ArticleEmerging infectious diseases2025
Attribution of Salmonella enterica to Food Sources by Using Whole-Genome Sequencing Data.
Article in Emerging infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Factors Influencing Biofilm Formation ofFoods (Basel, Switzerland) · 2026Article
- Assessing transcriptomic responses ofMicrobiology spectrum · 2026Article
- Review
- Genomic epidemiology ofProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Asymptomatic Carriage and Antimicrobial Resistance ofMicroorganisms · 2026Article
- Antibiotic resistance profiles and genetic characterization of Salmonella enterica from water supplies in Kaduna State, Northwest Nigeria.BMC microbiology · 2025Article
- Genotypes and associated traits inFrontiers in microbiology · 2025Article
- Exploring the genetic landscape of the Copper Homeostasis and Silver Resistance Island (CHASRI) in Salmonella enterica.PloS one · 2025Article
- Tracing microbial hazards in the aquatic supply chain: challenges, technologies, and future directions.Frontiers in nutrition · 2025Review
- Longitudinal study highlights patterns ofFrontiers in microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Salmonella enterica bacteria are a leading cause of foodborne illness in the United States; however, most Salmonella illnesses are not associated with known outbreaks, and predicting the source of sporadic illnesses remains a challenge. We used a supervised random forest model to determine the most likely sources responsible for human salmonellosis cases in the United States. We trained the model by using whole-genome multilocus sequence typing data from 18,661 Salmonella isolates from collected single food sources and used feature selection to determine the subset of loci most influential for prediction. The overall out-of-bag accuracy of the trained model was 91%; the highest prediction accuracy was for chicken (97%). We applied the trained model to 6,470 isolates from humans with unknown exposure to predict the source of infection. Our model predicted that >33% of the human-derived Salmonella isolates originated from chicken and 27% were from vegetables.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.