Comparison of Body Characteristics, Carotenoid Composition, and Nutritional Quality of Chinese Mitten Crab (Eriocheir sinensis) with Different Hepatopancreas Redness.

In this study, we investigated the body characteristics, carotenoid composition, and nutritional quality of Eriocheir sinensis with different hepatopancreas redness (a*). We distributed the crabs into two groups based on the hepatopancreas a* values and compared their body characteristics, chroma, carotenoid composition, and protein, lipid, total sugar, amino acid, and fatty acid content via paired t-test. The results revealed that the relationships between hepatopancreas a* values and crab quality are sex specific. In female crabs, the differences in nutritional characteristics were evident mainly in the hepatopancreases and ovaries. In the redder hepatopancreases, the content of zeaxanthin and ß-carotene increased, and the levels of C22:6n3 and C20:5n3 decreased (p < 0.05). In the ovaries, the content of astaxanthin, canthaxanthin, ß-carotene, umami, and sweet amino acids were lower in the redder hepatopancreas crabs (p < 0.05). In male crabs, there were positive relationships between hepatopancreas a* and amino acid and fatty acid content. The content of leucine, arginine, and total umami amino acids in muscles and of unsaturated fatty acids and n-6 polyunsaturated fatty acids in hepatopancreases and testicles increased with increasing hepatopancreas a* values (p < 0.05). Therefore, the redder the hepatopancreas, the higher the nutritional quality of male crabs.

Extracellular vesicles as carriers for noncoding RNA-based regulation of macrophage/microglia polarization: an emerging candidate regulator for lung and traumatic brain injuries.

Macrophage/microglia function as immune defense and homeostatic cells that originate from bone marrow progenitor cells. Macrophage/microglia activation is historically divided into proinflammatory M1 or anti-inflammatory M2 states based on intracellular dynamics and protein production. The polarization of macrophages/microglia involves a pivotal impact in modulating the development of inflammatory disorders, namely lung and traumatic brain injuries. Recent evidence indicates shared signaling pathways in lung and traumatic brain injuries, regulated through non-coding RNAs (ncRNAs) loaded into extracellular vesicles (EVs). This packaging protects ncRNAs from degradation. These vesicles are subcellular components released through a paracellular mechanism, constituting a group of nanoparticles that involve exosomes, microvesicles, and apoptotic bodies. EVs are characterized by a double-layered membrane and are abound with proteins, nucleic acids, and other bioactive compounds. ncRNAs are RNA molecules with functional roles, despite their absence of coding capacity. They actively participate in the regulation of mRNA expression and function through various mechanisms. Recent studies pointed out that selective packaging of ncRNAs into EVs plays a role in modulating distinct facets of macrophage/microglia polarization, under conditions of lung and traumatic brain injuries. This study will explore the latest findings regarding the role of EVs in the progression of lung and traumatic brain injuries, with a specific focus on the involvement of ncRNAs within these vesicles. The conclusion of this review will emphasize the clinical opportunities presented by EV-ncRNAs, underscoring their potential functions as both biomarkers and targets for therapeutic interventions.

Integrating Reliable Pt-S Bond-Mediated 3D DNA Nanomachine with Magnetic Separation in a Homogeneous Electrochemical Strategy for Exosomal MicroRNA Detection with Low Background and High Sensitivity.

Precise and sensitive analysis of exosomal microRNA (miRNA) is of great importance for noninvasive early disease diagnosis, but it remains a great challenge to detect exosomal miRNA in human blood samples because of their small size, high sequence homology, and low abundance. Herein, we integrated reliable Pt-S bond-mediated three-dimensional (3D) DNA nanomachine and magnetic separation in a homogeneous electrochemical strategy for the detection of exosomal miRNA with low background and high sensitivity. The 3D DNA nanomachine was easily prepared via a facile and rapid freezing method, and it was capable of resisting the influence of biothiols, thus endowing it with high stability. Notably, the as-developed magnetic 3D DNA nanomachine not only enabled the detection system to have a low background but also coupled with liposome nanocarriers to synergistically amplify the current signal. Consequently, by ingeniously combining the low background and multiple signal-amplification strategies in homogeneous electrochemical biosensing, highly sensitive detection of exosomal miRNA was successfully achieved. More significantly, with good anti-interference ability, the as-proposed method could effectively discriminate plasma samples from cancer patients and healthy subjects, thus showing a high potential for application in the nondestructive early clinical diagnosis of disease.

