antimicrobial activity against all the strains studied, and the purification of polyphenols along with the combination with gentamicin improved the extract activity. The presence of the plant extract induced morphological changes in the lung cells after 24 h of exposure. Therefore, Psidium guajava L. leaf extract is a potential antimicrobial agent.
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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".