Search

Alzheimer’s disease (AD) is a neurodegenerative disorder caused by a variety of factors, including age, genetic susceptibility, cardiovascular disease, traumatic brain injury, and environmental factors. The pathogenesis of AD is largely associated with the overproduction and accumulation of amyloid-β peptides and the hyperphosphorylation of tau protein in the brain. Recent studies have identified the presence of diverse pathogens, including viruses, bacteria, and parasites, in the tissues of AD patients, underscoring the critical role of central nervous system infections in inducing pathological changes associated with AD. Nevertheless, it remains unestablished about the specific mechanism by which infections lead to the occurrence of AD. As an important post-transcriptional RNA modification, RNA 5-methylcytosine (m5C) methylation regulates a wide range of biological processes, including RNA splicing, nuclear export, stability, and translation, therefore affecting cellular function. Moreover, it has been recently demonstrated that multiple pathogenic microbial infections are associated with the m5C methylation of the host. However, the role of m5C methylation in infectious AD is still uncertain. Therefore, this review discusses the mechanisms of pathogen-induced AD and summarizes research on the molecular mechanisms of m5C methylation in infectious AD, thereby providing new insight into exploring the mechanism underlying infectious AD.

Abstract Cigarette smoke (CS) has been generally recognized as a chief carcinogenic factor in renal cell carcinoma (RCC). The stimulative effect of CS on renal cancer stem cells (RCSCs) has been described previously. The Sonic Hedgehog (SHH) pathway plays an essential role in self-renewal, cell growth, drug resistance, metastasis, and recurrence of cancer stem cells (CSCs). Renal cancer-related gene ΔNp63α is highly expressed in renal epithelial tissues and contributes to the RCSCs characteristics of tumors. The aim of this study was to elucidate the role of ΔNp63α and the SHH pathway on the activity of RCSCs induced by CS through a series of in vivo and in vitro studies. It was shown that in renal cancer tissues, ΔNp63α and RCSCs markers in smokers are expressed higher than that in non-smokers. RCSCs were effectively enriched by tumor sphere formation assay. Besides, CS increased the expression of RCSCs markers and the capability of sphere-forming in vitro and in vivo. Moreover, the SHH pathway was activated, and the specialized inhibitor alleviated the promotion of CS on RCSCs. ΔNp63α activated the SHH pathway and promoted CS-induced enhancement of RCSCs activity. These findings indicate that ΔNp63α positively regulates the activity of CS-induced RCSCs via the SHH pathway.

Abstract Threatening public health, pulmonary disease (PD) encompasses diverse lung injuries like chronic obstructive PD, pulmonary fibrosis, asthma, pulmonary infections due to pathogen invasion, and fatal lung cancer. The crucial involvement of RNA epigenetic modifications in PD pathogenesis is underscored by robust evidence. These modifications not only shape cell fates but also finely modulate the expression of genes linked to disease progression, suggesting their utility as biomarkers and targets for therapeutic strategies. The critical RNA modifications implicated in PDs are summarized in this review, including N6‐methylation of adenosine, N1‐methylation of adenosine, 5‐methylcytosine, pseudouridine (5‐ribosyl uracil), 7‐methylguanosine, and adenosine to inosine editing, along with relevant regulatory mechanisms. By shedding light on the pathology of PDs, these summaries could spur the identification of new biomarkers and therapeutic strategies, ultimately paving the way for early PD diagnosis and treatment innovation.

Abstract The specific energy of Li metal batteries (LMBs) can be improved by using high‐voltage cathode materials; however, achieving long‐term stable cycling performance in the corresponding system is particularly challenging for the liquid electrolyte. Herein, a novel pseudo‐oversaturated electrolyte (POSE) is prepared by introducing 1,1,2,2‐tetrafluoroethyl‐2,2,3,3‐tetrafluoropropyl ether (TTE) to adjust the coordination structure between diglyme (G2) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Surprisingly, although TTE shows little solubility to LiTFSI, the molar ratio between LiTFSI and G2 in the POSE can be increased to 1:1, which is much higher than that of the saturation state, 1:2.8. Simulation and experimental results prove that TTE promotes closer contact of the G2 molecular with Li+ in the POSE. Moreover, it also participates in the formation of electrolyte/electrode interphases. The electrolyte shows outstanding compatibility with both the Li metal anode and typical high‐voltage cathodes. Li||Li symmetric cells show a long life of more than 2000 h at 1 mA cm−2, 1 mAh cm−2. In the meantime, Li||LiNi0.8Co0.1Mn0.1O2 (NCM811) cell with the POSE shows a high reversible capacity of 134.8 mAh g−1 after 900 cycles at 4.5 V, 1 C rate. The concept of POSE can provide new insight into the Li+ solvation structure and in the design of advanced electrolytes for LMBs.

