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◆ Frontiers in bioengineering and biotechnology2026-01-01

CRISPR/Cas diagnostics for Klebsiella pneumoniae: from resistance and hypervirulence markers to clinically interpretable risk reporting.

Weijia Yi, Hongzhi Luo, Fukang Luo, Qianyi Li, Xiaoxue Huang, Qiujie Yu, Guozhen Deng, Shan Liu

原始摘要(英文原文)· Original abstract
Klebsiella pneumoniae (Kp) is a major cause of healthcare-associated and community-onset invasive infections. The emergence of carbapenem-resistant Kp (CRKP), hypervirulent Kp (hvKp), and resistance-hypervirulence convergence has increased the need for rapid tests that provide organism context and clinically interpretable risk information. CRISPR/Cas diagnostics combine programmable sequence recognition with flexible signal generation and can be integrated with isothermal amplification, portable readouts, and multiplex workflows. Available studies support the analytical feasibility of detecting Kp- or Klebsiella pneumoniae species complex (KpSC)-associated organism-context targets, major resistance genes, and hypervirulence-associated markers, but clinical and workflow validation remains limited because many studies use small cohorts, cultured isolates, or spiked matrices. Here, clinically interpretable risk reporting means translating validated molecular findings into bounded categories-organism context, gene-associated resistance risk, hypervirulence-associated risk, and suspected resistance-hypervirulence convergence risk-while stating interpretive limits and appropriate confirmatory actions. We critically evaluate CRISPR/Cas engineering, target selection, multiplexing, specimen-specific workflows, and validation requirements, and propose a conceptual four-layer target-to-report framework for prospective evaluation. CRISPR/Cas-based Kp testing should complement, rather than replace, culture, phenotypic antimicrobial susceptibility testing, virulence assessment, and genomic confirmation.
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CRISPR/Cas diagnostics for Klebsiella pneumoniae: from resistance and hypervirulence markers to clinically interpretable risk reporting. — 科研速览 Science Skim