Role of NLRP3 inflammasome in central nervous system diseases.

The central nervous system (CNS) is the most delicate system in human body, with the most complex structure and function. It is vulnerable to trauma, infection, neurodegeneration and autoimmune diseases, and activates the immune system. An appropriate inflammatory response contributes to defence against invading microbes, whereas an excessive inflammatory response can aggravate tissue damage. The NLRP3 inflammasome was the first one studied in the brain. Once primed and activated, it completes the assembly of inflammasome (sensor NLRP3, adaptor ASC, and effector caspase-1), leading to caspase-1 activation and increased release of downstream inflammatory cytokines, as well as to pyroptosis. Cumulative studies have confirmed that NLRP3 plays an important role in regulating innate immunity and autoimmune diseases, and its inhibitors have shown good efficacy in animal models of various inflammatory diseases. In this review, we will briefly discuss the biological characteristics of NLRP3 inflammasome, summarize the recent advances and clinical impact of the NLRP3 inflammasome in infectious, inflammatory, immune, degenerative, genetic, and vascular diseases of CNS, and discuss the potential and challenges of NLRP3 as a therapeutic target for CNS diseases.

Microbial contribution to 14 biogenic amines accumulation in refrigerated raw and deep-fried hairtails (Trichiurus lepturus).

Biogenic amines (BAs) produced by microbial decarboxylation of amino acids are crucial toxic nitrogenous compounds in fish. An optimized ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method with simple pretreatment was established to detect 14 BAs in both raw (control check, CK) and deep-fried (DF) hairtails. This method exhibited a good linear relationship with average recoveries of 73.3-120.0 % and relative standard deviations of 2.5-10.0 %, respectively. The total BAs in CK and DF hairtails decreased sharply to 338.2 and 25.3 mg/kg on the 9th day, respectively. Four BAs, including cadaverine (CAD), histamine (HIS), tyramine (TYR), and putrescine (PUT) accounted for 92.5-99.9 % of total BAs were selected as the dominant BAs. Bacterial analysis showed that the abundance of DF was relatively low. Further correlation analysis proved that Vibrio had a significant (p < 0.05) positive correlation with total BAs and could be the main BA-producing bacterium in DF hairtail. This work provides new evidence of the accumulation of BAs in refrigerated hairtail.

Inpainting with Separable Mask Update Convolution Network.

Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are many existing methods that can produce high-quality restoration results, they often struggle when dealing with images that have large missing areas, resulting in blurry and artifact-filled outcomes. This is primarily because of the presence of invalid information in the inpainting region, which interferes with the inpainting process. To tackle this challenge, the paper proposes a novel approach called separable mask update convolution. This technique automatically learns and updates the mask, which represents the missing area, to better control the influence of invalid information within the mask area on the restoration results. Furthermore, this convolution method reduces the number of network parameters and the size of the model. The paper also introduces a regional normalization technique that collaborates with separable mask update convolution layers for improved feature extraction, thereby enhancing the quality of the restored image. Experimental results demonstrate that the proposed method performs well in restoring images with large missing areas and outperforms state-of-the-art image inpainting methods significantly in terms of image quality.

Therapeutic potential of MSCs and MSC-derived extracellular vesicles in immune thrombocytopenia.

Immune thrombocytopenia (ITP) is an acquired autoimmune disease involving a variety of immune cells and factors. Despite being a benign disease, it is still considered incurable due to its complex pathogenesis. Mesenchymal stem cells (MSCs), with low immunogenicity, pluripotent differentiation, and immunomodulatory ability, are widely used in a variety of autoimmune diseases. In recent years, impaired bone marrow mesenchymal stem cells (BMMSCs) were found to play an important role in the pathogenesis of ITP; and the therapeutic role of MSCs in ITP has also been supported by increasing evidence with encouraging efficacy. MSCs hold promise as a new approach to treat or even cure refractory ITP. Extracellular vesicles (EVs), as novel carriers in the "paracrine" mechanism of MSCs, are the focus of MSCs. Encouragingly, several studies suggested that EVs may perform similar functions as MSCs to treat ITP. This review summarized the role of MSCs in the pathophysiology and treatment of ITP.