Tuning the magnetic ordering driven by cationic antisite defects in the Li(ZnMn)As system.

The electronic structure and magnetic properties of Li(ZnMn)As with antisite defects have been investigated by using first-principles calculations within the Perdew-Burke-Ernzerhof generalized gradient approximation. The cation antisite defect induced by Zn substitution for As was considered. Mn-3d, As-4p, Zn-4s, and Zn-4p were involved in the formation of d-sp hybrid orbitals, which enhanced the non-localized properties of Mn-3d electrons and provided a channel of Mn(↑)-As(↓)-ZnAs(↓)-Mn(↑) for indirect exchange of electrons between the magnetic ions. The antisite defect of Zn-substituted As belonged to the acceptor doping, rendering the compound p-type characteristics. The existence of the extra free hole carriers regulated the magnetic ordering transition. The ferromagnetic coupling between the Mn magnetic dopants was more favorable in the system with an antisite defect. In this paper, a novel type of dilute magnetic semiconductor with controllable carriers was designed and the mechanism of ferromagnetic coupling was revealed, which provided a theoretical reference for the subsequent studies.

Multi-scale, multi-dimensional binocular endoscopic image depth estimation network.

During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) simulation model for generating image datasets and acquiring depth information in real-time. Here, we proposed an end-to-end multi-scale supervisory depth estimation network (MMDENet) model for the depth estimation of pairs of binocular images. The proposed MMDENet highlights a multi-scale feature extraction module incorporating contextual information to enhance the correspondence precision of poorly exposed regions. A multi-dimensional information-guidance refinement module is also proposed to refine the initial coarse disparity map. Statistical experimentation demonstrated a 3.14% reduction in endpoint error compared to state-of-the-art methods. With a processing time of approximately 30fps, satisfying the requirements of real-time operation applications. In order to validate the performance of the trained MMDENet in actual endoscopic images, we conduct both qualitative and quantitative analysis with 93.38% high precision, which holds great promise for applications in surgical navigation.

Exploring the methylation status of CFTR and PKIA genes as potential biomarkers for lung adenocarcinoma.

BACKGROUND: One of the most prevalent cancers in the world is lung cancer, with adenocarcinoma (LUAD) making up a significant portion of cases. According to the National Cancer Institute (NCI), there are new cases and fatality rates per 100,000 individuals as follows: New instances of lung and bronchial cancer occur annually at a rate of 50.0 per 100,000 persons. The yearly death rate for men and women is 35.0 per 100,000. DNA methylation is one of the earliest discovered and widely studied epigenetic regulatory mechanisms, and its abnormality is closely related to the occurrence and development of cancer. However, the prognostic value of DNA methylation and LUAD needs to be further explored to improve the survival prediction of LUAD patients. METHODS: The transcriptome data and clinical data of LUAD were downloaded from TCGA and GEO databases, and the Illumina Human Methylation450 array (450k array) data were downloaded from the TCGA database. Firstly, the intersection of the expressed genes of the two databases is corrected, the differential analysis is performed, and the methylation data is evaluated by the MethylMix package to obtain differentially methylated genes. Independent prognostic genes were screened out using univariate and multivariate Cox regression analysis, and a methylation prognostic model was developed using univariate Cox analysis and validated with the GSE30219 dataset in the GEO database. Survival analysis between methylation high-risk and low-risk groups was performed and a methylation-based gene prognostic model was constructed. Finally, the prediction of potential drugs associated with the LUAD gene signature using Drug Sensitivity Genomics in Cancer (GDSC). RESULTS: In this study, a total of 555 samples from the TCGA database and 307 samples from GSE30219 were included, and a total of 24 differential methylation driver genes were identified. Univariate and multivariate Cox regression analyzes were used to screen out independent prognostic genes, involving 2 genes: CFTR, PKIA. Survival analysis was different between the methylation high-risk group and the low-risk group, the CFTR high methylation group and the low methylation group were poor, and the opposite was true for PKIA. CONCLUSIONS: Our study revealed that the methylation status of CFTR and PKIA can serve as potential prognostic biomarkers and therapeutic targets in lung cancer.

Generalization of learned Fourier-based phase-diversity wavefront sensing.

Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a better estimate of the unknown aberrations. However, the network relies significantly on the training settings, such as imaging object and optical system parameters, resulting in a weak generalization ability. Here we propose a generalized Fourier-based PDWS method by combining an object-independent network with a system-independent image processing procedure. We demonstrate that a network trained with a specific setting can be applied to any image regardless of the actual settings. Experimental results show that a network trained with one setting can be applied to images with four other settings. For 1000 aberrations with RMS wavefront errors bounded within [0.2 λ, 0.4 λ], the mean RMS residual errors are 0.032 λ, 0.039 λ, 0.035 λ, and 0.037 λ, respectively, and 98.9% of the RMS residual errors are less than 0.05 λ.

Metal-mediated Fe3O4@polydopamine-aptamer capture nanoprobe coupling multifunctional MXene@Au@Pt nanozyme for direct and portable photothermal analysis of circulating breast cancer cells.

Breast cancer (BC) is the most common cancer in the world and circulating tumor cells (CTCs) are reliable biomarkers for early breast cancer diagnosis in a non-invasive manner. However, effective isolation and sensitive detection of BC-CTCs by portable devices in human blood samples are extremely challenging. Herein, we proposed a highly sensitive and portable photothermal cytosensor for direct capture and quantification of BC-CTCs. To achieve efficient isolation of BC-CTCs, aptamer functionalized Fe3O4@PDA nanoprobe was facilely prepared through Ca2+-mediated DNA adsorption. To further detect the captured BC-CTCs with high sensitivity, multifunctional two-dimensional Ti3C2@Au@Pt nanozyme was synthesized, which not only possessed superior photothermal effect but also exhibited high peroxidase-like activity for catalyzing 3,3',5,5'-tetramethylbenzidine (TMB) to produce TMB oxide (oxTMB) with a strong photothermal characteristic, combining with Ti3C2@Au@Pt to synergistically amplify the temperature signal. Moreover, numerous Ti3C2@Au@Pt nanocomposites would be selectively attained on the BC-CTCs surface through multi-aptamer recognition and binding strategy, which further enhanced the specificity and facilitated signal amplification. Therefore, direct separation and highly sensitive detection of BC-CTCs was successfully achieved in human blood samples. More significantly, the controlled release of the captured BC-CTCs without affecting cell viability could be straightforwardly realized by a simple strand displacement reaction. Thus, with the distinct features of portability, high sensitivity, and easy operation, the current method holds great promise for early diagnosis of breast cancer.

Freeform optical system design with differentiable three-dimensional ray tracing and unsupervised learning.

Optical systems have been crucial for versatile applications such as consumer electronics, remote sensing and biomedical imaging. Designing optical systems has been a highly professional work due to complicated aberration theories and intangible rules-of-thumb, hence neural networks are only coming into this realm until recent years. In this work, we propose and implement a generic, differentiable freeform raytracing module, suitable for off-axis, multiple-surface freeform/aspheric optical systems, paving the way toward a deep learning-based optical design method. The network is trained with minimal prior knowledge, and it can infer numerous optical systems after a one-time training. The presented work unlocks great potential for deep learning in various freeform/aspheric optical systems, and the trained network could serve as an effective, unified platform for generating, recording, and replicating good initial optical designs.

Semantic-Aware Dehazing Network With Adaptive Feature Fusion.

Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration. In addition, we design a densely connected block to capture global and local information for dehazing and semantic prior estimation. To eliminate the unnatural appearance of some objects, we propose to fuse the features from shallow and deep layers adaptively. Experimental results demonstrate that our proposed model performs favorably against the state-of-the-art single image dehazing approaches.

[The role of red blood cells in immune response to infection: A review].

Red blood cells are the largest number of cells in blood. Apart from transporting oxygen and carbon dioxide, they also regulate the immune responses. It has been confirmed that red blood cells can play a role in clearing immune complex, promoting phagocytosis, and presenting antigen through immune adhesion.They can also regulate the immune response by participating in the oxidative stress or interacting with the other lymphocytes. Furthermore, when pathogens invading, the host will exert anti-infective immune response and also induce inflammatory response, causing a series of immunopathological injuries, which may bring changes to the red blood cells' maturation process, their morphology, and functionality. This review summarizes recent progresses on the roles and mechanisms of red blood cells in anti-infectious immunity, providing theoretical basis for revealing pathogenesis and exploring therapeutic targets.

Phase-diversity wavefront sensing enhanced by a Fourier-based neural network.

