TY - JOUR AU - Denani, Caio AU - Horbach, Ingrid AU - Setatino, Bruno AU - Azevedo, Adriana AU - Lima, Sheila AU - Schwarcz, Waleska AU - Sousa, Ivanildo TI - Evolution of the PRNT: Merging tradition and innovation to set the gold standard in the era of automation JF - HUMAN VACCINES & IMMUNOTHERAPEUTICS J2 - HUM VACC IMMUNOTHER VL - 21 PY - 2025 IS - 1 SP - 2528368 SN - 2164-5515 DO - 10.1080/21645515.2025.2528368 UR - https://m2.mtmt.hu/api/publication/36331636 ID - 36331636 LA - English DB - MTMT ER - TY - JOUR AU - Xu, Cheng AU - Zhao, Ling-Yun AU - Ye, Cun-Si AU - Xu, Ke-Chen AU - Xu, Ke-Yang TI - The application of machine learning in clinical microbiology and infectious diseases JF - FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY J2 - FRONT CELL INFECT MI VL - 15 PY - 2025 SN - 2235-2988 DO - 10.3389/fcimb.2025.1545646 UR - https://m2.mtmt.hu/api/publication/36176641 ID - 36176641 AB - With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases. Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning’s current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning. LA - English DB - MTMT ER - TY - JOUR AU - Bogacheva, M.S. AU - Kuivanen, S. AU - Potdar, S. AU - Hassinen, A. AU - Huuskonen, S. AU - Pöhner, I. AU - Luck, T.J. AU - Turunen, L. AU - Feodoroff, M. AU - Szirovicza, L. AU - Savijoki, K. AU - Saarela, J. AU - Tammela, P. AU - Paavolainen, L. AU - Poso, A. AU - Varjosalo, M. AU - Kallioniemi, O. AU - Pietiäinen, V. AU - Vapalahti, O. TI - Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome JF - ANTIVIRAL RESEARCH J2 - ANTIVIR RES VL - 223 PY - 2024 SN - 0166-3542 DO - 10.1016/j.antiviral.2024.105813 UR - https://m2.mtmt.hu/api/publication/34716198 ID - 34716198 N1 - Department of Virology, Medicum, University of Helsinki, Helsinki, Finland Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Sciences (HiLIFE), University of Helsinki, Helsinki, Finland Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland Faculty of Health Sciences, School of Pharmacy, University of Eastern Finland, Kuopio, Finland Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland Department of Internal Medicine VIII, University Hospital Tubingen, Tubingen, Germany Science for Life Laboratory (SciLifeLab), Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden Department of Veterinary Biosciences, Faculty of Veterinary Medicine, University of Helsinki, Helsinki, Finland HUS Diagnostic Center, HUSLAB, Clinical Microbiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland Export Date: 1 March 2024 CODEN: ARSRD Correspondence Address: Bogacheva, M.S.P.O. Box 21 (Haartmaninkatu 3), Finland; email: mariia.bogacheva@helsinki.fi Funding details: Helsingin Yliopisto, HY Funding details: Minerva Foundation Funding details: Academy of Finland, AKA, FIRI 2020-337036 Funding details: Juho Vainion Säätiö Funding text 1: The authors thank Minerva Institute (Helsinki, Finland) for providing utilities for the project; the FIMM High Throughput Biomedicine Unit for providing access to HT robotics, the FIMM High Content Imaging and Analysis Unit for HC imaging and analysis, and FIMM Genomics unit for genotyping services (HiLIFE, University of Helsinki and Biocenter Finland). The authors acknowledge the CSC-IT Center for Science Ltd. for computational resources. The grants awarded by the Academy of Finland [grant numbers iCOIN-336496: OK, VP, OV; FIRI 2020-337036: FIMM-HCA, AH, VP], the EU H2020 VEO project (OV), Juho Vainio foundation, anonymous donors through Helsinki University Research funds (OV) and Minerva Foundation for the COVID19 research project grant (VP) are also greatly acknowledged. The authors thank Katja Rosti, University of Helsinki, Dr. Petri Saviranta, and Sirpa Jylhä, VTT, Finland for the supply of 7A12 antibodies against SARS-CoV-2 S protein. The authors thank Prof. Kari Alitalo for providing a fluorescence reader at BSL-3 and Alyce Whipp for checking the English language grammar in the manuscript. Funding text 2: The authors thank Minerva Institute (Helsinki, Finland) for providing utilities for the project; the FIMM High Throughput Biomedicine Unit for providing access to HT robotics, the FIMM High Content Imaging and Analysis Unit for HC imaging and analysis, and FIMM Genomics unit for genotyping services (HiLIFE, University of Helsinki and Biocenter Finland). The authors acknowledge the CSC-IT Center for Science Ltd. for computational resources. The grants awarded by the Academy of Finland [grant numbers iCOIN-336496 : OK, VP, OV; FIRI 2020-337036 : FIMM-HCA, AH, VP], the EU H2020 VEO project (OV), Juho Vainio foundation , anonymous donors through Helsinki University Research funds (OV) and Minerva Foundation for the COVID19 research project grant (VP) are also greatly acknowledged. The authors thank Katja Rosti, University of Helsinki, Dr. Petri Saviranta, and Sirpa Jylhä, VTT, Finland for the supply of 7A12 antibodies against SARS-CoV-2 S protein. The authors thank Prof. Kari Alitalo for providing a fluorescence reader at BSL-3 and Alyce Whipp for checking the English language grammar in the manuscript. AB - The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2–induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus–host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses’ responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2. © 2024 The Authors LA - English DB - MTMT ER - TY - JOUR AU - Lintala, Annika AU - Vapalahti, Olli AU - Nousiainen, Arttu AU - Kantele, Anu AU - Hepojoki, Jussi TI - Whole Blood as a Sample Matrix in Homogeneous Time-Resolved Assay-Förster Resonance Energy Transfer-Based Antibody Detection JF - DIAGNOSTICS J2 - DIAGNOSTICS VL - 14 PY - 2024 IS - 7 PG - 11 SN - 2075-4418 DO - 10.3390/diagnostics14070720 UR - https://m2.mtmt.hu/api/publication/35021738 ID - 35021738 N1 - Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Medicum, 00290, Finland Department of Veterinary Biosciences, Faculty of Veterinary Medicine, University of Helsinki, Helsinki, 00014, Finland Helsinki University Hospital Diagnostic Center, Helsinki, 00029, Finland Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland Meilahti Infectious Diseases and Vaccine Research Center, MeiVac, Department of Infectious Diseases, University of Helsinki and Helsinki University Hospital, Helsinki, 00029, Finland Vetsuisse Faculty, Institute of Veterinary Pathology, University of Zürich, Zürich, 8057, Switzerland Export Date: 18 October 2024 Correspondence Address: Hepojoki, J.; Department of Virology, Helsinki, Finland; email: jussi.hepojoki@helsinki.fi LA - English DB - MTMT ER - TY - JOUR AU - Sartingen, N. AU - Stürmer, V. AU - Kaltenböck, M. AU - Müller, T.G. AU - Schnitzler, P. AU - Kreshuk, A. AU - Kräusslich, H.-G. AU - Merle, U. AU - Mücksch, F. AU - Müller, B. AU - Pape, C. AU - Laketa, V. TI - Multiplex Microscopy Assay for Assessment of Therapeutic and Serum Antibodies against Emerging Pathogens JF - VIRUSES J2 - VIRUSES-BASEL VL - 16 PY - 2024 IS - 9 SN - 1999-4915 DO - 10.3390/v16091473 UR - https://m2.mtmt.hu/api/publication/35469968 ID - 35469968 N1 - Virology, Department of Infectious Diseases, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, 69120, Germany European Molecular Biology Laboratory, Heidelberg, 69117, Germany German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, 69120, Germany Department of Internal Medicine IV, University Hospital Heidelberg, Heidelberg, 69120, Germany Institute of Computer Science, Göttingen University, Göttingen, 37073, Germany Cluster of Excellence ‘Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells’ (MBExC), Göttingen University, Göttingen, 37073, Germany Export Date: 18 October 2024 Correspondence Address: Laketa, V.; Virology, Germany; email: vibor.laketa@med.uni-heidelberg.de AB - The emergence of novel pathogens, exemplified recently by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the need for rapidly deployable and adaptable diagnostic assays to assess their impact on human health and guide public health responses in future pandemics. In this study, we developed an automated multiplex microscopy assay coupled with machine learning-based analysis for antibody detection. To achieve multiplexing and simultaneous detection of multiple viral antigens, we devised a barcoding strategy utilizing a panel of HeLa-based cell lines. Each cell line expressed a distinct viral antigen, along with a fluorescent protein exhibiting a unique subcellular localization pattern for cell classification. Our robust, cell segmentation and classification algorithm, combined with automated image acquisition, ensured compatibility with a high-throughput approach. As a proof of concept, we successfully applied this approach for quantitation of immunoreactivity against different variants of SARS-CoV-2 spike and nucleocapsid proteins in sera of patients or vaccinees, as well as for the study of selective reactivity of monoclonal antibodies. Importantly, our system can be rapidly adapted to accommodate other SARS-CoV-2 variants as well as any antigen of a newly emerging pathogen, thereby representing an important resource in the context of pandemic preparedness. © 2024 by the authors. LA - English DB - MTMT ER -