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. Additionally, the generalization ability of the model is improved by transfer learning. Thirdly, the SE(squeeze and excitation) module is added to the ResNet-50 variant ResNeXt-50 to adjust the channel weight and capture the key information of the input data. The GELU activation function is used to better fit the data distribution. Finally, Sugeno fuzzy integral is used to fuse the output of each model to get the final classification result. The experimental results showed that on the Img_raw, Img_resample and Img_XYalign dataset, the accuracy of 4-category classification reached 98.04%, 91.75% and 93.13%, respectively, and the accuracy of 12-category classification reached 97.82%, 85.68% and 87.60%, respectively. The proposed method has good classification performance in the morphological classification of neurons.

Virus-like structures for combination antigen protein mRNA vaccination.

Improved vaccination requires better delivery of antigens and activation of the natural immune response. Here we report a lipid nanoparticle system with the capacity to carry antigens, including mRNA and proteins, which is formed into a virus-like structure by surface decoration with spike proteins, demonstrating application against SARS-CoV-2 variants. The strategy uses S1 protein from Omicron BA.1 on the surface to deliver mRNA of S1 protein from XBB.1. The virus-like particle enables specific augmentation of mRNAs expressed in human respiratory epithelial cells and macrophages via the interaction the surface S1 protein with ACE2 or DC-SIGN receptors. Activation of macrophages and dendritic cells is demonstrated by the same receptor binding. The combination of protein and mRNA increases the antibody response in BALB/c mice compared with mRNA and protein vaccines alone. Our exploration of the mechanism of this robust immunity suggests it might involve cross-presentation to diverse subsets of dendritic cells ranging from activated innate immune signals to adaptive immune signals.

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). Then, lnc-SNHG16 in PBMCs was detected by reverse transcription-quantitative polymerase chain reaction. Sepsis patients were followed up until death or up to 28 days. RESULTS: lnc-SNHG16 was declined in sepsis patients compared with HCs (p < 0.001). The incidence of ARDS was 27.5% among sepsis patients; meanwhile, sepsis patients with ARDS had higher mortality than those without ARDS (p < 0.001). Furthermore, lnc-SNHG16 was declined in sepsis patients with ARDS compared to those without ARDS (p < 0.001); besides, higher lnc-SNHG16 was independently correlated with declined ARDS occurrence in sepsis patients (p = 0.001), while primary respiratory infection and higher CRP were independently correlated with elevated ARDS occurrence in sepsis patients (both p < 0.05). Moreover, a negative correlation was found in lnc-SNHG16 with history of diabetes, history of chronic obstructive pulmonary disease, and APACHE II and SOFA scores (all p < 0.05). Additionally, lnc-SNHG16 was declined in sepsis deaths compared with survivors (p = 0.002), while it was not independently linked with sepsis mortality. CONCLUSION: lnc-SNHG16 correlates with lower ARDS occurrence and better prognosis in sepsis patients.

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. MALT1 expression was detected in PBMCs from all subjects by RT-qPCR. Besides, Th1 and Th17 cells were measured in PBMCs from sepsis patients by flow cytometry; interleukin 17A (IL-17A) and interferon gamma (IFN-γ) were determined in serum from sepsis patients by ELISA. RESULTS: MALT1 expression was higher in sepsis patients than HCs (p < 0.001). MALT1 expression was positively correlated with Th17 cells (rs  = 0.291, p = 0.038) and IL-17A (rs  = 0.383, p = 0.001), but not with Th1 cells (rs  = 0.204, p = 0.151) or IFN-γ (rs  = 0.175, p = 0.125) in sepsis patients. MALT1 expression was positively correlated with APACHE II score (rs  = 0.275, p = 0.015), C-reactive protein (CRP) (rs  = 0.257, p = 0.023), and sequential organ failure assessment (SOFA) score (rs  = 0.306, p = 0.006) (MALT1 expression was positively correlated with SOFA respiratory system score (rs  = 0.348, p = 0.002), and SOFA liver score (rs  = 0.260, p = 0.021), but not with SOFA scores in nervous system, cardio vascular system, coagulation, and renal system (all p > 0.05)). MALT1 expression (p = 0.010), Th1 cells (p = 0.010), Th17 cells (p = 0.038), and IL-17A (p = 0.012), except for IFN-γ (p = 0.102), elevated in sepsis deaths compared with sepsis survivors. CONCLUSION: PBMC MALT1 is highly expressed in sepsis patients with its overexpression associated with multiple organic dysfunctions, elevated Th17 cells, and increased mortality risk.

