Precise Detection of Carbapenem-Resistant and Hypervirulent <i>Klebsiella pneumoniae</i> Using MOF-Derived Bimetallic Nanocube Hybrid Nanosheet
Bibliographic record
Abstract
Carbapenem resistance and hypervirulence represent two distinct evolutionary pathways in Klebsiella pneumoniae, posing significant challenges in clinical settings. Of particular concern are convergent strains that combine both traits, complicating timely diagnosis and treatment. Herein, we present a novel MOF-derived bimetallic nanocube hybrid nanosheet (denoted Pt-G@Cu 5 Zn 8 C@Au) designed to enhance laser desorption/ionization mass spectrometry (LDI-MS) in distinguishing convergent strains from other variants. The novel material, synthesized through the pyrolysis of pristine MOFs, features uniformly distributed Cu and Zn synergistic metal sites within the carbon matrix, addressing critical limitations of current nanomatrices for in situ extraction of metabolic fingerprints from microbial cells, such as limited sensitivity (e.g., amorphous silicon, TiO 2, and metal nanoparticles) or relatively weak conductivity and stability (MOF-based materials). Utilizing this advanced matrix, the metabolic fingerprints of 248 K. pneumoniae isolates were rapidly extracted, identifying 23 top VIP-score peaks as potential biomarkers for differentiating convergent strains from their variants. Combined with machine learning, the prediction model achieved 100% accuracy in distinguishing convergent strains from carbapenem-sensitive isolates (CS_cKP) or hypervirulent isolates (hvKP) using the SVM model, while achieving 78.26% accuracy in differentiating them from carbapenem-resistant isolates (CR_cKP) with the KNN/NB models. These findings highlight the high accuracy and efficacy of our assay in distinguishing convergent strains from their variants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".