VASCULAR ENDOTHELIAL DYSFUNCTION IMPROVEMENTS IN PATIENTS WITH UREMIA USING PENTOXIFYLLINE-SUPPRESSING NLRP3 EXPRESSIONS AND HMGB1 RELEASE.

This study aimed to investigate the protective effect of pentoxifylline (PTX) on vascular endothelial dysfunction in uremia. The human aortic endothelial cells (HAECs) required for the experiments were all obtained from the National Collection of Authenticated Cell Cultures (Salisbury, UK). The permeability of HAECs was assessed.

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on T2-Weighted MRI and Multifeature Fusion.

Background: MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information.

Purpose: To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph.

A heuristic method for rapid and automatic radiofrequency ablation planning of liver tumors.

Purpose: Preprocedural planning is a key step in radiofrequency ablation (RFA) treatment for liver tumors, which is a complex task with multiple constraints and relies heavily on the personal experience of interventional radiologists, and existing optimization-based automatic RFA planning methods are very time-consuming. In this paper, we aim to develop a heuristic RFA planning method to rapidly and automatically make a clinically acceptable RFA plan.

Methods: First, the insertion direction is heuristically initialized based on tumor long axis.

Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study.

Introduction: Fetal growth restriction (FGR) is associated with placental abnormalities, and its precise diagnosis is challenging. This study aimed to explore the role of radiomics based on placental MRI in predicting FGR.

Methods: A retrospective study using T2-weighted placental MRI data were conducted.

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

Objectives: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images.

Methods: In this retrospective study, T1-weighted (T1) and T1-weighted contrast-enhanced (T1ce) MRI images of 649 patients who underwent radiotherapy from August 2019 to January 2022 were included. A total of 5163 metastases were manually annotated by neuroradiologists.

Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.

Purposes And Objectives: The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT).

Methods: A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively.

Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer.

Background: To explore an effective predictive model based on PET/CT radiomics for the prognosis of early-stage uterine cervical squamous cancer.

Methods: Preoperative PET/CT data were collected from 201 uterine cervical squamous cancer patients with stage IB-IIA disease (FIGO 2009) who underwent radical surgery between 2010 and 2015. The tumor regions were manually segmented, and 1318 radiomic features were extracted.

3D Graph-Connectivity Constrained Network for Hepatic Vessel Segmentation.

Segmentation of hepatic vessels from 3D CT images is necessary for accurate diagnosis and preoperative planning for liver cancer. However, due to the low contrast and high noises of CT images, automatic hepatic vessel segmentation is a challenging task. Hepatic vessels are connected branches containing thick and thin blood vessels, showing an important structural characteristic or a prior: the connectivity of blood vessels.

Synthesis of Sulfonamide-Based Ynamides and Ynamines in Water.

Ynamides, though relatively more stable than ynamines, are still moisture-sensitive and prone to hydration especially under acidic and heating conditions. Here we report an environmentally benign, robust protocol to synthesize sulfonamide-based ynamides and arylynamines via Sonogashira coupling reactions in water, using a readily available quaternary ammonium salt as the surfactant.

Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints.

Segmenting the bladder wall from MRI images is of great significance for the early detection and auxiliary diagnosis of bladder tumors. However, automatic bladder wall segmentation is challenging due to weak boundaries and diverse shapes of bladders. Level-set-based methods have been applied to this task by utilizing the shape prior of bladders.

A nordehydroabietyl amide-containing chiral diene for rhodium-catalysed asymmetric arylation to nitroolefins.

A highly enantioselective rhodium catalysed asymmetric arylation (RCAA) of nitroolefins with arylboronic acids is presented using a newly developed, C-symmetric, non-covalent interacted, phellandrene derived, nordehydroabietyl amide-containing chiral diene under mild conditions. Stereoelectronic effects were studied, suggesting an activation of the bound substrate through the secondary amide as a hydrogen-bond donor.