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Article: COVID-19 Infection in Children: Diagnosis and Management.

Zhu, Frank / Ang, Jocelyn Y

2022 Volume 24, Issue 4, Page(s) 51–62

Abstract: Purpose of review: Due to the rapidly changing landscape of COVID-19, the purpose of this review is to provide a concise and updated summary of pediatric COVID-19 diagnosis and management.: Recent findings: The relative proportion of pediatric cases ... ...

Abstract	Purpose of review: Due to the rapidly changing landscape of COVID-19, the purpose of this review is to provide a concise and updated summary of pediatric COVID-19 diagnosis and management. Recent findings: The relative proportion of pediatric cases have significantly increased following the emergence of the Omicron variant (from < 2% in the early pandemic to 25% from 1/27 to 2/3/22). While children present with milder symptoms than adults, severe disease can still occur, particularly in children with comorbidities. There is a relative paucity of pediatric data in the management of COVID-19 and the majority of recommendations remain based on adult data. Summary: Fever and cough remain the most common clinical presentations, although atypical presentations such as "COVID toes," anosmia, and croup may be present. Children are at risk for post-infectious complications such as MIS-C and long COVID. Nucleic acid amplification tests through respiratory PCR remain the mainstay of diagnosis. The mainstay of management remains supportive care and prevention through vaccination is highly recommended. In patients at increased risk of progression, interventions such as monoclonal antibody therapy, PO Paxlovid, or IV remdesivir × 3 days should be considered. In patients with severe disease, the use of remdesivir, dexamethasone, and immunomodulatory agents (tocilizumab, baricitinib) is recommended. Children can be at risk for thrombosis from COVID-19 and anticoagulation is recommended in children with markedly elevated D-dimer levels or superimposed clinical risk factors for hospital associated venous thromboembolism.
Language	English
Publishing date	2022-04-11
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2019948-X
ISSN	1534-3146 ; 1523-3847
ISSN (online)	1534-3146
ISSN	1523-3847
DOI	10.1007/s11908-022-00779-0
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Article: 2021 Update on the Clinical Management and Diagnosis of Kawasaki Disease.

Zhu, Frank / Ang, Jocelyn Y

Current infectious disease reports

2021 Volume 23, Issue 3, Page(s) 3

Abstract: Purpose of review: Provide an updated review of the clinical management and diagnosis of Kawasaki disease with inclusion of potential diagnostic difficulties with multisystem inflammatory syndrome in children (MIS-C) given the ongoing COVID-19 pandemic.! ...

Abstract	Purpose of review: Provide an updated review of the clinical management and diagnosis of Kawasaki disease with inclusion of potential diagnostic difficulties with multisystem inflammatory syndrome in children (MIS-C) given the ongoing COVID-19 pandemic. Recent findings: Adjunctive corticosteroid therapy has been shown to reduce the rate of coronary artery dilation in children at high risk for IVIG resistance in multiple Japanese clinical studies (most notably RAISE study group). Additional adjunctive therapies (etanercept, infliximab, cyclosporin) may also provide limited benefit, but data is limited to single studies and subgroups of patients with cardiac abnormalities. The efficacy of other agents (atorvastatin, doxycycline) is currently being investigated. MIS-C is a clinically distinct entity from KD with broad clinical manifestations and multiorgan involvement (cardiac, GI, hematologic, dermatologic, respiratory, renal). MIS-C with Kawasaki manifestations is more commonly seen in children < 5 years of age. Summary: The 2017 American Heart Association (AHA) treatment guidelines have included changes in aspirin dosing (including both 80-100 mg/kg/day and 30-50 mg/kg/day treatment options), consideration of the use of adjuvant corticosteroid therapy in patients at high risk of IVIG resistance, and the change in steroid regimen for refractory KD to include both pulse-dose IVMP and longer course of prednisolone with an oral taper. A significant proportion of children diagnosed with MIS-C, a post-infectious syndrome of SARS-CoV-2 infection, meet criteria for Kawasaki disease. Further investigation is warranted to further delineate these conditions and optimize treatment of these conditions given the ongoing COVID-19 pandemic.
Language	English
Publishing date	2021-02-06
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2019948-X
ISSN	1534-3146 ; 1523-3847
ISSN (online)	1534-3146
ISSN	1523-3847
DOI	10.1007/s11908-021-00746-1
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Article ; Online: Long-short-term memory machine learning of longitudinal clinical data accurately predicts acute kidney injury onset in COVID-19: a two-center study.