The Corona Virus Disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has quickly spread worldwide and resulted in significant morbidity and mortality. Although most infections are mild, some patients can also develop severe and fatal myocarditis. In eukaryotic RNAs, 5-methylcytosine (m5C) is a common kind of post-transcriptional modification, which is involved in regulating various biological processes (such as RNA export, translation, and stability maintenance). With the rapid development of m5C modification detection technology, studies related to viral m5C modification are ever-increasing. These studies have revealed that m5C modification plays an important role in various stages of viral replication, including transcription and translation. According to recent studies, m5C methylation modification can regulate SARS-CoV-2 infection by modulating innate immune signaling pathways. However, the specific role of m5C modification in SARS-CoV-2-induced myocarditis remains unclear. Therefore, this review aims to provide insights into the molecular mechanisms of m5C methylation in SARS-CoV-2 infection. Moreover, the regulatory role of NSUN2 in viral infection and host innate immune response was also highlighted. This review may provide new directions for developing therapeutic strategies for SARS-CoV-2-associated myocarditis.

This study aimed to create a robust prediction model for sepsis patient mortality and identify key biomarkers in those with myocardial injury. A retrospective analysis of 261 sepsis inpatients was conducted, with 44 deaths and 217 recoveries. Key factors were assessed via univariate and multivariate analyses, revealing myocardial injury, shock, and pulmonary infection as independent mortality risk factors. Using LASSO regression, a reliable prediction model was developed and internally validated. Additionally, procalcitonin (PCT) emerged as a sensitive biomarker for myocardial injury prediction in sepsis patients. In summary, this study highlights myocardial injury, shock, and pulmonary infection as independent risk factors for sepsis-related deaths. The LASSO-based prediction model effectively forecasts the prognosis of septic patients with myocardial injury, with PCT showing promise as a predictive biomarker.

Abstract Since its discovery in 2019, coronavirus disease 2019 (COVID-2019) spans a wide clinical spectrum from the asymptomatic stage, mild infection, to severe pneumonia. In patients with COVID-2019, factors such as advanced age, diabetes, or hypertension are associated with a significantly increased risk of severe diseases and death. Of note, the mechanisms underlying differences in the risk and symptoms of COVID-2019 among different populations are still poorly characterized. Accordingly, it is imperative to elucidate potential pathophysiological mechanisms and develop targeted therapeutic approaches for COVID-2019 infection. N6-methyladenosine (m6A) is one of the most common modifications in mammalian RNA transcripts and is widely found in messenger RNAs and some non-coding RNAs. It has been reported that m6A methylation modifications are present in viral RNA transcripts, which are of great significance for the regulation of the viral life cycle. Furthermore, m6A methylation has recently been found to be strongly associated with COVID-2019 infection. Therefore, this article reviews recent advances in studies related to the role of m6A methylation in COVID-2019 infection.

Abstract Background This study aims to deeply explore the relationship between m6A methylation modification and peripheral immune cells in patients with advanced sepsis and mine potential epigenetic therapeutic targets by analyzing the differential expression patterns of m6A-related genes in healthy subjects and advanced sepsis patients. Methods A single cell expression dataset of peripheral immune cells containing blood samples from 4 patients with advanced sepsis and 5 healthy subjects was obtained from the gene expression comprehensive database (GSE175453). Differential expression analysis and cluster analysis were performed on 21 m6A-related genes. The characteristic gene was identified based on random forest algorithm, and the correlation between the characteristic gene METTL16 and 23 immune cells in patients with advanced sepsis was evaluated using single-sample gene set enrichment analysis. Results IGFBP1, IGFBP2, IGF2BP1, and WTAP were highly expressed in patients with advanced sepsis and m6A cluster B. IGFBP1, IGFBP2, and IGF2BP1 were positively correlated with Th17 helper T cells. The characteristic gene METTL16 exhibited a significant positive correlation with the proportion of various immune cells. Conclusion IGFBP1, IGFBP2, IGF2BP1, WTAP, and METTL16 may accelerate the development of advanced sepsis by regulating m6A methylation modification and promoting immune cell infiltration. The discovery of these characteristic genes related to advanced sepsis provides potential therapeutic targets for the diagnosis and treatment of sepsis.