MSC-EVs transferring mitochondria and related components: A new hope for the treatment of kidney disease.

Kidney disease is a serious hazard to human health. Acute or chronic renal disease will have a significant negative impact on the body's metabolism. The involvement of mitochondria in renal illness has received a lot of interest as research on kidney disease has advanced. Extracellular vesicles are gaining popularity as a means of intercellular communication in recent years. They have a close connection to both the nephropathy process and the intercellular transfer of mitochondria. The goal of this review is to present the extracellular vesicle transport mitochondria and its related biologically active molecules as new therapeutic options for the treatment of clinical kidney disease. This review focuses on the extracellular vesicles through the transfer of mitochondria and its related bioactive molecules, which affect mitochondrial energy metabolism, take part in immune regulation, and secrete outside the body.

Oral Realgar-Indigo Naturalis Formula Treatment for Acute Promyelocytic Leukemia in Children: A Randomized, Control Clinical Trial.

Objective: To analyze the efficacy, safety, and economy of RIF compared with intravenous arsenic trioxide (ATO) for the induction and consolidation therapy of pediatric APL. Materials and Methods: In this randomized control clinical trial (NCT02200978), children with newly diagnosed APL from June 2013 to December 2017 were randomly divided into RIF and ATO groups. The groups were treated with RIF or ATO in combination with all-trans retinoic acid (ARTA) and conventional chemotherapeutic drugs during induction and consolidation therapy. Results: Ninteen patients were enrolled, including eight in the RIF group and 11 in the ATO group. After induction therapy, the bone marrow morphologic complete remission (CR) rate, the median time to CR, and molecular remission (promyelocytic leukemia protein (PML)/retinoic acid receptor α (RARα) conversion) rates showed no significant differences between patients in the RIF versus ATO groups (100% vs. 100%, p=1.000; 22 vs. 24 days, p=0.395; 28.5% vs. 54.5%, p=0.367, resp.). After consolidation therapy, the molecular remission rate was 100% in both groups. At the end of more than two years of follow-up, the disease-free survival (DFS) rate was 100% in both groups. Conclusion: Oral RIF can achieve similar efficacy to intravenous ATO for APL in children with good safety, less toxicity, fewer side effects, and fewer inpatient days. Therefore, oral RIF can be used as an alternative to intravenous ATO for the treatment of APL in children.

Human umbilical cord mesenchymal stem cells derived extracellular vesicles regulate acquired immune response of lupus mouse in vitro.

Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple systems. Immunopathology believes that abnormal T cell function and excessive production of autoantibodies by B cells are involved in multi-organ damage. Human umbilical cord mesenchymal stem cells (hUCMSCs) therapies have endowed with promise in SLE, while the function of MSC-derived extracellular vesicles (MSC-EVs) was still unclear. Extracellular vesicles (EVs) are subcellular components secreted by a paracellular mechanism and are essentially a group of nanoparticles. EVs play a vital role in cell-to-cell communication by acting as biological transporters. New evidence has shown beneficial effects of MSC-EVs on autoimmune diseases, such as their immunomodulatory properties. In this study, we investigated whether hUCMSCs derived extracellular vesicles (hUCMSC-EVs) could regulate abnormal immune responses of T cells or B cells in SLE. We isolated splenic mononuclear cells from MRL/lpr mice, a classical animal model of SLE. PBS (Phosphate-buffered saline), 2 × 105 hUCMSCs, 25 µg/ml hUCMSC-EVs, 50 µg/ml hUCMSC-EVs were co-cultured with 2 × 106 activated splenic mononuclear cells for 3 days in vitro, respectively. The proportions of CD4+ T cell subsets, B cells and the concentrations of cytokines were detected. Both hUCMSCs and hUCMSC-EVs inhibited CD4+ T cells, increased the production of T helper (Th)17 cells, promoted the production of interleukin (IL)-17 and transforming growth factor beta1 (TGF-ß1) (P < 0.05), although they had no significant effects on Th1, Th2, T follicular helper (Tfh), regulatory T (Treg) cells and IL-10 (P > 0.05); only hUCMSCs inhibited CD19+ B cells, promoted the production of interferon-gamma (IFN-γ) and IL-4 (P < 0.05). hUCMSCs exert immunoregulatory effects on SLE at least partially through hUCMSC-EVs in vitro, therefore, hUCMSC-EVs play novel and potential regulator roles in SLE.