Lu, Justin Y / Zhu, Joanna / Zhu, Jocelyn / Duong, Tim Q

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

2022 Volume 122, Page(s) 802–810

Abstract: Objectives: This study used the long-short-term memory (LSTM) artificial intelligence method to model multiple time points of clinical laboratory data, along with demographics and comorbidities, to predict hospital-acquired acute kidney injury (AKI) ... ...

Abstract	Objectives: This study used the long-short-term memory (LSTM) artificial intelligence method to model multiple time points of clinical laboratory data, along with demographics and comorbidities, to predict hospital-acquired acute kidney injury (AKI) onset in patients with COVID-19. Methods: Montefiore Health System data consisted of 1982 AKI and 2857 non-AKI (NAKI) hospitalized patients with COVID-19, and Stony Brook Hospital validation data consisted of 308 AKI and 721 NAKI hospitalized patients with COVID-19. Demographic, comorbidities, and longitudinal (3 days before AKI onset) laboratory tests were analyzed. LSTM was used to predict AKI with fivefold cross-validation (80%/20% for training/validation). Results: The top predictors of AKI onset were glomerular filtration rate, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein. Longitudinal data yielded marked improvement in prediction accuracy over individual time points. The inclusion of comorbidities and demographics further improves prediction accuracy. The best model yielded an area under the curve, accuracy, sensitivity, and specificity to be 0.965 ± 0.003, 89.57 ± 1.64%, 0.95 ± 0.03, and 0.84 ± 0.05, respectively, for the Montefiore validation dataset, and 0.86 ± 0.01, 83.66 ± 2.53%, 0.66 ± 0.10, 0.89 ± 0.03, respectively, for the Stony Brook Hospital validation dataset. Conclusion: LSTM model of longitudinal clinical data accurately predicted AKI onset in patients with COVID-19. This approach could help heighten awareness of AKI complications and identify patients for early interventions to prevent long-term renal complications.
MeSH term(s)	Acute Kidney Injury/diagnosis ; Acute Kidney Injury/etiology ; Artificial Intelligence ; COVID-19/diagnosis ; Humans ; Machine Learning ; Memory, Short-Term ; Prognosis ; Retrospective Studies ; Risk Factors
Language	English
Publishing date	2022-07-22
Publishing country	Canada
Document type	Journal Article
ZDB-ID	1331197-9
ISSN	1878-3511 ; 1201-9712
ISSN (online)	1878-3511
ISSN	1201-9712
DOI	10.1016/j.ijid.2022.07.034
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Article ; Online: A Triple-Double Convolutional Neural Network for Panchromatic Sharpening.

Zhang, Tian-Jiang / Deng, Liang-Jian / Huang, Ting-Zhu / Chanussot, Jocelyn / Vivone, Gemine

IEEE transactions on neural networks and learning systems

2023 Volume 34, Issue 11, Page(s) 9088–9101

Abstract: Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel ... ...

Abstract	Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.
Language	English
Publishing date	2023-10-27
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3155655
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Article ; Online: LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening.

Wu, Zhong-Cheng / Huang, Ting-Zhu / Deng, Liang-Jian / Huang, Jie / Chanussot, Jocelyn / Vivone, Gemine

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2023 Volume PP

Abstract: Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for ... ...

