Sepsis is characterized as an uncontrolled host response to infection, and it represents a serious health challenge, causing excess mortality and morbidity worldwide. The discovery of sepsis-related epigenetic and molecular mechanisms could result in improved diagnostic and therapeutic approaches, leading to a reduced overall risk for affected patients. Accumulating data show that microRNAs, non-coding RNAs, and exosomes could all be considered as novel diagnostic markers for sepsis patients. These biomarkers have been demonstrated to be involved in regulation of sepsis pathophysiology. However, epigenetic modifications have not yet been widely reported in actual clinical settings, and further investigation is required to determine their importance in intensive care patients. Further studies should be carried out to explore tissue-specific or organ-specific epigenetic RNA-based biomarkers and their therapeutic potential in sepsis patients.

Non-coding RNAs and Exosomes: Their Role in the Pathogenesis of Sepsis.

Sepsis is a dysregulated systemic reaction to a common infection, that can cause life-threatening organ dysfunction. Over the last decade, the mortality rate of patients with sepsis has decreased as long as patients are treated according to the recommendations of the Surviving Sepsis Campaign, but is still unacceptably high. Patients at risk of sepsis should therefore be identified prior to the onset of organ dysfunction and this requires a rapid diagnosis and a prompt initiation of treatment. Unfortunately, there is no gold standard for the diagnosis of sepsis and traditional standard culture methods are time-consuming. Recently, in order to overcome these limitations, biomarkers which could help in predicting the diagnosis and prognosis of sepsis, as well as being useful for monitoring the response to treatments, have been identified. In addition, recent advances have led to the development of newly identified classes of biomarkers such as microRNAs, long-non-coding RNAs, and the human microbiome. This review focuses on the latest information on biomarkers that can be used to predict the diagnosis and prognosis of sepsis.

/pdf/an-update-on-sepsis-biomarkers-32q5nkbjxn.pdf

An Update on Sepsis Biomarkers

Objectives HIV infection is well known to cause impairment of the human immune system, and until recently was a leading cause of death. It has been shown that T lymphocytes are the main targets of HIV. The virus inactivates T lymphocytes by interfering with a wide range of cellular and molecular targets, leading to suppression of the immune system. The objective of this review is to investigate to what extent microRNAs (miRNAs) are involved in HIV pathogenesis. Methods The scientific literature (Pubmed and Google scholar) for the period 1988-2019 was searched. Results Mounting evidence has revealed that miRNAs are involved in viral replication and immune response, whether by direct targeting of viral transcripts or through indirect modulation of virus-related host pathways. In addition, exosomes have been found to act as nanoscale carriers involved in HIV pathogenesis. These nanovehicles target their cargos (i.e. DNA, RNA, viral proteins and miRNAs) leading to alteration of the behaviour of recipient cells. Conclusions miRNAs and exosomes are important players in HIV pathogenesis. Additionally, there are potential diagnostic applications of miRNAs as biomarkers in HIV infection.

MicroRNAs and exosomes: key players in HIV pathogenesis

Sepsis can cause sepsis-associated encephalopathy (SAE), but whether SAE was induced or exacerbated by ferroptosis remains unknown. In this study, the rat sepsis model was constructed using the cecal ligation and puncture method. The blood-brain barrier (BBB) permeability was measured by Evans blue dye (EBD) in vivo. The levels of ROS, Fe ion, MDA, GSH, and GPX4 were assessed by enzyme-linked immunosorbent assay (ELISA). The exosomes isolated from serum were cultured with bEnd.3 cells for the in vitro analysis. Moreover, bEnd.3 cells cultured with 100 μM FeCl3 (iron-rich) were to simulate ferroptosis stress. The cell viability was evaluated by Cell Counting Kit-8 (CCK-8) assay. A dual-luciferase reporter gene assay was performed to confirm the relationship between miR-9-5p with NEAT1, TFRC, and GOT1. In vivo, it is found that BBB permeability was damaged in model rats. Level of ROS, Fe ion, and MDA was increased, and level of GSH and GPX4 was decreased, which means ferroptosis was induced by sepsis. Exosome-packaged NEAT1 in serum was significantly upregulated in model rats. In vitro, it is found that NEAT1 functions as a ceRNA for miR-9-5p to facilitate TFRC and GOT1 expression. Overexpression of NEAT1 enhanced ferroptosis stress in bEnd.3 cells. Increased miR-9-5p alleviated sepsis-induced ferroptosis by suppressing the expression of TFRC and GOT1 both in vivo and in vitro. In conclusion, these findings suggest that sepsis induced high expression of serous exosome-derived NEAT1, and it might exacerbate SAE by promoting ferroptosis through regulating miR-9-5p/TFRC and GOT1 axis. 