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes.

Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method.

The Role of B1 Cells in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by multisystemic and multi-organ involvement, recurrent relapses and remissions, and the presence of large amounts of autoantibodies in the body as the main clinical features. The mechanisms involved in this disease are complex and remain poorly understood; however, they are generally believed to be related to genetic susceptibility factors, external stimulation of the body's immune dysfunction, and impaired immune regulation. The main immune disorders include the imbalance of T lymphocyte subsets, hyperfunction of B cells, production of large amounts of autoantibodies, and further deposition of immune complexes, which result in tissue damage. Among these, B cells play a major role as antibody-producing cells and have been studied extensively. B1 cells are a group of important innate-like immune cells, which participate in various innate and autoimmune processes. Yet the role of B1 cells in SLE remains unclear. In this review, we focus on the mechanism of B1 cells in SLE to provide new directions to explore the pathogenesis and treatment modalities of SLE.

Management and Clinical Outcome of Posterior Reversible Encephalopathy Syndrome in Pediatric Oncologic/Hematologic Diseases: A PRES Subgroup Analysis With a Large Sample Size.

This study investigated the management and clinical outcomes along with associated factors of posterior reversible encephalopathy syndrome (PRES) in childhood hematologic/oncologic diseases. We present data from children with hematologic/oncologic diseases who developed PRES after treatment of the primary disease with chemotherapy and hematopoietic stem cell transplantation (HSCT) at 3 medical centers in Changsha, China from 2015 to 2020, and review all previously reported cases with the aim of determining whether this neurologic manifestation affects the disease prognosis. In the clinical cohort of 58 PRES patients, hypertension [pooled odds ratio (OR) = 4.941, 95% confidence interval (CI): 1.390, 17.570; P = 0.001] and blood transfusion (OR = 14.259, 95% CI: 3.273, 62.131; P = 0.001) were significantly associated with PRES. Elevated platelet (OR = 0.988, 95% CI: 0.982, 0.995; P < 0.001), hemoglobin (OR = 0.924, 95% CI: 0.890, 0.995; P < 0.001), and blood sodium (OR = 0.905, 95% CI: 0.860, 0.953; P < 0.001), potassium (OR = 0.599, 95% CI: 0.360, 0.995; P = 0.048), and magnesium (OR = 0.093, 95% CI: 0.016, 0.539; P = 0.008) were protective factors against PRES. Data for 440 pediatric PRES patients with hematologic/oncologic diseases in 21 articles retrieved from PubMed, Web of Science, and Embase databases and the 20 PRES patients from our study were analyzed. The median age at presentation was 7.9 years. The most common primary diagnosis was leukemia (62.3%), followed by solid tumor (7.7%) and lymphoma (7.5%). Most patients (65.0%) received chemotherapy, including non-induction (55.2%) and induction (44.8%) regimens; and 86.5% used corticosteroids before the onset of PRES. Although 21.0% of patients died during follow-up, in most cases (93.2%) this was not attributable to PRES but to severe infection (27.3%), underlying disease (26.1%), graft-vs.-host disease (14.8%), multiple organ dysfunction syndrome (8.0%), and respiratory failure (3.4%). PRES was more common with HSCT compared to chemotherapy and had a nearly 2 times higher mortality rate in patients with oncologic/hematologic diseases than in those with other types of disease. Monitoring neurologic signs and symptoms in the former group is therefore critical for ensuring good clinical outcomes following treatment of the primary malignancy.

Numerical simulation study on the crack propagation of conglomerate.

