Injectable Hydrogels Including Magnetic Nanosheets for Multidisciplinary Treatment of Hepatocellular Carcinoma via Magnetic Hyperthermia.

Relapse and unresectability have become the main obstacle for further improving hepatocellular carcinoma (HCC) treatment effect. Currently, single therapy for HCC in clinical practice is limited by postoperative recurrence, intraoperative blood loss and poor patient outcomes. Multidisciplinary therapy has been recognized as the key to improving the long-term survival rate for HCC. However, the clinical application of HCC synthetic therapy is restricted by single functional biomaterials. In this study, a magnetic nanocomposite hydrogel (CG-IM) with iron oxide nanoparticle-loaded mica nanosheets (Iron oxide nanoparticles@Mica, IM) is reported. This biocompatible magnetic hydrogel integrated high injectability, magnetocaloric property, mechanical robustness, wet adhesion, and hemostasis, leading to efficient HCC multidisciplinary therapies including postoperative tumor margin treatment and percutaneous locoregional ablation. After minimally invasive hepatectomy of HCC, the CG-IM hydrogel can facilely seal the bleeding hepatic margin, followed by magnetic hyperthermia ablation to effectively prevent recurrence. In addition, CG-IM hydrogel can inhibit unresectable HCC by magnetic hyperthermia through the percutaneous intervention under ultrasound guidance.

ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

[Study on automatic and rapid diagnosis of distal radius fracture by X-ray].

This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.

[Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix].

Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors.

Extraction of water bodies from high-resolution remote sensing imagery based on a deep semantic segmentation network.

The precise delineation of urban aquatic features is of paramount importance in scrutinizing water resources, monitoring floods, and devising water management strategies. Addressing the challenge of indistinct boundaries and the erroneous classification of shadowed regions as water in high-resolution remote sensing imagery, we introduce WaterDeep, which is a novel deep learning framework inspired by the DeepLabV3 + architecture and an innovative fusion mechanism for high- and low-level features. This methodology first creates a comprehensive dataset of high-resolution remote sensing images, then progresses through the Xception baseline network for low-level feature extraction, and harnesses densely connected Atrous Spatial Pyramid Pooling (ASPP) modules to assimilate multi-scale data into sophisticated high-level features. Subsequently, the network decoder amalgamates the elemental and intricate features and applies dual-line interpolation to the amalgamated dataset to extract aqueous formations from the remote images. Experimental evidence substantiates that WaterDeep outperforms its existing deep learning counterparts, achieving a stellar overall accuracy of 99.284%, FWIoU of 95.58%, precision of 97.562%, recall of 95.486%, and F1 score of 96.513%. It also excels in the precise demarcation of edges and the discernment of shadows cast by urban infrastructure. The superior efficacy of the proposed method in differentiating water bodies in complex urban environments has significant practical applications in real-world contexts.

Squeeze-and-excitation-attention-based mobile vision transformer for grading recognition of bladder prolapse in pelvic MRI images.

BACKGROUND: Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer-aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve. PURPOSE: The purpose of this study is to combine convolutional neural network (CNN) and vision transformer (ViT) for grading bladder prolapse in place of traditional neural networks, and to incorporate attention mechanisms into mobile vision transformer (MobileViT) for assisting in the grading of bladder prolapse. METHODS: This study focuses on the grading of bladder prolapse in pelvic organs using a combination of a CNN and a ViT. First, this study used MobileNetV2 to extract the local features of the images. Next, a ViT was used to extract the global features by modeling the non-local dependencies at a distance. Finally, a channel attention module (i.e., squeeze-and-excitation network) was used to improve the feature extraction network and enhance its feature representation capability. The final grading of the degree of bladder prolapse was thus achieved. RESULTS: Using pelvic MRI images provided by a Huzhou Maternal and Child Health Care Hospital, this study used the proposed method to grade patients with bladder prolapse. The accuracy, Kappa value, sensitivity, specificity, precision, and area under the curve of our method were 86.34%, 78.27%, 83.75%, 95.43%, 85.70%, and 95.05%, respectively. In comparison with other CNN models, the proposed method performed better. CONCLUSIONS: Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis.

Methylation changes of liver DNA during the formation of gallstones.

