| 1. |
- Kübler, André, et al.
(författare)
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Mycobacterium tuberculosis dysregulates MMP/TIMP balance to drive rapid cavitation and unrestrained bacterial proliferation.
- 2015
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Ingår i: Journal of Pathology. - : Wiley. - 0022-3417 .- 1096-9896. ; 235:3, s. 431-444
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Tidskriftsartikel (refereegranskat)abstract
- Active tuberculosis (TB) often presents with advanced pulmonary disease, including irreversible lung damage and cavities. Cavitary pathology contributes to antibiotic failure, transmission, morbidity and mortality. Matrix metalloproteinases (MMPs), in particular MMP-1 are implicated in TB pathogenesis. We explored the mechanisms relating MMP/TIMP imbalance to cavity formation in a modified rabbit model of cavitary TB. Our model results in consistent progression of consolidation to human-like cavities (100% by day 28) with resultant bacillary burdens (>10(7) CFU/g) far greater than those found in matched granulomatous tissue (10(5) CFU/g). Using a novel, breath-hold computerized tomography scanning and image analysis protocol. We show that cavities develop rapidly from areas of densely consolidated tissue. Radiological change correlated with a decrease in functional lung tissue as estimated by changes in lung density during controlled pulmonary expansion (R(2) =0.6356, p < 0.0001). We demonstrated that the expression of interstitial collagenase (MMP-1) is specifically greater in cavitary compared to granulomatous lesions (p < 0.01), and that TIMP-3 significantly decreases at the cavity surface. Our findings demonstrate that an MMP-1/TIMP imbalance, is associated with the progression of consolidated regions to cavities containing very high bacterial burdens. Our model provided mechanistic insight, correlating with human disease at the pathological, microbiological and molecular levels,. It also provides a strategy to investigate therapeutics in the context of complex TB pathology. We used these findings to predict a MMP/TIMP balance in active TB; and confirmed this in human plasma, revealing the potential of MMP/TIMP levels as key components of a diagnostic matrix aimed at distinguishing active from latent TB (PPV=92.9%; 95%CI 66.1-99.8%, NPV=85.6%; 95%CI 77.0-91.9%).
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| 2. |
- Luna, Brian, et al.
(författare)
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In Vivo Prediction of Tuberculosis-Associated Cavity Formation in Rabbits
- 2015
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Ingår i: The Journal of infectious diseases. - : Oxford University Press (OUP). - 1537-6613 .- 0022-1899. ; 211:3, s. 481-485
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Tidskriftsartikel (refereegranskat)abstract
- The presence of cavitary lesions in patients with tuberculosis poses a significant clinical concern due to the risk of infectivity and the risk of antibiotic treatment failure. We describe 2 algorithms that use noninvasive positron emission tomography (PET) and computed tomography (CT) to predict the development of cavitary lesions in rabbits. Analysis of the PET region of interest predicted cavitary disease with 100% sensitivity and 76% specificity, and analysis of the CT region of interest predicted cavitary disease with 83.3% sensitivity and 76.9% specificity. Our results show that restricting our analysis to regions with high [(18)F]-fluorodeoxyglucose uptake provided the best combination of sensitivity and specificity.
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| 3. |
- Rauniyar, Ashish, et al.
(författare)
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Federated learning for medical applications : A taxonomy, current trends, challenges, and future research directions
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. Consequently, the realm of data-driven medical applications has garnered significant attention spanning academia and industry, ushering in marked enhancements in healthcare delivery quality. Despite these strides, the adoption of AI-driven medical applications remains hindered by formidable challenges, including the arduous task of meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. We delve into the contemporary research trends and their outcomes, unraveling the intricacies of designing reliable and scalable FL models. Our survey outlines the foundational statistical predicaments of FL, confronts device-related obstacles, delves into security challenges, and navigates the intricate terrain of privacy concerns, all while spotlighting its transformative potential within the medical domain. A primary focus of our study rests on medical applications, where we underscore the weighty burden of global cancer and illuminate the potency of FL in engendering computer-aided diagnosis tools that address this challenge with heightened efficacy. Further augmenting our discourse, recent literature has unveiled the inherent robustness and generalization of FL models compared to traditional data-driven medical applications. We hope that this review endeavors to serve as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.
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| 4. |
- Rauniyar, Ashish, et al.
(författare)
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Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions
- 2024
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:5, s. 7374-7398
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and Quality-of-Service (QoS) standards. Recent developments in federated learning (FL) have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this article, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unraveling the complexities of designing reliable and scalable FL models. This article outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state of the art and identifying open problems and future research directions.
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| 5. |
- Zhuang, Xiahai, et al.
(författare)
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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation : An open-access grand challenge.
- 2019
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
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