Previously, it was believed that type III interferon (IFN-III) has functions similar to those of type I interferon (IFN-I). However, recently, emerging findings have increasingly indicated the non-redundant role of IFN-III in innate antiviral immune responses. Still, the regulatory activity of IFN-III in adaptive immune response has not been clearly reported yet due to the low expression of IFN-III receptors on most immune cells. In the present study, we reviewed the adjuvant, antiviral, antitumor, and disease-moderating activities of IFN-III in adaptive immunity; moreover, we further elucidated the mechanisms of IFN-III in mediating the adaptive antiviral immune response in a thymic stromal lymphopoietin (TSLP)-dependent manner, a pleiotropic cytokine involved in mucosal adaptive immunity. Research has shown that IFN-III can enhance the antiviral immunogenic response in mouse species by activating germinal center B (GC B) cell responses after stimulating TSLP production by microfold (M) cells, while in human species, TSLP exerts OX40L for regulating GC B cell immune responses, which may also depend on IFN-III. In conclusion, our review highlights the unique role of the IFN-III/TSLP axis in mediating host adaptive immunity, which is mechanically different from IFN-I. Therefore, the IFN-III/TSLP axis may provide novel insights for clinical immunotherapy.

Bromadiolone, as a second-generation coumarin anticoagulant rodenticide, may accidently cause harm to humans and non-target animals when overused or misused due to its high toxicity and long-lasting effects. In some severe cases such as the presence of active bleeding, treatment should involve the administration of hemoperfusion therapy. Nafamostat mesylate is a synthesized protease inhibitor that inhibits most factors in the coagulation process, preventing clotting and ensuring smooth blood flow during the procedure. Nafamostat mesylate helps maintain the efficacy and safety of hemoperfusion treatment. Despite its wide application in Japan, the clinical practice and research of nafamostat mesylate are limited in China, especially for patients undergoing maintenance hemodialysis. This paper reports two cases of bromadiolone poisoning and describes the treatment procedure and therapeutic effect of anticoagulation in hemoperfusion therapy with nafamostat mesylate.

Age-related hearing loss (ARHL) is a common issue associated with aging. One of the typical causes of hearing loss is the damage to inner ear hair cells. In addition, oxidative stress and inflammation contribute to ARHL. To avoid excessive inflammatory responses, non-classical scorch death pathway by cell membrane lipopolysaccharide (LPS) activates of caspase-11. Piceatannol (PCT) is also known for anti-tumor, antioxidant and anti-inflammatory effects; however, the protective effect of piceatannol (PCT) on ARHL is unclear. The aim of this study was to elucidate the mechanism underlying protective effect of PCT on ARHL-induced inner ear hair cell damage. In vivo experiments showed that PCT could protect mice from inflammatory aging-induced hearing loss as well as from inner hair cells (IHC) and spiral ganglion (SG) deficits. In addition, inflammatory vesicle inhibitor BAY11–7082 ameliorated ARHL, inhibited NLRP3 and reduced GSDMD expression. In in vitro experiments we used LPS and D-gal to simulate the aging inflammatory environment. The results showed that intracellular reactive oxygen species levels, expression of Caspase-11, NLRP3, and GSDMD were significantly increased, yet treatment with PCT or BAY11–7082 significantly improved HEI-OC-1 cell injury while reducing inflammation-associated protein expression as well as the occurrence of pyroptosis. In conclusion, these results suggest a protective role for PCT against ARHL, possibly through Caspase-11-GSDMD pathway. Our findings may provide a new target and theoretical basis for hearing loss treatment using PCT.

