ArticleAntibiotics (Basel, Switzerland)2023
A Comprehensive Study of Historical Detection Data for Pathogen Isolates from U.S. Cattle.
Article in Antibiotics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Analysis of Multi-Antimicrobial Resistance Patterns in U.S. Foodborne Pathogens (2015-2025) Using Data from the NCBI Pathogen Isolates Browser.Pathogens (Basel, Switzerland) · 2025Article
- Investigation of the Antimicrobial Resistance of Important Pathogens Isolated from Poultry from 2015 to 2023 in the United States.Pathogens (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Foodborne pathogens pose substantial health hazards and result in considerable economic losses in the U.S. Fortunately, the National Center for Biotechnology Information Pathogen Detection Isolates Browser (NPDIB) provides valuable access to antimicrobial resistance (AMR) genes and antimicrobial assay data. This study aimed to conduct the first comprehensive investigation of AMR genes in pathogens isolated from U.S. cattle over the past decade, driven by the urgent need to address the dangers of AMR specifically originating in pathogens isolated from U.S. cattle. In this study, around 28,000 pathogen isolate samples were extracted from the NPDIB and then analyzed using multivariate statistical methods, mainly principal component analysis (PCA) and hierarchical clustering (H-clustering). These approaches were necessary due to the high dimensions of the raw data. Specifically, PCA was utilized to reduce the dimensions of the data, converting it to a two-dimensional space, and H-clustering was used to better identify the differences among data points. The findings from this work highlighted
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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.