Abstract Purpose of Review The prevalence of non-alcoholic fatty liver disease (NAFLD) is rapidly increasing worldwide, making it the leading cause of liver related morbidity and mortality. Currently, liver biopsy is the gold standard for assessing individuals with steatohepatitis and fibrosis. However, its invasiveness, sampling variability, and impracticality for large-scale screening has driven the search for non-invasive methods for early diagnosis and staging. In this review, we comprehensively summarise the evidence on the diagnostic performance and limitations of existing non-invasive serum biomarkers and scores in the diagnosis and evaluation of steatosis, steatohepatitis, and fibrosis. Recent Findings Several non-invasive serum biomarkers and scores have been developed over the last decade, although none has successfully been able to replace liver biopsy. The introduction of new NAFLD terminology, namely metabolic dysfunction-associated fatty liver disease (MAFLD) and more recently metabolic dysfunction-associated steatotic liver disease (MASLD), has initiated a debate on the interchangeability of these terminologies. Indeed, there is a need for more research on the variability of the performance of non-invasive serum biomarkers and scores across the diagnostic entities of NAFLD, MAFLD and MASLD. Summary There remains a significant need for finding valid and reliable non-invasive methods for early diagnosis and assessment of steatohepatitis and fibrosis to facilitate prompt risk stratification and management to prevent disease progression and complications. Further exploration of the landscape of MASLD under the newly defined disease subtypes is warranted, with the need for more robust evidence to support the use of commonly used serum scores against the new MASLD criteria and validation of previously developed scores. 

pdf/non-invasive-scores-and-serum-biomarkers-for-fatty-liver-in-14uoslwglt.pdf

Non-invasive Scores and Serum Biomarkers for Fatty Liver in the Era of Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD): A Comprehensive Review From NAFLD to MAFLD and MASLD

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.

/pdf/time-series-based-edge-resource-prediction-and-parallel-28srks4k.pdf

Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment

Mine video surveillance has a key role in ensuring the production safety of intelligent mining. However, existing mine intelligent monitoring technology mainly processes the video data in the cloud, which has problems, such as network congestion, large memory consumption, and untimely response to regional emergencies. In this paper, we address these limitations by utilizing the edge-cloud collaborative optimization framework. First, we obtained a coarse model using the edge-cloud collaborative architecture and updated this to realize the continuous improvement of the detection model. Second, we further proposed a target detection model based on the Vision Swin Transformer-YOLOv5(ViST-YOLOv5) algorithm and improved the model for edge device deployment. The experimental results showed that the object detection model based on ViST-YOLOv5, with a model size of only 27.057 MB, improved the average detection accuracy is by 25% compared to the state-of-the-art model, which makes it suitable for edge-end deployment in mining workface. For the actual mine surveillance video, the edge-cloud collaborative architecture can achieve better performance and robustness in typical application scenarios, such as weak lighting and occlusion, which verifies the feasibility of the designed architecture.

/pdf/ecvist-mine-intelligent-monitoring-based-on-edge-computing-9hu48k1i.pdf

ECViST: Mine Intelligent Monitoring Based on Edge Computing and Vision Swin Transformer-YOLOv5

Edge computing permits the computation process at the edge of the network for enhancing the efficiency of the data access. The problem of optimal data block placement entirely depends on the maximization of interdependent factors, which corresponds to an NP‐hard problem. In this paper, the Hybrid Harris Hawk‐Salp Swarm‐based Optimization, Data Placement and Task Scheduling (HHHSS‐ODPTS) scheme is proposed for improving the user experience in edge computing. This data placement is devised considering the popularity and user preference of data blocks, storage capacity of edge server, and the ratio of replacement associated with the edge servers. This proposed scheme utilized a 2/3‐approximation method for essential mapping of tasks with generated containers of the edge server. The simulation results confirmed that the proposed HHHSS‐ODPTS scheme is better at data response time, response time of tasks, number of the replaced data blocks, and hit rate of tasks with different capacity of data storage, number of required data blocks, and data popularity.

Hybrid Harris Hawk-Salp swarm optimization algorithm-based integrated optimal data placement and task scheduling for improving the user experience in edge computing

The role of ANGPTL8 in metabolism and cardiovascular diseases: consensus and controversy

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency.

/pdf/edge-computing-task-scheduling-strategy-based-on-load-4vaq6scqa7.pdf

Edge computing task scheduling strategy based on load balancing

Metabolic dysfunction-associated steatotic liver disease(MASLD), formerly known as non-alcoholic fatty liver disease(NAFLD), has become a major cause of chronic liver disease and a significant risk factor for hepatocellular carcinoma, which poses a huge burden on global public health and economy. MASLD includes steatotic liver disease, steatohepatitis, and cirrhosis, and the latter two cause great harm to human health and life, even complicated with liver cancer. Immunologic mechanism plays a major role in promoting its development into hepatitis and cirrhosis. Now more and more evidences show that T cells play an important role in the progression of MASLD. In this review, we discuss the double roles of T cells in MASLD from the perspective of T cell response pathways, as well as new evidences regarding the possible application of immunomodulatory therapy in MASH. 

The double roles of T cell-mediated immune response in the progression of MASLD.

From NAFLD to HCC: Advances in noninvasive diagnosis.

Non-alcoholic fatty liver disease (NAFLD) is the most frequent cause of chronic liver disease, with a range of conditions including non-alcoholic fatty liver, non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma (HCC). Currently recognized as the liver component of the metabolic syndrome, NAFLD is intimately linked to metabolic diseases. Angiopoietin-like proteins (ANGPTLs) comprise a class of proteins that resemble angiopoietins structurally. It is closely related to obesity, insulin resistance and lipid metabolism, and may be the critical factor of metabolic syndrome. In recent years, many studies have found that there is a certain correlation between ANGPTLs and the occurrence and progression of NAFLD disease spectrum. This article reviews the possible mechanisms and roles of ANGPTL protein in the pathogenesis and progression of NAFLD. 

Role of the angiopoietin-like protein family in the progression of NAFLD

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Edge computing task scheduling strategy based on load balancing

The double roles of T cell-mediated immune response in the progression of MASLD.

From NAFLD to HCC: Advances in noninvasive diagnosis.

Role of the angiopoietin-like protein family in the progression of NAFLD