Abstract	Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap. Different from previous variational methods, we first formulate a pioneering image super-resolution (ISR) degradation model, which equivalently removes the downsampling operator and transforms the tensor completion framework. Under such a framework, the original pansharpening problem is realized by the LRTC-based technique with some deblurring regularizers. From the perspective of regularizer, we further explore a local-similarity-based dynamic detail mapping (DDM) term to more accurately capture the spatial content of the panchromatic image. Moreover, the low-tubal-rank property of multispectral images is investigated, and the low-tubal-rank prior is introduced for better completion and global characterization. To solve the proposed LRTCFPan model, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Comprehensive experiments at reduced-resolution (i.e., simulated) and full-resolution (i.e., real) data exhibit that the LRTCFPan method significantly outperforms other state-of-the-art pansharpening methods. The code is publicly available at: https://github.com/zhongchengwu/code_LRTCFPan.
Language	English
Publishing date	2023-02-28
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2023.3247165
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Article ; Online: Extracellular vesicles in venous thromboembolism and pulmonary hypertension.

Zhang, Jiwei / Hu, Xiaoyi / Wang, Tao / Xiao, Rui / Zhu, Liping / Ruiz, Matthieu / Dupuis, Jocelyn / Hu, Qinghua

Journal of nanobiotechnology

2023 Volume 21, Issue 1, Page(s) 461

Abstract: Venous thromboembolism (VTE) is a multifactorial disease, and pulmonary hypertension (PH) is a serious condition characterized by pulmonary vascular remodeling leading with increased pulmonary vascular resistance, ultimately leading to right heart ... ...

Abstract	Venous thromboembolism (VTE) is a multifactorial disease, and pulmonary hypertension (PH) is a serious condition characterized by pulmonary vascular remodeling leading with increased pulmonary vascular resistance, ultimately leading to right heart failure and death. Although VTE and PH have distinct primary etiologies, they share some pathophysiologic similarities such as dysfunctional vasculature and thrombosis. In both conditions there is solid evidence that EVs derived from a variety of cell types including platelets, monocytes, endothelial cells and smooth muscle cells contribute to vascular endothelial dysfunction, inflammation, thrombosis, cellular activation and communications. However, the roles and importance of EVs substantially differ between studies depending on experimental conditions and parent cell origins of EVs that modify the nature of their cargo. Numerous studies have confirmed that EVs contribute to the pathophysiology of VTE and PH and increased levels of various EVs in relation with the severity of VTE and PH, confirming its potential pathophysiological role and its utility as a biomarker of disease severity and as potential therapeutic targets.
MeSH term(s)	Humans ; Hypertension, Pulmonary/metabolism ; Hypertension, Pulmonary/therapy ; Venous Thromboembolism/metabolism ; Endothelial Cells/metabolism ; Extracellular Vesicles/metabolism ; Thrombosis
Language	English
Publishing date	2023-11-30
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2100022-0
ISSN	1477-3155 ; 1477-3155
ISSN (online)	1477-3155
ISSN	1477-3155
DOI	10.1186/s12951-023-02216-3
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Article ; Online: Reduced Peak Bone Mass in Young Adults With Low Motor Competence.

Tan, Jocelyn / Ng, Carrie-Anne / Hart, Nicolas H / Rantalainen, Timo / Sim, Marc / Scott, David / Zhu, Kun / Hands, Beth / Chivers, Paola

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research

2023 Volume 38, Issue 5, Page(s) 665–677

Abstract: Although suboptimal bone health has been reported in children and adolescents with low motor competence (LMC), it is not known whether such deficits are present at the time of peak bone mass. We examined the impact of LMC on bone mineral density (BMD) in ...

Abstract	Although suboptimal bone health has been reported in children and adolescents with low motor competence (LMC), it is not known whether such deficits are present at the time of peak bone mass. We examined the impact of LMC on bone mineral density (BMD) in 1043 participants (484 females) from the Raine Cohort Study. Participants had motor competence assessed using the McCarron Assessment of Neuromuscular Development at 10, 14, and 17 years, and a whole-body dual-energy X-ray absorptiometry (DXA) scan at 20 years. Bone loading from physical activity was estimated from the International Physical Activity Questionnaire at the age of 17 years. The association between LMC and BMD was determined using general linear models that controlled for sex, age, body mass index, vitamin D status, and prior bone loading. Results indicated LMC status (present in 29.6% males and 21.9% females) was associated with a 1.8% to 2.6% decrease in BMD at all load-bearing bone sites. Assessment by sex showed that the association was mainly in males. Osteogenic potential of physical activity was associated with increased BMD dependent on sex and LMC status, with males with LMC showing a reduced effect from increasing bone loading. As such, although engagement in osteogenic physical activity is associated with BMD, other factors involved in physical activity, eg, diversity, movement quality, may also contribute to BMD differences based upon LMC status. The finding of lower peak bone mass for individuals with LMC may reflect a higher risk of osteoporosis, especially for males; however, further research is required. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
MeSH term(s)	Male ; Child ; Adolescent ; Female ; Humans ; Young Adult ; Bone Density ; Cohort Studies ; Osteoporosis/diagnostic imaging ; Absorptiometry, Photon ; Bone and Bones/diagnostic imaging
Language	English
Publishing date	2023-03-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632783-7
ISSN	1523-4681 ; 0884-0431
ISSN (online)	1523-4681
ISSN	0884-0431
DOI	10.1002/jbmr.4788
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Article ; Online: Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks.

Hu, Jin-Fan / Huang, Ting-Zhu / Deng, Liang-Jian / Jiang, Tai-Xiang / Vivone, Gemine / Chanussot, Jocelyn

IEEE transactions on neural networks and learning systems

2022 Volume 33, Issue 12, Page(s) 7251–7265

Abstract: Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, ... ...

Abstract	Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI's scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html.
Language	English
Publishing date	2022-11-30
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2021.3084682
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Article: Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model.

Hong, Danfeng / Hu, Jingliang / Yao, Jing / Chanussot, Jocelyn / Zhu, Xiao Xiang

ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)

2021 Volume 178, Page(s) 68–80

Abstract: As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap ... ...

Abstract	As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively, particularly for heterogeneous data sources. Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e.,
Language	English
Publishing date	2021-04-30
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2021.05.011
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Article ; Online: Joint and Progressive Subspace Analysis (JPSA) With Spatial-Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction.

Hong, Danfeng / Yokoya, Naoto / Chanussot, Jocelyn / Xu, Jian / Zhu, Xiao Xiang

IEEE transactions on cybernetics

2021 Volume 51, Issue 7, Page(s) 3602–3615

Abstract: Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost effectiveness (linearization), generalization capability (out-of-sample), and representability ( ... ...

Abstract	Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination). To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semisupervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA). The JPSA learns a high-level, semantically meaningful, joint spatial-spectral feature representation from hyperspectral (HS) data by: 1) jointly learning latent subspaces and a linear classifier to find an effective projection direction favorable for classification; 2) progressively searching several intermediate states of subspaces to approach an optimal mapping from the original space to a potential more discriminative subspace; and 3) spatially and spectrally aligning a manifold structure in each learned latent subspace in order to preserve the same or similar topological property between the compressed data and the original data. A simple but effective classifier, that is, nearest neighbor (NN), is explored as a potential application for validating the algorithm performance of different HDR approaches. Extensive experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely used HS datasets: 1) Indian Pines (92.98%) and 2) the University of Houston (86.09%) in comparison with previous state-of-the-art HDR methods. The demo of this basic work (i.e., ECCV2018) is openly available at https://github.com/danfenghong/ECCV2018_J-Play.
Language	English
Publishing date	2021-06-23
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2020.3028931
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