Objective Osteoarthritis (OA) is characterized by synovial cartilage degeneration and is the leading cause of disability and pain worldwide. This study sought to investigate the expression of integrin beta-2 (ITGB2) in synovial fluid of OA patients and its clinical significance. Methods A total of 110 OA patients were enrolled, who were classified into grade I ( N = 35), II ( N = 42), and III ( N = 33) according to the Kellgren-Lawrence classification, with 110 healthy subjects as controls, and their clinical data were compared. ITGB2 level was detected by RT-qPCR. The receiver operating characteristic curve was used to analyze the predictive value of ITGB2 on OA occurrence. The correlation between ITGB2 and bone metabolism indexes procollagen type I N-terminal peptide (PINP), bone glaprotein (BGP), bone alkaline phosphatase (BALP), and β-collagen I telopeptide (β-CTX) was analyzed by the Pearson method. Logistic regression model was performed to analyze the influencing factors of OA. Results The content of red blood cells, white blood cells, PINP, BGP, and BALP was lowered in OA patients, while β-CTX was elevated. ITGB2 was highly-expressed in OA patients, negatively-correlated with PINP, BGP, and BALP, but positively-correlated with β-CTX. ITGB2 level increased with the elevation of OA grade. The ITGB2 level >1.375 had certain diagnostic values for OA. ITGB2 level is related to OA severity and may be a biomarker for OA classification. ITGB2 was an independent risk factor for OA. Conclusion High expression of ITGB2 in synovial fluid can assist OA diagnosis and may be a biomarker for OA grade.

In the present study, the mechanism of electromagnetic resonance interference on an inductive speed sensor is investigated. To this end, a second-order equivalent resonance model is established based on the lumped parameter of the speed sensor. Then the effects of different parameters on the interference signal are analyzed. The effect of terminal loads and filter capacitances are simulated in the CST software and the obtained results for the resonance interference signal are analyzed. Moreover, an experiment is carried out and the oscillation of the voltage interference signal is produced by the speed sensor under the testing of early-time high altitude nuclear electromagnetic pulse (HEMP) field. The GTEM cell is used to produce the plane wave for the experiment. Finally, the advantages and disadvantages of different resonance elimination methods are analyzed, and the method is proposed to eliminate the oscillation of the voltage interference signal.

Abstract Objectives To share our initial experience with the modified vein clamping technique for the treatment of renal cell carcinoma complicated with level I–II IVC thrombi. Methods From March 2018 to April 2021, 11 patients with renal cell carcinoma (RCC) involving an IVC tumour thrombus were admitted to our hospital. They all underwent laparoscopic radical nephrectomy and IVC thrombectomy (LRN-IVCTE) using a modified vein clamping technique. Results All procedures were successfully completed without conversion to open surgery. The median operative time was 185.00 min (145.00–216.00 min); the median estimated blood loss was 200.00 ml (155.00–300.00 ml), and four patients received an intraoperative transfusion. In addition, the median IVC clamping time was 18.00 min (12.00–20.00 min); the median postoperative hospital stay was 6.00 days (4.00–7.00 days), while the median follow-up period was 28.00 months (4.00–34.00 months). Conclusions The modified vein clamping technique for the treatment of renal cell carcinoma complicated with level I–II IVC thrombi may be a safe and technically feasible alternative technique.

Abstract Background Cervical cancer (CC) endangers women’s health in the world range. Accumulating studies have revealed the crucial regulatory role of long non-coding RNAs (lncRNAs) in multiple malignancies, including CC. Our study aimed to explore the role of lncRNA double homeobox A pseudogene 8 (DUXAP8) in cervical carcinogenesis. Methods Gene expressions in CC were assessed by RT-qPCR. Function experiments and tube formation assays were performed to evaluate the role of DUXAP8 in CC cells. Subcellular fractionation and FISH assays were conducted to determine the subcellular location of DUXAP8. Luciferase reporter, RNA pull down and RIP assays were conducted to investigate the mechanism of DUXAP8. Results DUXAP8 was notably upregulated in CC cells. Downregulation of DUXAP8 repressed cell malignant behaviors and angiogenesis in CC. Mechanically, DUXAP8 boosted the expression of reticulocalbin-2 (RCN2) through relieving the binding of miR-1297 to RCN2 3’-UTR. Moreover, miR-1297 inhibition and RCN2 overexpression could counteract the inhibitory effects of DUXAP8 knockdown on the malignant phenotypes of CC cells. Besides, enhanced RCN2 expression restored the tumor growth in vivo that was inhibited by DUXAP8 repression. Conclusions DUXAP8 promotes malignant behaviors in CC cells via regulating miR-1297/RCN2 axis. Graphical Abstract

IntroductionSepsis is a severe clinical syndrome caused by dysregulated systemic inflammatory responses to infection. Methylation modification, as a crucial mechanism of RNA functional modification, can manipulate the immunophenotype and functional activity of immune cells to participate in sepsis progression. This study aims to explore the mechanism of N6-methyladenosine (m6A) methylation modification in immune cell-mediated sepsis through keyword search.MethodsLiterature retrieval.Results and DiscussionLiterature retrieval reveals that m6A methylation is implicated in sepsis-induced lung injury and myocardial injury,as well as sepsis-related encephalopathy. Furthermore, it is found that m6A methylation can regulate sepsis by inhibiting the chemotaxis of neutrophils and the formation of neutrophil extracellular traps and suppressing macrophage phagocytosis, thereby playing a role in sepsis.