The conglomerate reservoir is rich in oil and gas reserves; however, the gravel's mechanical properties and laws are difficult to gain through laboratory experiments, which furthermore constrain the hydraulic fracturing design. To analyse the failure law of conglomerate, we simulated the uniaxial compression test based on discrete element software PFC2D and analysed the effect of different cementation strength, gravel content and gravel geometry on the rock deformation and failure characteristics. Results show that (i) as the cementation strength decreases, the compressive strength and elasticity modulus both reduce clearly, while the crack shapes get more complex and the critical value is 0.3; (ii) as the gravel content increases, the conglomerate strength first decreases then increases under the influences of cracks bypassing gravels; cementation strength and gravel content of the conglomerate both contribute to the increase in local additional stress, which leads to a series of changes in crack shapes and mechanical properties of the conglomerate. Based on the above research, the conglomerate strength and crack shapes after failure are relatively complex due to the common influence of cementation strength and gravel content. The gravel edge crack caused by stress concentration is the micro-mechanism that affects the conglomerate mechanical properties.

Natural Killer Cell-Derived Extracellular Vesicles: Novel Players in Cancer Immunotherapy.

Natural killer (NK) cells are critical components of host innate immunity and function as the first line of defense against tumors and viral infection. There is increasing evidence that extracellular vesicles (EVs) are involved in the antitumor activity of NK cells. NK cell-derived EVs (NKEVs) carrying cargo such as cytotoxic proteins, microRNAs, and cytokines employ multiple mechanisms to kill tumor cells, but also exhibit immunomodulatory activity by stimulating other immune cells. Several studies have reported that NKEVs can reverse immune suppression under tolerogenic conditions and contribute to NK-mediated immune surveillance against tumors. Thus, NKEVs are a promising tool for cancer immunotherapy. In this review, we describe the biological effects and potential applications of NKEVs in antitumor immunity.

Self-selective attention using correlation between instances for distant supervision relation extraction.

Distant supervision relation extraction methods are widely used to extract relational facts in text. The traditional selective attention model regards instances in the bag as independent of each other, which makes insufficient use of correlation information between instances and supervision information of all correctly labeled instances, affecting the performance of relation extractor. Aiming at this problem, a distant supervision relation extraction method with self-selective attention is proposed. The method uses a layer of convolution and self-attention mechanism to encode instances to learn the better semantic vector representation of instances. The correlation between instances in the bag is used to assign a higher weight to all correctly labeled instances, and the weighted summation of instances in the bag is used to obtain a bag vector representation. Experiments on the NYT dataset show that the method can make full use of the information of all correctly labeled instances in the bag. The method can achieve better results as compared with baselines.

Deep supervised learning with mixture of neural networks.

Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single model to perform well on the classification tasks. In this paper, we propose mixture model based on DNNs (MoNNs), a supervised approach to perform classification tasks with a gating network and multiple local expert models. We use a neural network as a gating function and use DNNs as local expert models. The gating network split the heterogeneous data into several homogeneous components. DNNs are combined to perform classification tasks in each component. Moreover, we use EM (Expectation Maximization) as an optimization algorithm. Experiments proved that our MoNNs outperformed the other compared methods on determination of diabetes, determination of benign or malignant breast cancer, and handwriting recognition. Therefore, the MoNNs can solve the problem of data heterogeneity and have a good effect on classification tasks.

Immunoregulatory Effects of Mitochondria Transferred by Extracellular Vesicles.

Mitochondria participate in immune regulation through various mechanisms, such as changes in the mitochondrial dynamics, as metabolic mediators of the tricarboxylic acid cycle, by the production of reactive oxygen species, and mitochondrial DNA damage, among others. In recent years, studies have shown that extracellular vesicles are widely involved in intercellular communication and exert important effects on immune regulation. Recently, the immunoregulatory effects of mitochondria from extracellular vesicles have gained increasing attention. In this article, we review the mechanisms by which mitochondria participate in immune regulation and exert immunoregulatory effects upon delivery by extracellular vesicles. We also focus on the influence of the immunoregulatory effects of mitochondria from extracellular vesicles to further shed light on the underlying mechanisms.

