Evaluation of tigecycline activity in clinical isolates among Indian medical centers
Bibliographic record
Abstract
BACKGROUND: Resistance to multiple antibiotics among Gram-positive cocci (GPC) and Gram negative bacilli (GNB) is high in India. Tigecycline, a glycylcycline antibiotic is a newer treatment option for emerging single or multidrug-resistant (MDR) GPC and GNB. MATERIAL AND METHOD: We evaluated the in vitro activity of tigecycline and compared it against other antimicrobials. Between 2005-2007, seven Indian medical centers from diverse geographic regions forwarded 727 isolates [Escherichia coli (166), Staphylococcus aureus (125), Klebsiella spp (120), Streptococcus pneumoniae (102), Enterococcus spp. (100), Pseudomonas aeruginosa (50), Acinetobacter spp. (50) and Enterobacter spp. (14)] from patients with blood stream (BSI), skin and soft tissue (SSTI) including surgical site, urinary tract and respiratory infections to our reference laboratory. Susceptibility to 11 antimicrobials besides tigecycline included: vancomycin, linezolid, teicoplanin, quinopristin-dalfopristin, daptomycin, amikacin, imipenem, levofloxacin, meropenem, and piperacillin/tazobactam was determined by agar dilution and Etest method. RESULT: Tigecycline was active against all GPC (MIC 90 < 0.25 μg/ml), E. coli and Klebsiella spp. (MIC 90 ≤1 μg/ml). MDR Acinetobacter spp. showed lower susceptibility (70.6%) to tigecycline. Tigecycline MIC 90 values were not influenced by oxacillin resistance among S. aureus, S. pneumoniae, vancomycin resistance in Enterococci (VRE) and ESBL producing E. coli, Klebsiella spp. and Enterobacter spp. Increased resistance was seen to other antimicrobials among ESBL producing E. coli, Klebsiella spp., Metallo Beta Lactamase (MBL) producing P. aeruginosa and VRE. CONCLUSION: Tigecycline is an alternative option for emerging multidrug-resistant (MDR) pathogens exhibiting promising spectrum/potency exceeding currently available agents seen in India.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".