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Marini-Rapoport O, Fernández-Quintero ML, Keswani T, Zong G, Shim J, Pedersen LC, Mueller GA, Patil SU. Defining the cross-reactivity between peanut allergens Ara h 2 and Ara h 6 using monoclonal antibodies. Clin Exp Immunol 2024; 216:25-35. [PMID: 38346116 PMCID: PMC10929694 DOI: 10.1093/cei/uxae005] [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: 10/13/2023] [Revised: 12/08/2023] [Accepted: 02/09/2024] [Indexed: 03/13/2024] Open
Abstract
In peanut allergy, Arachis hypogaea 2 (Ara h 2) and Arachis hypogaea 6 (Ara h 6) are two clinically relevant peanut allergens with known structural and sequence homology and demonstrated cross-reactivity. We have previously utilized X-ray crystallography and epitope binning to define the epitopes on Ara h 2. We aimed to quantitatively characterize the cross-reactivity between Ara h 2 and Ara h 6 on a molecular level using human monoclonal antibodies (mAbs) and structural characterization of allergenic epitopes. We utilized mAbs cloned from Ara h 2 positive single B cells isolated from peanut-allergic, oral immunotherapy-treated patients to quantitatively analyze cross-reactivity between recombinant Ara h 2 (rAra h 2) and Ara h 6 (rAra h 6) proteins using biolayer interferometry and indirect inhibitory ELISA. Molecular dynamics simulations assessed time-dependent motions and interactions in the antibody-antigen complexes. Three epitopes-conformational epitopes 1.1 and 3, and the sequential epitope KRELRNL/KRELMNL-are conserved between Ara h 2 and Ara h 6, while two more conformational and three sequential epitopes are not. Overall, mAb affinity was significantly lower to rAra h 6 than it was to rAra h 2. This difference in affinity was primarily due to increased dissociation of the antibodies from rAra h 6, a phenomenon explained by the higher conformational flexibility of the Ara h 6-antibody complexes in comparison to Ara h 2-antibody complexes. Our results further elucidate the cross-reactivity of peanut 2S albumins on a molecular level and support the clinical immunodominance of Ara h 2.
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Affiliation(s)
- Orlee Marini-Rapoport
- Harvard University, Cambridge, MA, USA
- Food Allergy Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
| | | | - Tarun Keswani
- Food Allergy Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - Guangning Zong
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Jane Shim
- Food Allergy Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - Lars C Pedersen
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Geoffrey A Mueller
- National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Sarita U Patil
- Food Allergy Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
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2
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Macpherson MS, Hutchinson CE, Horst C, Goh V, Montana G. Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications. Radiol Artif Intell 2023; 5:e230019. [PMID: 38074779 PMCID: PMC10698609 DOI: 10.1148/ryai.230019] [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: 01/25/2023] [Revised: 08/24/2023] [Accepted: 09/07/2023] [Indexed: 01/31/2024]
Abstract
Purpose To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets. Materials and Methods This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction. Results Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error. Conclusion The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.
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Affiliation(s)
- Matthew S. Macpherson
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Charles E. Hutchinson
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Carolyn Horst
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Vicky Goh
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Giovanni Montana
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
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3
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Selby IA, Roberts M, Breger A, Rudd JHF, Weir-McCall JR. Shortcut Learning: Reduced But Not Resolved. Radiology 2023; 308:e230379. [PMID: 37552069 PMCID: PMC10477499 DOI: 10.1148/radiol.230379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Affiliation(s)
- Ian A. Selby
- *Department of Radiology, University of Cambridge School of Clinical
Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ,
United Kingdom
- Department of Radiology, Cambridge University Hospitals NHS
Foundation Trust, Cambridge, United Kingdom
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University
of Cambridge, Centre for Mathematical Sciences, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge School of Clinical
Medicine, Cambridge, United Kingdom
| | - Anna Breger
- Department of Applied Mathematics and Theoretical Physics, University
of Cambridge, Centre for Mathematical Sciences, Cambridge, United Kingdom
- Center of Medical Physics and Biomedical Engineering, Medical
University of Vienna, Vienna, Austria
| | - James H. F. Rudd
- Department of Cardiology, Cambridge University Hospitals NHS
Foundation Trust, Cambridge, United Kingdom
| | - Jonathan R. Weir-McCall
- *Department of Radiology, University of Cambridge School of Clinical
Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ,
United Kingdom
- Department of Radiology, Cambridge University Hospitals NHS
Foundation Trust, Cambridge, United Kingdom
- **Department of Radiology, Royal Papworth Hospital NHS Foundation
Trust, Cambridge, United Kingdom
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Massussi M, Metra M, Adamo M. Revolutionizing healthcare: artificial intelligence detection of coronary artery disease paves the way for future tools. J Cardiovasc Med (Hagerstown) 2023; 24:467-468. [PMID: 37285277 DOI: 10.2459/jcm.0000000000001493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Affiliation(s)
- Mauro Massussi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia
- Cardiac Catheterization Laboratory and Cardiology, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Marco Metra
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia
| | - Marianna Adamo
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia
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Chen CC, Huang JF, Lin WC, Cheng CT, Chen SC, Fu CY, Lee MS, Liao CH, Chung CY. The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data. Bioengineering (Basel) 2023; 10:bioengineering10040458. [PMID: 37106645 PMCID: PMC10136253 DOI: 10.3390/bioengineering10040458] [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: 02/07/2023] [Revised: 03/15/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost.
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Affiliation(s)
- Chih-Chi Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Jen-Fu Huang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Wei-Cheng Lin
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
- Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Shann-Ching Chen
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Chih-Yuan Fu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Mel S Lee
- Department of Orthopaedic Surgery, Pao-Chien Hospital, Pingtung 90078, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
| | - Chia-Ying Chung
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan
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6
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Künstliche Intelligenz zur Triage bei akutem Brustschmerz. Dtsch Med Wochenschr 2023. [DOI: 10.1055/a-1985-3263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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Goo JM. Triaging: Another Vital Application of the Deep Learning Technique on Chest Radiographs at the Emergency Department. Radiology 2023; 306:e223112. [PMID: 36648351 DOI: 10.1148/radiol.223112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Jin Mo Goo
- From the Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul 110-744, Korea; and Cancer Research Institute, Seoul National University, Seoul, Korea
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