Effective data-driven rotating machine fault diagnosis has recently been a research topic in the diagnosis and health management of machinery systems owing to the benefits, including safety guarantee, labor saving, and reliability improvement. However, in vast real-world applications, the classifier trained on one dataset will be extended to datasets under variant working conditions. Meanwhile, the deviation between datasets can be triggered easily by rotating speed oscillation and load variation, and it will highly degenerate the performance of machine learning-based fault diagnosis methods. Hence, a novel dataset distribution discrepancy measuring algorithm called high-order Kullback-Leibler (HKL) divergence is proposed. Based on HKL divergence and transfer learning, a new fault diagnosis network which is robust to working condition variation is constructed in this paper. In feature extraction, sparse filtering with HKL divergence is proposed to learn sharing and discriminative features of the source and target domains. In feature classification, HKL divergence is introduced into softmax regression to link the domain adaptation with health conditions. Its effectiveness is verified by experiments on a rolling bearing dataset and a gearbox dataset, which include 18 transfer learning cases. Furthermore, the asymmetrical performance phenomenon found in experiments is also analyzed.

Abstract Background CCL2 was up-regulated in neurons and involved in microglia activation and neurological decline in mice suffering from hepatic encephalopathy (HE). However, no data exist concerning the effect of neuron-derived CCL2 on microglia activation in vitro. Methods The rats were pretreated with CCL2 receptor inhibitors (INCB or C021, 1 mg/kg/day i.p.) for 3 days prior to thioacetamide (TAA) administration (300 mg/kg/day i.p.) for inducing HE model. At 8 h following the last injection (and every 4 h after), the grade of encephalopathy was assessed. Blood and whole brains were collected at coma for measuring CCL2 and Iba1 expression. In vitro, primary neurons were stimulated with TNF-α, and then the medium were collected for addition to microglia cultures with or without INCB or C021 pretreatment. The effect of the medium on microglia proliferation and activation was evaluated after 24 h. Results CCL2 expression and microglia activation were elevated in the cerebral cortex of rats received TAA alone. CCL2 receptors inhibition improved neurological score and reduced cortical microglia activation. In vitro, TNF-α treatment induced CCL2 release by neurons. Medium from TNF-α stimulated neurons caused microglia proliferation and M1 markers expression, including iNOS, COX2, IL-6 and IL-1β, which could be suppressed by INCB or C021 pretreatment. The medium could also facilitate p65 nuclear translocation and IκBα phosphorylation, and NF-κB inhibition reduced the increased IL-6 and IL-1β expression induced by the medium. Conclusion Neuron-derived CCL2 contributed to microglia activation and neurological decline in HE. Blocking CCL2 or inhibiting microglia excessive activation may be potential strategies for HE.

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

The intensity data recorded by a terrestrial laser scanner (TLS) contain spectral characteristics of a scanned target and are mainly influenced by incidence angle and distance. In this study, an improved implementable method is proposed to empirically correct the intensity data of long-distance TLSs. Similar to existing methods, the incidence angle⁻intensity relationship is estimated using some reference targets scanned in the laboratory. By contrast, due to the length limit of indoor environments and the laborious data processing, the distance⁻intensity relationship is derived by selecting some natural homogeneous targets with distances covering the entire distance scale of the adopted long-distance TLS. A case study of intensity correction and point cloud classification in an intertidal zone in Chongming Island, Shanghai, China, is conducted to validate the feasibility of the improved method by using the intensity data of a long-distance TLS (Riegl VZ-4000). Results indicate that the improved method can accurately eliminate the effects of incidence angle and distance on the intensity data of long-distance TLSs; the coefficient of variation of the intensity data for the targets in the study intertidal zone can be reduced by approximately 54%. The classification results of the study intertidal zone show that the improved method can effectively eliminate the variations caused by the incidence angle and distance in the original intensity data of the same target to obtain a corrected intensity that merely depends on target characteristics for improving classification accuracy by 49%.

Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model.

