Multiscale High-Level Feature Fusion for Histopathological Image Classification

Histopathological image classification is one of the most important steps for disease diagnosis. We proposed a method for multiclass histopathological image classification based on deep convolutional neural network referred to as coding network. It can gain better representation for the histopathological image than only using coding network. The main process is that training a deep convolutional neural network is to extract high-level feature and fuse two convolutional layers’ high-level feature as multiscale high-level feature. In order to gain better performance and high efficiency, we would employ sparse autoencoder (SAE) and principal components analysis (PCA) to reduce the dimensionality of multiscale high-level feature. We evaluate the proposed method on a real histopathological image dataset. Our results suggest that the proposed method is effective and outperforms the coding network

Medical Image Classification Based on Deep Features Extracted by Deep Model and Statistic Feature Fusion with Multilayer Perceptron‬

Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Traditional methods rely mainly on the shape, color, and/or texture features as well as their combinations, most of which are problem-specific and have shown to be complementary in medical images, which leads to a system that lacks the ability to make representations of high-level problem domain concepts and that has poor model generalization ability. Recent deep learning methods provide an effective way to construct an end-to-end model that can compute final classification labels with the raw pixels of medical images. However, due to the high resolution of the medical images and the small dataset size, deep learning models suffer from high computational costs and limitations in the model layers and channels. To solve these problems, in this paper, we propose a deep learning model that integrates Coding Network with Multilayer Perceptron (CNMP), which combines high-level features that are extracted from a deep convolutional neural network and some selected traditional features. The construction of the proposed model includes the following steps. First, we train a deep convolutional neural network as a coding network in a supervised manner, and the result is that it can code the raw pixels of medical images into feature vectors that represent high-level concepts for classification. Second, we extract a set of selected traditional features based on background knowledge of medical images. Finally, we design an efficient model that is based on neural networks to fuse the different feature groups obtained in the first and second step. We evaluate the proposed approach on two benchmark medical image datasets: HIS2828 and ISIC2017. We achieve an overall classification accuracy of 90.1% and 90.2%, respectively, which are higher than the current successful methods

The Effect of an External Magnetic Field on the Electrochemical Capacitance of Nanoporous Nickel for Energy Storage

This work investigates the effect of a magnetic field on the electrochemical performance of nanoporous nickel (np-Ni). We first compare the electrochemical capacitance of np-Ni electrodes, which were prepared using the chemical dealloying strategy under different magnetic flux densities (B = 0, 500 mT). Our experimental data show that np-Ni500 prepared under an external magnetic field of 500 mT exhibits a much better electrochemical performance, in comparison with that (np-Ni0) prepared without applying a magnetic field. Furthermore, the specific capacitance of the np-Ni0 electrode could be further enhanced when we increase the magnetic flux densities from 0 T to 500 mT, whereas the np-Ni500 electrode exhibits a stable electrochemical performance under different magnetic flux densities (B = 0 mT, 300 mT, 500 mT). This could be attributed to the change in the electrochemical impedance of the np-Ni0 electrode induced by an external magnetic field. Our work thus offers an alternative method to enhance the electrochemical energy storage of materials

The anti-hepatic fibrosis effects of chlorogenic acid extracted from Artemisia Capillaris Herba on CCl4-induced mice via regulating TGF-β1/smad3 pathway

Introduction: Artemisia Capillaris Herba, a famous traditional Chinese medicine, is effective for the treatment of hepatic fibrosis(HF) in clinical applications. Research has confirmed that chlorogenic acid (CA), an organic acid compound was extracted from Artemisia Capillaris Herba, could reduce the hepatocyte injury induced by HF, however, its mechanism of anti-HF is still unclear, and we investigated whether CA could help treating HF mice. Methods: In this study, we evaluated the therapeutic effect of CA on HF mice induced by CCl4, which was extracted from Artemisia Capillaris Herba and identified by 1H NMR and 13C NMR spectroscopy. Seventy two NIH mice were divided into following groups: normal group, model group, low, medium and high dose of CA groups (7.5, 15, 30 mg/kg) and colchicine (Colc)-positive control group (0.2 mg/kg). All mice were injected 40% CCl4 for 8 weeks with a 24 h interval except normal mice. Each drug group and Colc group were given intragastric administration for 40 days while modeling. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), collage IV (Col-IV), hyaluronic acid (HA), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), procollagen typeⅢ (PC-Ⅲ), malondialdehyde (MDA) and laminin (LN) levels were detected by ELISA, samd3 and TGF-β1 were examined by immunohistochemistry and western blotting and the liver and kidney tissues were observed by HE. Results: At the end of administrations, the body weight of mice was decreased and the levels of ALT, AST, Col-IV, HA, IL-6, TNF-α, LN, PC-III, and MDA were increased in the HF modle mice compared with that of normal mice. Compared with the HF mice only, treatment with CA significantly decreased the levels of ALT, AST, Col-IV, HA, IL-6, TNF-α, LN, PC-III, and MDA. The HE staining results showed that the hepatic and nephritic injury were significantly alleviated after CA treatment. And the smad3 and TGF-β1 expression were inhibited in the CA-treated mice in comparison with the model mice. Conclusion: Conclusively, CA treatment could attenuate HF through the regulation of TGF-β1/smad3 pathway, suggesting that CA may be an effective component of Artemisia Capillaris Herba in the treatment of HF

