{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:47:10Z","timestamp":1780418830824,"version":"3.54.1"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-BIGDATA Award #: 1838207"],"award-info":[{"award-number":["NSF-BIGDATA Award #: 1838207"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-Convergence Accelerator Phase I Award #: 2040680"],"award-info":[{"award-number":["NSF-Convergence Accelerator Phase I Award #: 2040680"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF-Convergence Accelerator Phase II Award #: 2134901"],"award-info":[{"award-number":["NSF-Convergence Accelerator Phase II Award #: 2134901"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["USDA-NIFA Award #: 2021-68014-34143"],"award-info":[{"award-number":["USDA-NIFA Award #: 2021-68014-34143"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007707","name":"University of California, Davis","doi-asserted-by":"publisher","award":["DataLab's Translational Health Science Fellowship grant"],"award-info":[{"award-number":["DataLab's Translational Health Science Fellowship grant"]}],"id":[{"id":"10.13039\/100007707","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Multidrug resistance (MDR) is frequently evident in\n                    <jats:italic>Streptococcus suis,<\/jats:italic>\n                    generating distinct antimicrobial resistance (AMR) profiles, which limits the effective antimicrobial drug (AMD) options against\n                    <jats:italic>S. suis<\/jats:italic>\n                    in pigs and humans. Despite its significance, there is a lack of studies and pertinent methodologies that uncover complex interactions among AMDs and associated resistance patterns. This study aimed to identify associations between phenotypic resistance patterns of\n                    <jats:italic>S. suis<\/jats:italic>\n                    isolates from swine production systems in the United States against common AMDs using Bayesian network analysis (BNA). Data from 259 unique\n                    <jats:italic>S. suis<\/jats:italic>\n                    isolates collected from 91 farms were included. Phenotypic susceptibility interpretations (resistance\n                    <jats:italic>vs<\/jats:italic>\n                    susceptible) of minimum inhibitory concentrations (MICs) were evaluated for 13 commonly used AMDs: ceftiofur (CEF), penicillin (PEN), enrofloxacin (ENR), gentamicin (GEN), neomycin (NEO), spectinomycin (SPC), sulfadimethoxine (SUL), tiamulin (TIA), tilmicosin (TIL), clindamycin (CLN), chlortetracycline (CHL), oxytetracycline (OXY), and tetracycline (TET). BNA was conducted using the R package\n                    <jats:italic>bnlearn<\/jats:italic>\n                    to identify joint resistance patterns and estimate conditional dependencies among resistance outcomes. Results revealed a high prevalence of MDR: 248 isolates (95.6%) were resistant to more than one AMD, and 209 isolates (80.7%) were resistant to at least one AMD in three or more classes. The Bayesian network comprised of 11 edges connecting 13 AMD nodes, highlighting statistical dependencies between AMDs resistances. PEN, TIA, and TIL were the most central nodes, with PEN connected to SUL, TIA, GEN, and CEF; TIA to PEN, SPC, TIL, and CLN; and TIL to SUL, TIA, CLN, and OXY. Other associations included CEF\u2013SPC, TET\u2013CLN, CEF\u2013ENR, and OXY\u2013CHL. These relationships implicate systematic dependencies between AMDs and may have resulted from mechanisms like cross-resistance and co-resistance. While these relationships are statistically derived and hypothesis-generating, they underscore the importance of understanding AMR patterns in guiding more effective AMD use. This approach can help prevent overuse, reduce treatment failures, and support AMR mitigation efforts for improved animal and public health outcomes.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014117","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:27:02Z","timestamp":1774546022000},"page":"e1014117","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian network models to assess antimicrobial resistance patterns of Streptococcus suis isolated from swine production systems in the United States between 2014\u20132021"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9470-2668","authenticated-orcid":true,"given":"Ruwini","family":"Rupasinghe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brittany L.","family":"Morgan 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