Phase diversity wavefront sensing (PDWS) has been a successful approach to quantifying wavefront aberrations with only a few intensity measurements and nonlinear optimization. However, the inherent non-convexity of the inverse problem may lead to stagnation at a local minimum far from the true solution. Proper initialization of the nonlinear optimization is important to avoid local minima and improve wavefront retrieval accuracy. In this paper, we propose an effective neural network based on low-frequency coefficients in the Fourier domain to determine a better estimate of the unknown aberrations. By virtue of the proposed network, only a small amount of simulation data suffice for a robust training, two orders of magnitude less than those in existing work. Experimental results show that, when compared with some existing methods, our method achieves the highest accuracy while drastically reducing the training time to 1.4 min. The minimum, maximum, and mean values of the root mean square (RMS) residual errors for 800 aberrations are 0.017λ, 0.056λ, and 0.039λ, respectively, and 95% of the RMS residual errors are less than 0.05λ.

The Emerging Role of Extracellular Vesicles from Mesenchymal Stem Cells and Macrophages in Pulmonary Fibrosis: Insights into miRNA Delivery.

Pulmonary fibrosis is a type of chronic, progressive, fibrotic lung disease of unclear cause with few treatment options. Cell therapy is emerging as a promising novel modality for facilitating lung repair. Mesenchymal stem cell (MSC)-based and macrophage-based cell therapies are regarded as promising strategies to promote lung repair, due to incredible regenerative potential and typical immunomodulatory function, respectively. Extracellular vesicles (EVs), including exosomes and microvesicles, are cell-derived lipid-bilayer membrane vesicles that are secreted from virtually every cell and are involved in intercellular communication by delivering expansive biological cargos to recipients. This review provides a deep insight into the recent research progress concerning the effects of MSC and macrophage-associated EVs on the pathogenesis of pulmonary fibrosis. In addition to discussing their respective vital roles, we summarize the importance of cross-talk, as macrophages are vital for MSCs to exert their protective effects through two major patterns, including attenuating macrophage activation and M1 phenotype macrophage polarization. Moreover, miRNAs are selectively enriched into EVs as essential components, and consideration is given to the particular effects of EV-associated miRNAs.

Different responses of representative denitrifying bacterial strains to gatifloxacin exposure in simulated groundwater denitrification environment.

The impact of antibiotics on denitrification in the ecological environment has attracted widespread attention. However, the concentration threshold and inhibitory effect of the same antibiotic on denitrification mediated by mixed denitrifying microbes were conflicting in some studies. In this study, Paracoccus denitrificans, Acidovorax sp., and Pseudomonas aeruginosa were selected as representative denitrifying bacterial strains to explore the response of a single strain to gatifloxacin (GAT) exposure in groundwater denitrification. The results showed that the nitrate and nitrite removal efficiencies of Pseudomonas aeruginosa decreased by 34.87-36.25 % and 18.27-23.31 %, respectively, with exposure to 10 µg/L GAT, accompanied by a significant decline in denitrifying enzyme activity and gene expression. In contrast, the elevated denitrifying enzyme activity and gene expression of Paracoccus denitrificans promoted its nitrate and nitrite reduction by 2.09-10.00 % and 0-8.44 %, respectively. Additionally, there were no obvious effects on the removal of nitrate and nitrite by Acidovorax sp. in the presence of 10 µg/L GAT, which was consistent with the variation in denitrifying enzyme activity and total gene expression levels. The fit results of the Monod equation and its modification further elucidated the nitrate degradation characteristics from the perspective of denitrification kinetics. Furthermore, antibiotic resistance gene (ARG) analysis showed that the addition of 10 µg/L GAT (approximately 30 days) did not observably increase the relative abundance of ARGs. This study provides some preliminary understanding of the response differences of representative denitrifying bacterial strains to antibiotic exposure in groundwater denitrification.

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.

Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.

Improvement of irradiation-induced fibroblast damage by α2-macroglobulin through alleviating mitochondrial dysfunction.