Characteristics of decline in cognition and locomotion among the elderly in seven provinces of China.

BACKGROUND: Decline in cognition and in locomotion is associated with aging. However, the relationship between them and the current occurrence of them in Chinese elderly people was weak. METHODS: To investigate the details of these two functions in Chinese elderly people and to try to find some early recognition and intervention clues, data of MMSE test and usual gait speed from 4487 elderly people from seven provinces in China were analysed. RESULTS: The prevalence of mild cognitive impairment (MCI) and dementia in persons aged 60 and over was 17.83% and 4.08%, respectively. Among 11 items of MMSE, calculation, three-word recall, drawing two pentagons, and temporal orientation were the most commonly impaired items in persons with MCI or dementia. The gait speed of old persons with dementia was significantly slower than that of persons with MCI or NCI. Meanwhile, old persons with gait speed >1.39 m/s fast gait speed also had high MMSE scores and no dementia was detected by MMSE. CONCLUSION: The prevalence of dementia observed in this population was similar to that reported 20 years ago. Loss of temporal orientation and drawing two pentagons may supply more information for early recognition of cognitive impairment. Maintaining locomotion in a proper way may help old persons to prevent cognitive function decline.

[Effect of corticosterone on lissencephaly 1 expression in developing cerebral cortical neurons of fetal rats cultured in vitro].

OBJECTIVE: To investigate the effect of corticosterone on the expression of the neuronal migration protein lissencephaly 1 (LIS1) in developing cerebral cortical neurons of fetal rats. METHODS: The primary cultured cerebral cortical neurons of fetal Wistar rats were divided into control group, low-dose group, and high-dose group. The neurons were exposed to the medium containing different concentrations of corticosterone (0 µmol/L for the control group, 0.1 µmol/L for the low-dose group, and 1.0 µmol/L for the high-dose group). The neurons were collected at 1, 4, and 7 days after intervention. Western blot and immunocytochemical staining were used to observe the change in LIS1 expression in neurons. RESULTS: Western blot showed that at 7 days after intervention, the low- and high-dose groups had significantly higher expression of LIS1 in the cytoplasm and nucleus of cerebral cortical neurons than the control group (P<0.05), and the high-dose group had significantly lower expression of LIS1 in the cytoplasm of cerebral cortical neurons than the low-dose group (P<0.05). Immunocytochemical staining showed that at 1, 4, and 7 days after corticosterone intervention, the high-dose group had a significantly lower mean optical density of LIS1 than the control group and the low-dose group (P<0.05). At 7 days after intervention, the low-dose group had a significantly lower mean optical density of LIS1 than the control group (P<0.05). CONCLUSIONS: Corticosterone downregulates the expression of the neuronal migration protein LIS1 in developing cerebral cortical neurons of fetal rats cultured in vitro, and such effect depends on the concentration of corticosterone and duration of corticosterone intervention.

An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans.

Having a system to stratify individuals according to risk is key to clinical disease prevention. This allows individuals identified at different risk tiers to benefit from further investigation and intervention. But the same risk score estimated for two different persons does not mean they need the same further investigation or represent the similarity health condition between two persons. Meanwhile, users still do not know a prior what most of the risk tiers are, and how many tiers should be found in risk stratification. In this paper, the proposed pairwise and size constrained Kmeans (PSCKmeans) method simultaneously integrates the limited supervised information and the size constraints to screen the high-risk population based on similarity measurement, and gets a feasible and balanced stratification solution to avoid cluster with few points. Results on China Health and Nutrition Survey public dataset and follow-up dataset show that the proposed PSCKmeans method can naturally grade the risk of diabetes into four tiers, and achieve 73.8%, 85.1%, and 0.95% sensitivity, specificity, and ratio of minimum to expected on testing data. The proposed method compares favorably with eight previous semisupervised clustering methods; it demonstrates that semisupervised clustering by unifying multiple forms of constraints can guide a good partition that is more relevant for the domain and find new categories through prior knowledge. Finally, this risk stratification model can provide a tool for risk stratification of clinical disease and be used for further intervention for people with similar health condition.

Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.