Synergistic Effect of Tazobactam on Amikacin MIC in Acinetobacter baumannii Isolated from Burn Patients in Tehran, Iran
Bibliographic record
Abstract
BACKGROUND: Burn is still an important global public health challenge. Wound colonization of antibiotic resistant bacteria such as Acinetobacter baumannii can lead to high morbidity and mortality in burn patients. The aim of this study was to evaluate the inhibitory effect of tazobactam on efflux pump, which can cause aminoglycoside resistant in A. baumannii isolated from burn patients. METHODS: In this study, 47 aminoglycoside resistant A. baumannii spp. were obtained from burn patients, admitted to the Shahid Motahari Burns Hospital in Tehran, Iran, during June-August 2018. The inhibitory effect of tazobactam against adeB such as efflux pump was evaluated by Minimum Inhibitory Concentration (MIC) determination of amikacin alone and in combination with tazobactam. Fractional Inhibitory Concentration index (FIC) was used to determine the efficacy of tazobactam/ amikacin combination. Further, semi-quantitative Real- Time PCR was performed to quantify the expression rates of the adeB gene before and after addition of tazobactam/amikacin. RESULTS: The MIC values were significantly reduced when a combined amikacin and tazobactam was utilized. The most common interaction observed was synergistic (78.2%), followed by.additive effects (21.8%), as per FIC results. The adeB mRNA expression levels were found to be downregulated in 60.7% of isolates treated with tazobactam. CONCLUSION: Tazobactam can have impact on resistance to aminoglycoside by inhibiting efflux pump. Thus, the combination of tazobactam with amikacin can be used as an alternative treatment approach in multidrug resistant A. baumannii infections.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".