LiF Reduces MICs of Antibiotics against Clinical Isolates of Gram-Positive and Gram-Negative Bacteria
Bibliographic record
Abstract
Antibiotic resistance is an ever-growing problem yet the development of new antibiotics has slowed to a trickle, giving rise to the use of combination therapy to eradicate infections. The purpose of this study was to evaluate the combined inhibitory effect of lithium fluoride (LiF) and commonly used antimicrobials on the growth of the following bacteria: Enterococcus faecalis, Staphyloccoccus aureus, Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae, Serratia marcescens, and Streptococcus pneumoniae. The in vitro activities of ceftazidime, sulfamethoxazole-trimethoprim, streptomycin, erythromycin, amoxicillin, and ciprofloxacin, doxycycline, alone or combined with LiF were performed by microdilution method. MICs were determined visually following 18-20 h of incubation at 37°C. We observed reduced MICs of antibiotics associated with LiF ranging from two-fold to sixteen-fold. The strongest decreases of MICs observed were for streptomycin and erythromycin associated with LiF against Acinetobacter baumannii and Streptococcus pneumoniae. An eight-fold reduction was recorded for streptomycin against S. pneumoniae whereas an eight-fold and a sixteen-fold reduction were obtained for erythromycin against A. baumannii and S. pneumoniae. This suggests that LiF exhibits a synergistic effect with a wide range of antibiotics and is indicative of its potential as an adjuvant in antibiotic therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".