Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
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
13 authors.
Michelle Baker *Faculty of Life Sciences & Medicine, School of Immunology and Microbial Sciences, Department of Infectious diseases, King's College London, London, UK.
Xin Lu *National 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, Beijing, China.ORCID 0000-0001-6002-028X
Chengchang Luo *Centre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.ORCID 0009-0003-5005-6993
Ge WuCentre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.
Alexandre Maciel-GuerraCentre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.
Komkiew PinpimaiCentre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.
Ming LuoYulin Center for Disease Control and Prevention, Yulin, China.
Mengyu WangNational 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, Beijing, China.
Yuan ZhangNational 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, Beijing, China.
Weihua MengCentre for Smart Food Research, Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China.ORCID 0000-0001-5388-8494
Baoli ZhuKey Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0001-5326-9503
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, Beijing, China. kanbiao@icdc.cn.ORCID 0000-0002-6141-4552
Tania DottoriniFaculty of Life Sciences & Medicine, School of Immunology and Microbial Sciences, Department of Infectious diseases, King's College London, London, UK. tania.dottorini@kcl.ac.uk.ORCID 0000-0001-7733-906X
Funding
RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/W020424/2RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) BB/X017370/2RCUK | Medical Research Council (MRC) JPIAMR2023-DISTOMOS-144 and MR/Y034422/2RCUK | Medical Research Council (MRC) MR/X009246/2
6 · The paper itself
Abstract
Antimicrobial resistance (AMR) is rising globally, yet factors shaping its genomic evolution remain poorly understood. We investigated how socioeconomic, climate, and health factors are associated with AMR spread in multi-drug resistant (MDR) Salmonella enterica across China's food chain, mapped hotspots and forecasted genomic trait prevalence to 2050. We analysed 2,536 MDR genomes from 15 provinces over 16 years, covering patients, food and non-food workers, meat, livestock, and environmental sources. A machine learning framework integrated genomic surveillance with risk assessment to identify high-risk AMR traits (clinically relevant, MDR, mobile). Regression models linked trait prevalence to socioeconomic, climate, health, and antimicrobial consumption data, informing projections to 2050. Eighty-four high-risk traits, including carbapenem, colistin, and β-lactam resistance, are projected to rise, with antibiotic use, population growth, healthcare spending, and urbanisation. Projections were trait-specific: bla
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.
High-risk antimicrobial resistance genomic traits in multidrug-resistant Salmonella across China's food chain: projections to 2050. · full record | OpenQuestion