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Chitre A, Querimit RCM, Rihm SD, Karan D, Zhu B, Wang K, Wang L, Hippalgaonkar K, Lapkin AA. Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset. Sci Data 2024; 11:728. [PMID: 38961122 PMCID: PMC11222379 DOI: 10.1038/s41597-024-03573-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 06/25/2024] [Indexed: 07/05/2024] Open
Abstract
Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.
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Affiliation(s)
- Aniket Chitre
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore
| | - Robert C M Querimit
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, 637459, Singapore
| | - Simon D Rihm
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore
| | - Benchuan Zhu
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Ke Wang
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Long Wang
- BASF Advanced Chemicals Co. Ltd., No. 300, Jiang Xin Sha Road, Pudong, Shanghai, 200137, China
| | - Kedar Hippalgaonkar
- Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138634, Singapore.
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, Singapore, 138602, Singapore.
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Saraiva MM, Spindler L, Manzione T, Ribeiro T, Fathallah N, Martins M, Cardoso P, Mendes F, Fernandes J, Ferreira J, Macedo G, Nadal S, de Parades V. Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study. Cancers (Basel) 2024; 16:1909. [PMID: 38791987 PMCID: PMC11119426 DOI: 10.3390/cancers16101909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
| | - Thiago Manzione
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Joana Fernandes
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- DigestAID—Artificial Intelligence Development, Rua Alfredo Allen, 4200-135 Porto, Portugal
| | - João Ferreira
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- DigestAID—Artificial Intelligence Development, Rua Alfredo Allen, 4200-135 Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Sidney Nadal
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil
| | - Vincent de Parades
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
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Quistberg DA. Potential of artificial intelligence in injury prevention research and practice. Inj Prev 2024; 30:89-91. [PMID: 38307714 PMCID: PMC11003389 DOI: 10.1136/ip-2023-045203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024]
Abstract
There is increasing interest and use of artificial Intelligence algorithms and methods in biomedical research and practice, particularly as the technology has made significant advances in the past decade and become more accessible to more disciplines. This editorial briefly reviews this technology and its potential for injury prevention research and practice, proposing ways that it can be used to advance the discipline, as well as the potential pitfalls, concerns and biases that accompany it.
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Affiliation(s)
- D Alex Quistberg
- Urban Health Collaborative, Drexel University, Philadelphia, Pennsylvania, USA
- Environmental & Occupational Health, Drexel University, Philadelphia, Pennsylvania, USA
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Jayakumar P, Oude Nijhuis KD, Oosterhoff JHF, Bozic KJ. Value-based Healthcare: Can Generative Artificial Intelligence and Large Language Models be a Catalyst for Value-based Healthcare? Clin Orthop Relat Res 2023; 481:1890-1894. [PMID: 37678399 PMCID: PMC10499068 DOI: 10.1097/corr.0000000000002854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 08/08/2023] [Indexed: 09/09/2023]
Affiliation(s)
- Prakash Jayakumar
- Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, TX, USA
| | - Koen D. Oude Nijhuis
- Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, TX, USA
- Department of Orthopedic Surgery, The University of Groningen, Groningen, the Netherlands
| | - Jacobien H. F. Oosterhoff
- Department of Engineering Systems and Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, the Netherlands
| | - Kevin J. Bozic
- Department of Surgery and Perioperative Care, Dell Medical School at the University of Texas at Austin, Austin, TX, USA
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