CONTEXT: α2-Macroglobulin (α2-M) is believed to be a potential anti-irradiation agent, but related mechanisms remains unclear. OBJECTIVE: We investigated the irradiation protective effect of α2-M. MATERIALS AND METHODS: A total of 10 Gy dose of irradiation was used to damage human skin fibroblasts. The influence of α2-M (100 µg/mL) on the proliferation, migration, invasion and apoptosis of fibroblasts was observed using Cell Counting Kit-8 (CCK8), wound healing, transwell, and flow cytometry. Malondialdehyde, superoxide dismutase and catalase was measured using related ELISA kits. The levels of mitochondrial membrane potential and calcium were detected using flow cytometry. The expression of transient receptor potential melastatin 2 (TRPM2) was investigated through western blotting and immunofluorescence staining. RESULTS: High purity of α2-M was isolated from Cohn fraction IV. α2-M significantly increased cell proliferation, migration, invasion, but suppressed cell apoptosis after irradiation. The promotion of cell proliferation, migration and invasion by α2-M exceeded over 50% compared group irradiation. The increased cell ratio in the S phase and decreased cell ratio in the G2 phase induced by irradiation were remarkably reversed by α2-M. α2-M markedly suppressed the increased oxidative stress level caused by irradiation. The mitochondrial damage induced by irradiation was improved by α2-M through inhibiting mitochondrial membrane potential loss, calcium and TRPM2 expression. DISCUSSION AND CONCLUSIONS: α2-M significantly promoted the decreased fibroblast viability and improved the mitochondria dysfunction caused by irradiation. α2-M might present anti-radiation effect through alleviating mitochondrial dysfunction caused by irradiation. This study could provide a novel understanding about the improvement of α2-M on irradiation-induced injury.

Video Super-Resolution via a Spatio-Temporal Alignment Network.

Deep convolutional neural network based video super-resolution (SR) models have achieved significant progress in recent years. Existing deep video SR methods usually impose optical flow to wrap the neighboring frames for temporal alignment. However, accurate estimation of optical flow is quite difficult, which tends to produce artifacts in the super-resolved results. To address this problem, we propose a novel end-to-end deep convolutional network that dynamically generates the spatially adaptive filters for the alignment, which are constituted by the local spatio-temporal channels of each pixel. Our method avoids generating explicit motion compensation and utilizes spatio-temporal adaptive filters to achieve the operation of alignment, which effectively fuses the multi-frame information and improves the temporal consistency of the video. Capitalizing on the proposed adaptive filter, we develop a reconstruction network and take the aligned frames as input to restore the high-resolution frames. In addition, we employ residual modules embedded with channel attention as the basic unit to extract more informative features for video SR. Both quantitative and qualitative evaluation results on three public video datasets demonstrate that the proposed method performs favorably against state-of-the-art super-resolution methods in terms of clearness and texture details.

Hierarchical Density-Aware Dehazing Network.

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. The proposed network incorporates density estimation but alleviates the constraint of the atmospheric model. The predicted haze density then guides the Laplacian pyramid decoder to generate a haze-free image in a coarse-to-fine fashion. In addition, we introduce a multiscale discriminator to preserve global and local consistency for dehazing. We conduct extensive experiments on natural and synthetic hazy images, which prove that the proposed model performs favorably against the state-of-the-art dehazing approaches.

The Presence of Circulating Nucleated Red Blood Cells Is Associated With Disease Severity in Patients of Hemorrhagic Fever With Renal Syndrome.

Hemorrhagic fever with renal syndrome (HFRS) is a regional infectious disease of epidemic potential caused by the Hantaan virus (HTNV). Red blood cells (RBCs) are the major components of peripheral blood. However, pathological changes in RBCs and the underlying mechanisms during HTNV infection remain largely unclear. Therefore, this study sought to explore changes in RBCs in the peripheral blood of HFRS patients. We isolated PBMCs from HFRS patients and performed single-cell RNA sequencing. The results showed that clusters of RBCs in the peripheral blood of HFRS could be classified as nucleated red blood cells (NRBC) based on their cellular components, gene expression profiles and cell surface markers. In addition, it was shown that the higher the count of NRBC in peripheral blood, the more severe the disease status was. Moreover, hematological indices related to RBCs were analyzed and the results showed that impairment in the folate pathway might be the possible reason behind the presence of NRBCs. This study, for the first time showed that the presence of NRBCs in the peripheral blood of HFRS patients was associated with disease severity. This was also the first study to show that infection with the HTNV virus hindered the maturation of RBCs. Therefore, this work provides further insights on the role of and pathological changes in RBCs during HTNV infection.

[Advances in head and neck squamous cell cancer research based on radiomics].

Radiomics refers to extract advanced quantitative features in radiological images in a high-throughput way, to invert the features into extensible data with the help of mathematical algorithms, and to establish descriptive and predictive models of tumors. It has important value in the diagnosis, treatment and prognosis of tumors. As an entirely new field, radiomics becomes the research hotspot of clinical medicine and biomedical engineering because of its objective, holistic, non-invasive characteristics. Head and neck squamous cell cancer is one of the common malignant tumors. Radiomics is gradually applied to the study of head and neck squamous cell cancer. This article reviews the research progress of radiomics and its application in head and neck cancer.