Aim: To explore the overall methylation changes in liver tissues during the formation of gallstones, as well as the key pathways and genes involved in the process. Methods: Reduced-representation bisulfite sequencing and RNA sequencing were conducted on the liver tissues of mice with gallstones and control normal mice. Results: A total of 8705 differentially methylated regions in CpG and 1410 differentially expressed genes were identified. The joint analysis indicated that aberrant DNA methylation may be associated with dysregulated gene expression in key pathways such as cholesterol metabolism and bile secretion. Conclusion: We propose for the first time that methylation changes in some key pathway genes in liver tissue may be involved in the formation of gallstones.

In Situ Fabrication of Electrospun Magnetic Film Under Laparoscopic Guidance for Preventing Postoperative Recurrence of Hepatocellular Carcinoma.

Despite laparoscopic-guided minimally invasive hepatectomy emerging as the primary approach for resecting hepatocellular carcinoma (HCC), there's still a significant gap in suitable biomaterials that seamlessly integrate with these techniques to achieve effective hemostasis and suppress residual tumors at the surgical margin. Electrospun films are increasingly used for wound closure, yet the employment of prefabricated electrospun films for hemostasis during minimally invasive HCC resection is hindered by prolonged operation times, complexity in implementation, limited visibility during surgery, and inadequate postoperative prevention of HCC recurrence. In this study, we integrated montmorillonite-iron oxide sheets into the PVP polymer framework, enhancing the resulting electrospun polyvinylpyrrolidone (PVP) /montmorillonite-iron oxide (MI) film (abbreviated as PMI) with robustness, hemostatic capability, and magnetocaloric properties. In contrast to the in vitro prefabricated electrospun films, the electrospun PMI film is designed to be formed in situ on liver wounds under laparoscopic guidance during hepatectomy. This design affords superior wound adaptability, facilitating meticulous wound closure and expeditious hemostasis, thereby simplifying the operative process and ultimately alleviating the workload of healthcare professionals. Moreover, when exposed to an alternating magnetic field, the film can efficiently ablate residual tumors, significantly augmenting the treatment efficacy of HCC. This article is protected by copyright. All rights reserved.

Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.

Background: Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ultrasonography (US) is the first-line imaging method for evaluating the thyroid gland and CLNs. However, this assessment relies mainly on the subjective judgment of the sonographer and is very much dependent on the sonographer's experience. This prospective study was designed to construct a machine learning model based on contrast-enhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis in patients with TC. Methods: Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2-minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lymph node imaging video, three machine learning models, namely, support vector machine, linear discriminant analysis (LDA), and decision tree (DT), were constructed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). Results: A total of 75 lymph nodes were included in the study, with 42 benign cases and 33 malignant cases. Among the machine learning models constructed, the support vector machine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%. Conclusions: The machine learning model based on US video is helpful for the diagnosis of whether metastasis occurs in the CLNs of TC patients.

STING agonist enhances the efficacy of programmed death-ligand 1 monoclonal antibody in breast cancer immunotherapy by activating the interferon-ß signalling pathway.

This study aimed to explore the role of a stimulator of interferon (IFN) gene (STING) agonist in breast cancer (BCa) immunotherapy. Clinical samples were collected from 37 patients with BCa. A tumor-bearing mouse model was established by injecting 4T1 cells into the mammary fat pad of mice. STING agonist and atezolizumab were injected in the mice twice a week for 2 weeks. Peripheral blood, tumor mass, lung, liver, brain cortex and kidney samples of the tumor-bearing mice were collected. Anti-IFN alpha receptor subunit 1 (IFNAR1) was used to treat 4T1 cells. Tumor tissues of patients with BCa exhibited lower STING and high programmed cell death protein 1 and programmed death-ligand 1 protein expressions. The STING agonist inhibited 4T1 cell growth in mice (P < 0.001) and increased the IFN-ß level and phosphorylation of STING, TBK1, IRF3 and STAT1 in tumor mass of tumor-bearing mice (P < 0.001). It synergized with atezolizumab to inhibit 4T1 cell growth in mice and increased tumor necrosis factor-α, IFN-ß, interleukin-10 and IFN-γ levels in the peripheral blood and tumor mass (P < 0.01). It synergized with atezolizumab to increase CD8+ cytotoxic T cells and decrease FOXP3+ Treg cells in the tumor-bearing mouse model. The STING agonist was nontoxic to the lung, liver, brain cortex and kidney. Anti-IFNAR1 reversed the STING agonist promotion on TBK1, IRF3 and STAT1 phosphorylation in 4T1 cells (P < 0.01). STING agonists enhance the efficacy of atezolizumab in BCa immunotherapy by activating the IFN-ß signaling pathway.