Evidence map›Paper›PMID 42170663›Full record

ArticleChina CDC weekly2026

Haijian Zhou, Chongye Guo, Guomei Fan, Jinrui Hu, Zhigang Cui, Xiaoli Du, Linhuan Wu, Biao Kan

Abstract read
In one paragraph

Article in China CDC weekly, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Haijian ZhouNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Chongye GuoChinese National Microbiology Data Center (NMDC), Beijing, China.
Guomei FanChinese National Microbiology Data Center (NMDC), Beijing, China.
Jinrui HuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Zhigang CuiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Xiaoli DuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Linhuan WuChinese National Microbiology Data Center (NMDC), Beijing, China.
Biao KanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: To address the escalating public health threat of hypervirulent and antibiotic-resistant Methods: KPGD integrates 75,987 genome assemblies from 122 countries with standardized annotations of serotypes, sequence types, antibiotic resistance genes (ARGs), virulence factors (VFs), and mobile genetic elements (MGEs). The platform offers interactive visualization modules and integrated analytical tools that enable real-time epidemiological monitoring and one-stop genomic analysis, thereby supporting global efforts to track the dissemination of resistant and hypervirulent KP (HvKp) and to inform infection control and antimicrobial stewardship strategies. Results: Longitudinal analyses revealed that the emergence of HvKp is driven by the sustained expansion of carbapenem-resistant high-risk lineages under selection pressure from restricted, higher-tier antibiotics. Conjugative ARG-bearing plasmids carrying key resistance determinants largely mediate this expansion. In contrast, selection by first-line, narrower-spectrum antibiotics appears to favor the dissemination of virulence plasmids (predominantly IncFIB types) as a compensatory mechanism to offset resistance-associated fitness costs. Conclusion: These findings collectively underscore the need for surveillance systems that simultaneously monitor high-risk lineages and the dissemination of ARGs and VFs - particularly via self-transmissible plasmids - to better understand and anticipate bacterial adaptation under diverse antibiotic pressures.

Indexed as

carbapenem-resistant high-risk lineagesdatabasegenomic surveillancehypervirulentKlebsiella pneumoniae

Identifiers

PMID42170663
PMCPMC13187654

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.