A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE.

Purpose: Type II diabetes mellitus (T2DM) has been found to increase the mortality of patients with hepatocellular carcinoma (HCC). Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE).

Patients And Methods: The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center.

Zero-Trust Access Control Mechanism Based on Blockchain and Inner-Product Encryption in the Internet of Things in a 6G Environment.

Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption.

Fine-Grained Building Classification in Rural Areas Based on GF-7 Data.

Building type information is widely used in various fields, such as disaster management, urbanization studies, and population modelling. Few studies have been conducted on fine-grained building classification in rural areas using China's Gaofen-7 (GF-7) high-resolution stereo mapping satellite data. In this study, we employed a two-stage method combining supervised classification and unsupervised clustering to classify buildings in the rural area of Pingquan, northern China, based on building footprints, building heights, and multispectral information extracted from GF-7 data.

Genome-Wide Identification and Analysis of the Gene Family in Cotton: Evolution and Expression Profiles During Normal Growth and Stress Response.

Background: The gene family of myelomatosis (MYC), serving as a transcription factor in the jasmonate (JA) signaling pathway, displays a significant level of conservation across diverse animal and plant species. Cotton is the most widely used plant for fiber production. Nevertheless, there is a paucity of literature reporting on the members of MYCs and how they respond to biotic stresses in cotton.

Structural basis of FpGalNase and its combination with FpGalNAcDeAc for efficient A-to-O blood group conversion.

Transfusion safety and blood typing continue to present significant challenges in clinical practice, including risks of incorrect blood transfusions and blood shortages. One promising solution is the enzymatic conversion of all red blood cell (RBC) types into universal O-type RBCs. However, the major obstacle to this strategy is the relatively low catalytic efficiency of the enzymes involved.