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. METHODS: Peripheral blood mononuclear cells (PBMC) were isolated from 95 sepsis patients and 50 health controls, followed by lnc-ITSN1-2 evaluation using RT-qPCR. PBMC Th1, Th17 cells and their secreted cytokines in serum were detected by flow cytometry and ELISA, respectively. RESULTS: Lnc-ITSN1-2 in sepsis patients was higher than it in health controls (Z = -7.328, p < 0.001). Lnc-ITSN1-2 correlated with increased interferon-gamma (p = 0.009), Th17 cells (p = 0.022), and interleukin-17A (p = 0.006), but not Th1 cells (p = 0.169) in sepsis patients. Moreover, lnc-ITSN1-2 had a positive connection with C-reactive protein (p = 0.001), acute pathologic and chronic health evaluation (APACHE) II (p = 0.024), and sequential organ failure assessment (SOFA) scores (p = 0.022). Regarding SOFA subscales, lnc-ITSN1-2 linked with elevated respiratory system score (p = 0.005), cardiovascular system score (p = 0.007), and renal system score (p = 0.004) but no other subscales. Besides, lnc-ITSN1-2 had an increasing trend, but no statistical difference, in septic deaths compared to survivors (Z = -1.852, p = 0.064). CONCLUSION: Lnc-ITSN1-2 reflects sepsis progression and unfavorable prognosis to some extent, which may serve as a potential biomarker to improve the management of sepsis patients.

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. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.

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). Severity and organ failure were assessed by acute physiology and chronic health evaluation II (APACHE II) score and sequential organ failure assessment (SOFA) score, and survival was assessed.Lnc-NEAT1 expression was increased while miR-124 expression was decreased in patients with sepsis compared to HCs, and both of them were able to distinguish patients with sepsis from HCs. For disease condition, lnc-NEAT1 positively associated with APACHE II score, SOFA score, and expressions of C-reactive protein (CRP), procalcitonin, tumor necrosis factor α (TNF-α), and interleukin-1ß (IL-1ß), whereas miR-124 negatively correlated with APACHE II score, SOFA score and levels of serum creatinine (Scr), CRP, TNF-α, IL-1ß, interleukin-6 (IL-6) and interleukin-17 (IL-17). Regarding prognosis, lnc-NEAT1 was upregulated but miR-124 was downregulated in nonsurvivors compared to survivors. Additionally, lnc-NEAT1 negatively correlated with miR-124. Besides, lnc-NEAT1/miR-124 axis was increased in patients with sepsis compared to HCs, and positively associated with APACHE II score, SOFA score, and levels of Scr, CRP, TNF-α, IL-1ß, IL-6, and IL-17, while negatively correlated with survival. Most importantly, lnc-NEAT1/miR-124 axis presented numerically increased predictive value for sepsis risk and survival compared to each index alone.Lnc-NEAT1/miR-124 axis correlates with increased sepsis risk, and associates with higher inflammation, deteriorative disease condition, and decreased survival in patients with sepsis.

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. RESULTS: LncRNA NEAT1 expression was remarkably higher in sepsis patients than in HCs (P < 0.001). Receiver operating characteristic (ROC) curve disclosed a good predictive value of lncRNA NEAT1 expression for sepsis risk with area under curve (AUC) of 0.730 (95% CI: 0.740-0.861). Subsequent multivariate logistic regression analysis demonstrated that lncRNA NEAT1 expression was independently associated with higher sepsis risk (P < 0.001). In sepsis patients, lncRNA NEAT1 expression was also observed to be positively correlated with Acute Physiology and Chronic Health Evaluation (APACHE) II score (P < 0.001), serum tumor necrosis factor-α (P < 0.001), interleukin (IL)-1ß (P = 0.043), IL-6 (P = 0.001) and IL-8 (P = 0.038), while negatively correlated with IL-10 (P < 0.001). In addition, lncRNA NEAT1 expression was increased in non-survivors compared to survivors (P = 0.006), and ROC curve revealed a good prognostic value of lncRNA NEAT1 for non-survivor risk with AUC 0.641 (95% CI: 0.536-0.746). CONCLUSION: Circulating lncRNA NEAT1 correlates with increased disease risk, elevated severity and unfavorable prognosis as well as higher expression of pro-inflammatory cytokines in sepsis patients.

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. To address the need for high sensitivity in MCE, we have developed an ultrasensitive MCE method based on a separation-assisted double cycling signal amplification strategy for the detection of microRNA (miRNA) in cell lysate. In this method, two short single-stranded DNAs P1 and P2 complement each other to form a duplex DNA probe (P1/P2). In the presence of target miRNA, P2 in the P1/P2 probe can be displaced to form double-stranded miRNA/P1. Then, the degradation of P1 in miRNA/P1 by T7 Exo releases the miRNA, and the released miRNA participates in a displacement reaction with another P1/P2 probe to complete the first cycle. The displaced free P2 hybridizes with the hairpin fluorescence probe (MB) to form the P2/MB duplex, which can also be degraded by T7 Exo to release P2. The released P2 can bind with another MB probe to complete the second cycle. By using MCE-laser-induced fluorescence (LIF) as separation and detection platform and miRNA-141 as model analyte, the proposed MCE assay can detect miRNA-141 at concentrations as low as 8.0 fM, which is the highest sensitivity achieved to date for an MCE assay. This method for detecting trace miRNA holds great potential in biomedical research and clinical diagnosis.