{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:51:23Z","timestamp":1786117883215,"version":"build-2736575974"},"reference-count":44,"publisher":"American Society for Microbiology","issue":"8","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Microbiol Spectr"],"published-print":{"date-parts":[[2026,8,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Carbapenem-resistant\n                      <jats:italic toggle=\"yes\">Acinetobacter baumannii<\/jats:italic>\n                      (CRAB) represents a major clinical threat, underscoring the urgent need for both new antibiotics and rapid antimicrobial susceptibility testing (AST) methods that can inform treatment. Here, we describe a susceptibility assessment that combines nanomotion technology with machine learning (ML) to rapidly classify responses to zosurabalpin (ZAB), the first-in-class antibiotic targeting the LptB\u2082FGC complex of the lipopolysaccharide transport pathway in\n                      <jats:italic toggle=\"yes\">A. baumannii<\/jats:italic>\n                      . Nanomotion recordings from five\n                      <jats:italic toggle=\"yes\">A. baumannii<\/jats:italic>\n                      strains spanning a wide range of minimum inhibitory concentrations (MICs) were measured at multiple ZAB concentrations. A logistic regression ML model was trained using a labeling strategy that categorized recordings as susceptible or non-susceptible based on strain MICs obtained by reference methods and antibiotic concentrations measured. This model, built on four quantile features, achieved &gt;90% accuracy compared with susceptibility responses. These results highlight four key advances: (i) nanomotion-based AST can deliver reliable phenotypic classification for new antibiotics before clinical breakpoints are available, (ii) accurate results can be obtained within only 2 h, (iii) the method extends nanomotion\u2019s applicability to high-priority pathogens, such as CRAB, and (iv) the nanomotion response can reflect early cellular changes that precede a measurable decline in viability, suggesting that susceptibility may be detected before killing becomes apparent by conventional methods. Together, this work establishes nanomotion-ML AST as a promising diagnostic platform to support the evaluation and future integration of novel drugs into clinical practice, advancing next-generation rapid AST workflows against critical multidrug-resistant pathogens.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>\n                      This study demonstrates a rapid, nanomotion-based approach with potential to inform antibiotic susceptibility assessment for\n                      <jats:italic toggle=\"yes\">Acinetobacter baumannii<\/jats:italic>\n                      infections. By combining nanomotion technology with quantitative machine-learning-assisted signal analysis, susceptibility to the novel antibiotic zosurabalpin, which is currently in clinical development, could be determined within 2 h. The assay reliably distinguished susceptible from non-susceptible phenotypes across strains with different MICs and in both standard and horse serum-supplemented media. Notably, the nanomotion signature reflected early cellular responses that preceded a measurable decline in viability by conventional methods. Together, these features provide rapid phenotypic insights that may support future development of timely, targeted therapy decisions. The findings highlight nanomotion-based antimicrobial susceptibility testing as a promising tool to accelerate diagnostic readiness for new antibiotics and improve treatment management in infections caused by multidrug-resistant\n                      <jats:italic toggle=\"yes\">A. baumannii<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/spectrum.04064-25","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T01:19:26Z","timestamp":1782868766000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Rapid delineation of\n                    <i>Acinetobacter baumannii<\/i>\n                    dose responses to zosurabalpin based on machine learning-assisted interpretation of bacterial nanomotions"],"prefix":"10.1128","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-5361","authenticated-orcid":false,"given":"Marta","family":"Pla Verge","sequence":"first","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9991-4912","authenticated-orcid":false,"given":"Jan","family":"Winnicki","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda","family":"Luraschi-Eggemann","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grzegorz","family":"Jozwiak","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"Fromm","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Munch","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gino","family":"Cathomen","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caspar","family":"Vogel","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Schwanke","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danuta","family":"Cichocka","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3818-0428","authenticated-orcid":true,"given":"Alexander","family":"Sturm","sequence":"additional","affiliation":[{"name":"Resistell AG","place":["Muttenz, Switzerland"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)02724-0"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(25)00501-8"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01990-1"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpath.2021.104766"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1128\/aac.01858-24"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-024-01054-w"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/antibiotics9030119"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpath.2025.107605"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1128\/cmr.00279-24"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1128\/mbio.02260-23"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.3390\/microorganisms13071501"},{"key":"e_1_3_4_13_2","volume-title":"WHO bacterial priority pathogens list, 2024: bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance","author":"World Health Organization","year":"2024","unstructured":"World Health Organization. 2024. 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