Effect of bovine lactoferrin on the minimum inhibitory concentrations of ampicillin and trimethoprim–sulfamethoxazole for clinical <i>Shigella </i>spp. strains<sup>1</sup>This article is part of a Special Issue entitled Lactoferrin and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Here, we determined the effect of bovine lactoferrin (bLF) on the minimum inhibitory concentration (MIC) of ampicillin and trimethoprim-sulfamethoxazole in Shigella . Using a microdilution method, the MIC was determined in the presence or absence of bovine lactoferrin (10 mg/mL) on 88 Shigella strains (56 Shigella flexneri , 15 Shigella boydii , 13 Shigella sonnei , and 4 Shigella dysenteriae ) previously isolated from peruvian children <2 years old. A fold change of 2 or more in MIC values was considered significant. For ampicillin, 67 (76%) strains were highly resistant; one-third of the strains (32%) showed a decrease in ampicillin MIC in the presence of LF. This was more typical of MIC values in less resistant strains. For 7 (8%) ampicillin-resistant strains, the decrease in the MIC resulted in the strains reaching the cutoff for susceptible in the presence of bLF. For trimethoprim-sulfamethoxazole, 93% of the isolates (n = 82) were highly resistant and only 4 isolates (5%) decreased their MIC in the presence of bLF. None of the trimethoprim-sulfamethoxazole resistant strains became susceptible in the presence of LF. The decrease in the MIC in the presence of bLF seems to depend on the mechanisms of action of each antibiotic. In vivo studies are needed to further evaluate bLF as a coadjuvant to antibiotic treatment of resistant Shigella.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".