A Multi-view Impartial Decision Network for Frontotemporal Dementia Diagnosis

Frontotemporal Dementia (FTD) diagnosis has been successfully progress using deep learning techniques. However, current FTD identification methods suffer from two limitations. Firstly, they do not exploit the potential of multi-view functional magnetic resonance imaging (fMRI) for classifying FTD. Secondly, they do not consider the reliability of the multi-view FTD diagnosis. To address these limitations, we propose a reliable multi-view impartial decision network (MID-Net) for FTD diagnosis in fMRI. Our MID-Net provides confidence for each view and generates a reliable prediction without any conflict. To achieve this, we employ multiple expert models to extract evidence from the abundant neural network information contained in fMRI images. We then introduce the Dirichlet Distribution to characterize the expert class probability distribution from an evidence level. Additionally, a novel Impartial Decision Maker (IDer) is proposed to combine the different opinions inductively to arrive at an unbiased prediction without additional computation cost. Overall, our MID-Net dynamically integrates the decisions of different experts on FTD disease, especially when dealing with multi-view high-conflict cases. Extensive experiments on a high-quality FTD fMRI dataset demonstrate that our model outperforms previous methods and provides high uncertainty for hard-to-classify examples. We believe that our approach represents a significant step toward the deployment of reliable FTD decision-making under multi-expert conditions. We will release the codes for reproduction after acceptance

Lentivirus-mediated RNA interference of vascular endothelial growth factor in monkey eyes with iris neovascularization

Purpose: To explore the in vivo anti-angiogenesis effects resulting from lentivirus-mediated RNAi of vascular endothelial growth factor (VEGF) in monkeys with iris neovascularization (INV). Methods: Five specific recombinant lentiviral vectors for RNA interference, targeting Macaca mulatta VEGFA, were designed and the one with best knock down efficacy (LV-GFP-VEGFi1) in H1299 cells and RF/6A cells was selected by real-time PCR for in vivo use. A laser-induced retinal vein occlusion model was established in one eye of seven cynomolgus monkeys. In monkeys number1, 3, and 5 (Group 1), the virus (1x10(8) particles) was intravitreally injected into the preretinal space of the animal's eye immediately after laser coagulation; and in monkeys number 2, 4, and 6 (Group 2), the virus (1x10(8) particles) was injected at 10 days after laser coagulation. In monkey number 7, a blank control injection was performed. In monkeys number 1 and 2, virus without RNAi sequence was used; in monkeys number 3 and 4, virus with nonspecific RNAi sequence was used; and in monkeys 5 and 6, LV-GFP-VEGFi1 was used. Results: In monkey number 5, at 23 days after laser treatment, no obvious INV was observed, while fluorescein angiography of the iris revealed high fluorescence at the margin of pupil and point posterior synechiae. At 50 days after laser treatment, only a slight ectropion uvea was found. However, in the other eyes, obvious INV or hyphema was observed. The densities of new iridic vessels all significantly varied: between monkey number 5 and number 3 (36.01 +/- 4.49/mm(2) versus 48.68 +/- 9.30/mm(2), p=0.025), between monkey number 3 and monkey number 7 (48.68 +/- 9.30/mm(2) versus 74.38 +/- 9.23/mm(2), p=0.002), and between monkey number 5 and number 7 (36.01 +/- 4.49/mm(2) versus 74.38 +/- 9.23/mm(2), p<0.001). Conclusions: Lentivirus-mediated RNAi of VEGF may be a new strategy to treat iris neovascularization, while further studies are needed to investigate the long-term effect.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000281341400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemistry & Molecular BiologyOphthalmologySCI(E)PubMed7ARTICLE187-891743-17531

Dexmedetomidine post-treatment attenuates cardiac ischaemia/reperfusion injury by inhibiting apoptosis through HIF-1α signalling.

Hypoxia-inducible factor 1α (HIF-1α) plays a critical role in the apoptotic process during cardiac ischaemia/reperfusion (I/R) injury. This study aimed to investigate whether post-treatment with dexmedetomidine (DEX) could protect against I/R-induced cardiac apoptosis in vivo and in vitro via regulating HIF-1α signalling pathway. Rat myocardial I/R was induced by occluding the left anterior descending artery for 30 minutes followed by 6-hours reperfusion, and cardiomyocyte hypoxia/reoxygenation (H/R) was induced by oxygen-glucose deprivation for 6 hours followed by 3-hours reoxygenation. Dexmedetomidine administration at the beginning of reperfusion or reoxygenation attenuated I/R-induced myocardial injury or H/R-induced cell death, alleviated mitochondrial dysfunction, reduced the number of apoptotic cardiomyocytes, inhibited the activation of HIF-1α and modulated the expressions of apoptosis-related proteins including BCL-2, BAX, BNIP3, cleaved caspase-3 and cleaved PARP. Conversely, the HIF-1α prolyl hydroxylase-2 inhibitor IOX2 partly blocked DEX-mediated cardioprotection both in vivo and in vitro. Mechanistically, DEX down-regulated HIF-1α expression at the post-transcriptional level and inhibited the transcriptional activation of the target gene BNIP3. Post-treatment with DEX protects against cardiac I/R injury in vivo and H/R injury in vitro. These effects are, at least in part, mediated via the inhibition of cell apoptosis by targeting HIF-1α signalling

SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image

Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented lesions or tissues. We estimate the distribution of SAM predictions via Monte Carlo with prior distribution parameters, which employs different prompts as formulation of test-time augmentation. Our experimental results found that multi-box prompts augmentation improve the SAM performance, and endowed each pixel with uncertainty. This provides the first paradigm for a reliable SAM

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

R-Chain: a universally composable relay resilience framework for smart grids

Smart grids can be exposed to relay attacks (or wormhole attacks) resulting from weaknesses in cryptographic operations such as authentication and key derivation associated with process automation protocols. Relay attacks refer to attacks in which authentication is evaded without needing to attack the smart grid itself. By using a universal composability model that provides a strong security notion for designing cryptographic operations, we formulate the necessary relay resilience settings for strengthening authentication and key derivation and enhancing relay security in process automation protocols in this paper. We introduce R-Chain, a universally composable relay resilience framework that prevents bypass of cryptographic operations. Our framework provides an ideal chaining functionality that integrates all cryptographic operations such that all outputs from a preceding operation are used as input to the subsequent operation to support relay resilience. We apply R-Chain to provide relay resilience in a practical smart grid process automation protocol, namely WirelessHART