The characterization of technical design of a virus-like structure (VLS) nanodelivery system as vaccine candidate against SARS-CoV-2 variants.

The constant mutation of SARS-CoV-2 has led to the continuous appearance of viral variants and their pandemics and has improved the development of vaccines with a broad spectrum of antigens to curb the spread of the virus. The work described here suggested a novel vaccine with a virus-like structure (VLS) composed of combined mRNA and protein that is capable of stimulating the immune system in a manner similar to that of viral infection. This VLS vaccine is characterized by its ability to specifically target dendritic cells and/or macrophages through S1 protein recognition of the DC-SIGN receptor in cells, which leads to direct mRNA delivery to these innate immune cells for activation of robust immunity with a broad spectrum of neutralizing antibodies and immune protective capacity against variants.

Morphological classification of neurons based on Sugeno fuzzy integration and multi-classifier fusion.

In order to extract more important morphological features of neuron images and achieve accurate classification of the neuron type, a method is proposed that uses Sugeno fuzzy integral integration of three optimized deep learning models, namely AlexNet, VGG11_bn, and ResNet-50. Firstly, using the pre-trained model of AlexNet and the output layer is fine-tuned to improve the model's performance. Secondly, in the VGG11_bn network, Global Average Pooling (GAP) is adopted to replace the traditional fully connected layer to reduce the number of parameters.

Correlation of small nucleolar RNA host gene 16 with acute respiratory distress syndrome occurrence and prognosis in sepsis patients.

Background: Long noncoding RNA small nucleolar RNA host gene 16 (lnc-SNHG16) regulates sepsis-induced acute lung injury and inflammation, which is involved in the pathophysiology of acute respiratory distress syndrome (ARDS). The present study intended to explore the role of lnc-SNHG16 as a potential biomarker indicating ARDS risk, disease severity, inflammation, and mortality in sepsis.

Methods: Peripheral blood mononuclear cell (PBMC) samples were collected from 160 sepsis patients within 24 hours after admission and 30 healthy controls (HCs).

Aberrant blood MALT1 and its relevance with multiple organic dysfunctions, T helper cells, inflammation, and mortality risk of sepsis patients.

Background: MALT1 is linked with multiple organic dysfunctions, inflammatory storm, and T helper (Th) cell differentiation. Herein, the current study aimed to investigate the correlation of peripheral blood mononuclear cell (PBMC) MALT1 with Th1 cells, Th17 cells, and prognosis of sepsis patients.

Methods: In general, 78 sepsis patients and 40 health controls (HCs) were enrolled.

Blood long non-coding RNA intersectin 1-2 is highly expressed and links with increased Th17 cells, inflammation, multiple organ dysfunction, and mortality risk in sepsis patients.

Background: Long non-coding RNA intersectin 1-2 (lnc-ITSN1-2) exacerbates inflammation and promotes T-helper (Th) cell differentiation, also serves as a biomarker in critical illness diseases. However, its clinical role in sepsis remains obscure. Hence, the study aimed to explore the relationship of lnc-ITSN1-2 with Th cells, inflammation, disease severity, multiple organ dysfunction, and mortality risk in sepsis.

A neuron image segmentation method based Deep Boltzmann Machine and CV model.

Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation.

Long noncoding RNA nuclear enriched abundant transcript 1/miRNA-124 axis correlates with increased disease risk, elevated inflammation, deteriorative disease condition, and predicts decreased survival of sepsis.

We aimed to investigate the correlation of long noncoding RNA nuclear enriched abundant transcript 1 (lnc-NEAT1), microRNA-124 (miR-124) and lnc-NEAT1/miR-124 axis with disease risk, severity, inflammatory cytokines, and survival of sepsis.Eighty-two patients with sepsis and 82 healthy controls (HCs) were consecutively enrolled. Blood samples were collected for detection of lnc-NEAT1 and miR-124 expressions (using RT-qPCR) and measurement of inflammatory cytokines expressions (by ELISA).

Circulating lncRNA NEAT1 correlates with increased risk, elevated severity and unfavorable prognosis in sepsis patients.

Objective: To investigate the correlation of circulating long non-coding RNA nuclear-enriched abundant transcript 1 (lncRNA NEAT1) expression with disease risk, severity, prognosis and inflammatory cytokine levels in sepsis patients.

Methods: 152 sepsis patients and 150 health controls (HCs) were enrolled in this study. Plasma and serum samples were obtained from sepsis patients and HCs, and lncRNA NEAT1 expression in plasma was determined by quantitative polymerase chain reaction, while levels of inflammatory cytokines in serum were detected by enzyme linked immune sorbent assay.

An ultrasensitive microchip electrophoresis assay based on separation-assisted double cycling signal amplification strategy for microRNA detection in cell lysate.

Microchip electrophoresis (MCE) assay is an analysis technique with low consumption and high automation. It is a useful tool in biomedical research and clinical diagnosis. However, the low detection sensitivity limits its application in trace biomarker analysis because of its extremely small sample size.