Quality of systematic reviews with meta-analyses of resveratrol:A methodological systematic review

Recently, several meta-analyses (MAs) have focused on the health effects of resveratrol. However, the methodological and reporting quality of these MAs has not yet been fully evaluated so far. Therefore, the present study evaluated the quality of these MAs through a methodological systematic review. Systematic searches were conducted in PubMed, Embase, Web of Science, and Cochrane Library from inception until May 20, 2022, and PubMed was used to update the search until September 6, 2023. The methodological and reporting quality of the selected MAs was evaluated using AMSTAR-2 and PRISMA 2009. Fifty-one MAs published during 2013–2023 were included. In each review, the number of primary studies ranged from 3 to 37, and the number of participants ranged from 50 to 2114. Among the first-listed primary outcomes, only 23 (45.10%) were “positive.” As for the methodological quality, most MAs (44, 86.27%) on resveratrol were rated critically low. Inadequate reporting of the included MAs mainly involved items 2 (“Structured summary”), 5 (“Protocol and registration”), 8 (“Search”), 9 (“Study selection”), 10 (“Data collection process”), 12 (“Risk of bias in individual studies”), and 24 (“Summary of evidence”) based on the PRISMA 2009. Additionally, journal's impact factor, number of authors, and funding support were positively associated with the overall methodological quality but were not statistically significant (p &gt; 0.05). Future MAs on resveratrol require better design, implementation, and reporting by following the Cochrane Handbook, AMSTAR-2, and PRISMA.</p

Identification of PHB2 as a Potential Biomarker of Luminal A Breast Cancer Cells Using a Cell-Specific Aptamer

Precise diagnosis of breast cancer molecular subtypes remains a great challenge in clinics. The present molecular biomarkers are not specific enough to classify breast cancer subtypes precisely, which requests for more accurate and specific molecular biomarkers to be discovered. Aptamers evolved by the cell-systematic evolution of ligands by exponential enrichment (SELEX) method show great potential in the discovery and identification of cell membrane targets via aptamer-based cell membrane protein pull-down, which has been regarded as a novel and powerful weapon for the discovery and identification of new molecular biomarkers. Herein, a cell membrane protein PHB2 was identified as a potential molecular biomarker specifically expressed in the cell membranes of MCF-7 breast cancer cells using a DNA aptamer MF3Ec. Further experiments demonstrated that the PHB2 protein is differentially expressed in the cell membranes of MCF-7, SK-BR-3, and MDA-MB-231 breast cancer cells and MCF-10A cells, and the binding molecular domains of aptamer MF3Ec and anti-PHB2 antibodies to the PHB2 protein are different due to there being no obvious competitions between aptamer MF3Ec and anti-PHB2 antibodies in the binding to the cell membranes of target MCF-7 cells. Due to those four cells belonging to luminal A, HER2-positive, and triple-negative breast cancer cell subtypes and human normal mammary epithelial cells, respectively, the PHB2 protein in the cell membrane may be a potential biomarker for precise diagnosis of the luminal A breast cancer cell subtype, which is endowed with the ability to differentiate the luminal A breast cancer cell subtype from HER2-positive and triple-negative breast cancer cell subtypes and human normal mammary epithelial cells, providing a new molecular biomarker and therapeutic target for the accurate and precise classification and diagnostics and personalized therapy of breast cancer