Detection of Metallo-beta-lactamase Producers Among Clinical Isolates of Enterobacterales in a Tertiary Care Centre, Nepal
Bibliographic record
Abstract
Background and Objective: Increased reports of metallo-beta-lactamase (MBL)producing isolates of Enterobacterales indicate that there are limited therapeutic options available for the treatment of infections caused by these organisms.To the best of our knowledge, there have been only a limited number of studies on MBLproducing Enterobacterales in Nepal.Therefore, this study was conducted to determine the current distribution of MBL-producing isolates in our setting.Methods: This was a cross-sectional study conducted over a period of six months (from August 2023 to January 2024) at the Department of Microbiology, TU Teaching Hospital, Nepal.A total of 243 clinical isolates of Enterobacterales from various clinical specimens were subjected to the Modified Carbapenem Inactivation Method (mCIM) and the EDTA-Modified Carbapenem Inactivation Method (eCIM) for the detection of MBL-producing isolates, according to the standard methodology outlined by the latest Clinical and Laboratory Standards Institute (CLSI) guidelines.Result: A total of 111 (45.70%) isolates were identified as potential carbapenemase producers, based on their resistance or intermediate susceptibility to carbapenems (Meropenem and/or Imipenem) and/or resistance to cephalosporin subclass III (Ceftazidime).Among the total isolates of Enterobacterales, 52 (21.40%) were confirmed as carbapenemase producers by the mCIM method, 45 (18.50%) were confirmed as MBL producers by the eCIM method, while the remaining 7 (2.90%) were identified as serine-carbapenemase producers.Multidrug resistance (MDR) was observed in 151 (62.10%) of the total isolates, and all MBL-producing isolates were also MDR.Conclusion: This study found a higher percentage of MBL-producing Enterobacterales isolates, along with a high prevalence of multidrug resistance.Regular surveillance and stringent infection control policies are essential to minimize the spread of resistant strains.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".