Navigating the Current Treatment Landscape of Metallo-β-Lactamase-Producing Gram-Negative Infections: What are the Limitations?
Bibliographic record
Abstract
The spread of carbapenemase-producing gram-negative pathogens, especially those producing metallo-β-lactamases (MBLs), has become a major health concern. MBLs are molecularly the most diverse carbapenemases, produced by a wide spectrum of gram-negative organisms, including the Enterobacterales, Pseudomonas spp., Acinetobacter baumannii, and Stenotrophomonas maltophilia, and can hydrolyze most β-lactams using metal ion cofactors in their active sites. Over the years, the prevalence of MBL-carrying isolates has increased globally, particularly in Asia. MBL infections are associated with adverse clinical outcomes including longer length of hospital stay, ICU admission, and increased mortality across the globe. The optimal treatment for MBL infections not only depends on the pathogen but also on the underlying resistance mechanisms. Currently, there are only few drugs or drug combinations that can efficiently offset MBL-mediated resistance, which makes the treatment of MBL infections challenging. The rising concern of MBLs along with the limited treatment options has led to the need and development of drugs that are specifically targeted towards MBLs. This review discusses the prevalence of MBLs, their clinical impact, and the current treatment options for MBL infections and their limitations. Furthermore, this review will discuss agents currently in the pipeline for treatment of MBL 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".