/pdf/exosome-derived-lncrna-neat1-exacerbates-sepsis-associated-2i9p3bte.pdf

Exosome-Derived lncRNA NEAT1 Exacerbates Sepsis-Associated Encephalopathy by Promoting Ferroptosis Through Regulating miR-9-5p/TFRC and GOT1 Axis

Nuclear paraspeckle assembly transcript 1 (Neat1) located at chromosome 11 is a long non-coding RNA that is widely expressed in mammalian cell types, and which is overexpressed in several inflammation-related disorders. Inflammation implies a plethora of mutual interactions between both soluble factors and cells due to various stimuli including tissue injury. Although there is no doubt that inflammation is critically involved in multiple biological and pathological processes alike, the precise mechanisms being involved are still open for debate. In this context, the role of Neat1 as a regulator of inflammation, microglial activation, and lipid accumulation under various inflammatory conditions remains elusive. Herein, we review the regulation of Neat1 and how it modulates the expression of its target genes. Thereafter, we will review the impact of Neat1 on inflammation by activating or inhibiting various signaling pathways, such as microRNAs, AKT, TLR4, TRAF6, and NF-κB. 

Novel Insights into the Emerging Role of Neat1 and Its Effects Downstream in the Regulation of Inflammation

Quantization approximates a deep network model with floating-point numbers by the one with low bit width numbers, in order to accelerate inference and reduce computation. Quantizing a model without access to the original data, zero-shot quantization can be accomplished by fitting the real data distribution by data synthesis. However, zero-shot quantization achieves inferior performance compared to the post-training quantization with real data. We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness. To overcome these problems, we propose a novel deep network quantizer, dubbed Long-Range Zero-Shot Generative Deep Network Quantization (LRQ). Technically, we propose a long-range generator to learn long-range information instead of simple local features. In order for the synthetic data to contain more global features, long-range attention using large kernel convolution is incorporated into the generator. In addition, we also present an Adversarial Margin Add (AMA) module to force intra-class angular enlargement between feature vector and class center. As AMA increases the convergence difficulty of the loss function, which is opposite to the training objective of the original loss function, it forms an adversarial process. Furthermore, in order to transfer knowledge from the full-precision network, we also utilize a decoupled knowledge distillation. Extensive experiments demonstrate that LRQ obtains better performance than other competitors.

pdf/long-range-zero-shot-generative-deep-network-quantization-2tdylt35.pdf

Long-Range Zero-Shot Generative Deep Network Quantization

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  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.

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

Speech Emotion Recognition (SER) has become a growing focus of research in human-computer interaction. An essential challenge in SER is to extract common attributes from different speakers or languages, especially when a specific source corpus has to be trained to recognize the unknown data coming from another speech corpus. To address this challenge, a Capsule Network (CapsNet) and Transfer Learning based Mixed Task Net (CTL-MTNet) are proposed to deal with both the single-corpus and cross-corpus SER tasks simultaneously in this paper. For the single-corpus task, the combination of Convolution-Pooling and Attention CapsNet module (CPAC) is designed by embedding the self-attention mechanism to the CapsNet, guiding the module to focus on the important features that can be fed into different capsules. The extracted high-level features by CPAC provide sufficient discriminative ability. Furthermore, to handle the cross-corpus task, CTL-MTNet employs a Corpus Adaptation Adversarial Module (CAAM) by combining CPAC with Margin Disparity Discrepancy (MDD), which can learn the domain-invariant emotion representations through extracting the strong emotion commonness. Experiments including ablation studies and visualizations on both single- and cross-corpus tasks using four well-known SER datasets in different languages are conducted for performance evaluation and comparison. The results indicate that in both tasks the CTL-MTNet showed better performance in all cases compared to a number of state-of-the-art methods. The source code and the supplementary materials are available at: https://github.com/MLDMXM2017/CTLMTNet.

/pdf/ctl-mtnet-a-novel-capsnet-and-transfer-learning-based-mixed-1j86qf2k.pdf

CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition

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Long-Range Zero-Shot Generative Deep Network Quantization

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

CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition