Histopathological diagnosis of microvascular invasion in hepatocellular carcinoma: Is it reliable?

Background: Microvascular invasion (MVI) is a critical prognostic factor for postoperative hepatocellular carcinoma recurrence, but the reliability of its current pathological diagnosis remains uncertain.

Aim: To evaluate the accuracy of current 7-point sampling methods and propose an optimal pathological protocol using whole-mount slide imaging (WSI) for better MVI detection.

Methods: We utilized 40 New Zealand white rabbits to establish VX2 liver tumor models.

Doppler ultrasound imaging and shear wave elastography for evaluation of interstitial fibrosis/tubular atrophy in IgA nephropathy.

Purpose: The purpose of this study was to explore the value of Doppler ultrasound imaging and shear wave elastography (SWE) in evaluating renal interstitial fibrosis/tubular atrophy (IFTA).

Methods: During April 2019 and November 2023, biopsy-proven IgA nephropathy (IgAN) patients were enrolled in our study. Conventional ultrasound, Doppler ultrasound imaging and SWE measurements were performed, and related parameters were collected.

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress.

Development and validation of a prognostic nomogram for patients with ganglioneuroblastoma: A SEER-based study.

Background: The objective of this study was to construct a prognostic nomogram for ganglioneuroblastoma (GNB), as the prognosis of GNB is difficult to accurately predict before therapy.

Methods: The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients included in this study were randomly divided into a development group and a validation group at a ratio of 7:3.

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet.

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval.

A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval.

A Novel Coacervate Embolic Agent for Tumor Chemoembolization.

Transcatheter arterial chemoembolization (TACE) has proven effective in blocking tumor-supplied arteries and delivering localized chemotherapeutic treatment to combat tumors. However, traditional embolic TACE agents exhibit certain limitations, including insufficient chemotherapeutic drug-loading and sustained-release capabilities, non-biodegradability, susceptibility to aggregation, and unstable mechanical properties. This study introduces a novel approach to address these shortcomings by utilizing a complex coacervate as a liquid embolic agent for tumor chemoembolization.

Autophagy-amplifying nanoparticles evoke immunogenic cell death combined with anti-PD-1/PD-L1 for residual tumors immunotherapy after RFA.

Incomplete radiofrequency ablation (IRFA) triggers mild protective autophagy in residual tumor cells and results in an immunosuppressive microenvironment. This accelerates the recurrence of residual tumors and causes resistance to anti-PD-1/PDL1 therapy, which bringing a great clinical challenge in residual tumors immunotherapy. Mild autophagy activation can promote cancer cell survival while further amplification of autophagy contributes to immunogenic cell death (ICD).

Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: an experimental study.

Background: Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). However, MVI cannot be detected by conventional imaging. To localize MVI precisely on magnetic resonance (MR) images, we evaluated the feasibility and accuracy of 3-dimensional (3D) histology-MR image fusion of the liver.

Consistency of transesophageal echocardiography and angiography in grading paravalvular regurgitation during transcatheter aortic valve replacement.

Background: The Valve Academic Research Consortium 3 (VARC-3) standardizes the classification criteria and Doppler parameters for paravalvular regurgitation (PVR) by echocardiography. However, the consistency between transesophageal echocardiography (TEE) and angiography in grading (using the VARC-3 criteria) of PVR during transcatheter aortic valve replacement (TAVR) is unclear.

Methods: Forty-six patients who underwent TEE and angiography during TAVR were retrospectively included.

A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet.

Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor.

Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.

Background: Given its progressive deterioration in the clinical course, noninvasive assessment and risk stratification for the severity of renal fibrosis in chronic kidney disease (CKD) are required. We aimed to develop and validate an end-to-end multilayer perceptron (MLP) model for assessing renal fibrosis in CKD patients based on real-time two-dimensional shear wave elastography (2D-SWE) and clinical variables.

Methods: From April 2019 to December 2021, a total of 162 patients with CKD who underwent a kidney biopsy and 2D-SWE examination were included in this single-center, cross-sectional, and prospective clinical study.

Identification of citrus diseases based on AMSR and MF-RANet.

Background: As one of the most widely planted fruit trees in southern China, citrus occupies an important position in the agriculture field and forestry economy in China. There are many kinds of citrus diseases. If citrus infected with diseases cannot be controlled in time, it easily seriously affects citrus production and causes large economic losses.

DS-MENet for the classification of citrus disease.

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus.

MDAM-DRNet: Dual Channel Residual Network With Multi-Directional Attention Mechanism in Strawberry Leaf Diseases Detection.

The growth of strawberry plants is affected by a variety of strawberry leaf diseases. Yet, due to the complexity of these diseases' spots in terms of color and texture, their manual identification requires much time and energy. Developing a more efficient identification method could be imperative for improving the yield and quality of strawberry crops.

MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism.

Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.

CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases.

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study.

Investigating Biochar-Derived Dissolved Organic Carbon (DOC) Components Extracted Using a Sequential Extraction Protocol.

Biochar-derived dissolved organic carbon (DOC), as the most important component of biochar, can be released on farmland, improving fertility and playing a role in soil amendment and remediation. The complexity of molecular structures and diversity of DOC compounds have influenced these functions to some extent. A sequential extract protocol consisting of water (25 °C), hot water (80 °C), and NaOH solution (0.

Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis.

Background: Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). To perform a meta-analysis to investigate the diagnostic performance of radiomics for the preoperative evaluation of MVI in HCC and the effect of potential factors.

Materials And Methods: A systematic literature search was performed in PubMed, Embase, and the Cochrane Library for studies focusing on the preoperative evaluation of MVI in HCC with radiomics methods.

DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification.

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality.

BC-DUnet-based segmentation of fine cracks in bridges under a complex background.

Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation techniques based on convolutional neural networks, new opportunities emerge in bridge crack detection.

Computer-Assisted Hepatocellular Carcinoma Ablation Planning Based on 3-D Ultrasound Imaging.

To evaluate computer-assisted hepatocellular carcinoma (HCC) ablation planning based on 3-D ultrasound, 3-D ultrasound images of 60 HCC lesions from 58 patients were obtained and transferred to a research toolkit. Compared with virtual manual ablation planning (MAP), virtual computer-assisted ablation planning (CAP) consumed less time and needle insertion numbers and exhibited a higher rate of complete tumor coverage and lower rate of critical structure injury. In MAP, junior operators used less time, but had more critical structure injury than senior operators.