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Individuals with Down syndrome (DS) are more prone to develop severe respiratory tract infections. Although a RSV infection has a high clinical impact and severe outcome in individuals with DS, no vaccine nor effective therapeutics are available. Any research into infection pathophysiology or prophylactic and therapeutic antiviral strategies in the specific context of DS would greatly benefit this patient population, but currently such relevant animal models are lacking. This study aimed to develop and characterize the first mouse model of RSV infection in a DS-specific context. Ts65Dn mice and wild type littermates were inoculated with a bioluminescence imaging-enabled recombinant human RSV to longitudinally track viral replication in host cells throughout infection progression. This resulted in an active infection in the upper airways and lungs with similar viral load in Ts65Dn mice and euploid mice. Flow cytometric analysis of leukocytes in lungs and spleen demonstrated immune alterations with lower CD8+ T cells and B-cells in Ts65Dn mice. Overall, our study presents a novel DS-specific mouse model of hRSV infection and shows that potential in using the Ts65Dn preclinical model to study immune-specific responses of RSV in the context of DS and supports the need for models representing the pathological development.

Purpose: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans., Methods: We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians., Results: The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic., (© 2023. The Author(s).)

Purpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities., Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60)., Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items)., Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza., Competing Interests: Conflict of interest: A. Vaidyanathan, F. Zerka, F. Belmans, I. Van Peufflik and M. Occhipinti are salaried employees of Radiomics (Oncoradiomics SA). J. Guiot reports, within and outside the submitted work, research agreements from Radiomics (Oncoradiomics SA); he is in the permanent SAB of Radiomics (Oncoradiomics SA) for the SALMON trial without any specific consultancy fee for this work; he is co-inventor of one issued patent on radiomics licensed to Radiomics (Oncoradiomics SA); he confirms that none of these entities or funding was involved in the preparation of this work. P. Lambin reports, within and outside the submitted work, grants/sponsored research agreements from Radiomics (Oncoradiomics SA), ptTheragnostic/DNAmito, Health Innovation Ventures; he received an advisor/presenter fee and/or reimbursement of travel costs/consultancy fee and/or in-kind manpower contribution from Radiomics (Oncoradiomics SA), BHV, Merck, Varian, Elekta, ptTheragnostic, BMS and Convert Pharmaceuticals; he has minority shares in Radiomics (Oncoradiomics SA), Convert Pharmaceuticals, Comunicare Solutions and LivingMed Biotech; he is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248 and PCT/NL2014/050728) licenced to Radiomics (Oncoradiomics SA), one issued patent on mtDNA (PCT/EP2014/059089) licenced to ptTheragnostic/DNAmito, one unissued patent on LSRT (PCT/ P126537PC00) licenced to Varian Medical, three nonpatented inventions (software) licenced to ptTheragnostic/DNAmito, Radiomics (Oncoradiomics SA) and Health Innovation Ventures, and three unissued, unlicenced patents on deep and handcrafted radiomics (US P125078US00, PCT/NL/2020/050794 and N2028271); he confirms that none of these entities or funding was involved in the preparation of this paper. R.T.H. Leijenaar has shares in the company Radiomics (Oncoradiomics SA) and is co-inventor of an issued patent with royalties on radiomics (PCT/NL2014/050728) licenced Radiomics (Oncoradiomics SA). S. Walsh and W. Vos have shares in the Radiomics (Oncoradiomics SA). The rest of the co-authors have no known competing financial interests or personal relationships to declare., (Copyright ©The authors 2022.)