Alzheimer's disease (AD) is a neurodegenerative disorder caused by a variety of factors, including age, genetic susceptibility, cardiovascular disease, traumatic brain injury, and environmental factors. The pathogenesis of AD is largely associated with the overproduction and accumulation of amyloid-β peptides and the hyperphosphorylation of tau protein in the brain. Recent studies have identified the presence of diverse pathogens, including viruses, bacteria, and parasites, in the tissues of AD patients, underscoring the critical role of central nervous system infections in inducing pathological changes associated with AD. Nevertheless, it remains unestablished about the specific mechanism by which infections lead to the occurrence of AD. As an important post-transcriptional RNA modification, RNA 5-methylcytosine (m5C) methylation regulates a wide range of biological processes, including RNA splicing, nuclear export, stability, and translation, therefore affecting cellular function. Moreover, it has been recently demonstrated that multiple pathogenic microbial infections are associated with the m5C methylation of the host. However, the role of m5C methylation in infectious AD is still uncertain. Therefore, this review discusses the mechanisms of pathogen-induced AD and summarizes research on the molecular mechanisms of m5C methylation in infectious AD, thereby providing new insight into exploring the mechanism underlying infectious AD. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has quickly spread worldwide and resulted in significant morbidity and mortality. Although most infections are mild, some patients can also develop severe and fatal myocarditis. In eukaryotic RNAs, 5-methylcytosine (m5C) is a common kind of post-transcriptional modification, which is involved in regulating various biological processes (such as RNA export, translation, and stability maintenance). With the rapid development of m5C modification detection technology, studies related to viral m5C modification are ever-increasing. These studies have revealed that m5C modification plays an important role in various stages of viral replication, including transcription and translation. According to recent studies, m5C methylation modification can regulate SARS-CoV-2 infection by modulating innate immune signaling pathways. However, the specific role of m5C modification in SARS-CoV-2-induced myocarditis remains unclear. Therefore, this review aims to provide insights into the molecular mechanisms of m5C methylation in SARS-CoV-2 infection. Moreover, the regulatory role of NSUN2 in viral infection and host innate immune response was also highlighted. This review may provide new directions for developing therapeutic strategies for SARS-CoV-2-associated myocarditis. [ABSTRACT FROM AUTHOR]

Increasing energy demand throughout the world produces great environmental issues, therefore, renewable and clean energy sources, such as tidal energy, wind energy, solar energy and geothermal energy, are desirable request. Meanwhile, developing a new critical energy storage technology to balance the instantaneous energy supply and demand of arranged electric energy is urgent. Rechargeable flow batteries are solutions for storing electricity in form of chemical energy, containing positive and negative electrodes reserved in two separate containers, which have the advantages of low self—discharge and independent scaling of power, therefore considered as promising energy storage technologies. Ionic liquids (ILs) have been widely studied and used in energy storage devices, such as lithium ion battery, for their unique prospective properties. Herein, the key role of ILs and their applications in supporting electrolytes, separators and additives in flow batteries are highlighted in this review. The approaches and challenges in developing ILs supported flow batteries are discussed, and a significative overview of the opportunities of ILs promote flow batteries are finally provided, which is expected to help achieving further improvements in flow batteries. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs have been confirmed closely related to the metastasis and angiogenesis of breast cancer (BC). LINC01857 can promote the growth and metastasis of BC cells. The present work focused on exploring the role of LINC01857 in BC metastasis and angiogenesis and investigating the possible mechanisms. The results showed that LINC01857 and CENPQ were highly expressed in BC tissues and cells, while miR-2052 was contrarily expressed. In vitro study showed that low expression of linc01857 could inhibit the migration ability and vascularization of BC cells, and mir-2052 inhibitor partially restored the effect of si-LINC01857 on the migration ability and vascularization of BC cells. Likewise, inhibition of CENPQ can partially rescue the effects of miR-2052 inhibitor on the migration ability and vascularization of BC cells. In vivo studies showed that down-regulation of LINC01857 notably suppressed tumor growth and angiogenesis in nude mice. The miR-2052 inhibitor partially restored the effects of si-LINC01857. CENPQ suppression partially rescued the effects of the miR-2052 inhibitor. To conclude, LINC01857/miR-2052/ CENPQ is the potential novel target for BC treatment. [ABSTRACT FROM AUTHOR]

Substantial evidence suggests that cancer stem cells (CSCs) are the main cause of the initiation, progression and recurrence of tumors. Benzidine has been identified as a risk factor for bladder cancer. The aim of the present study was to investigate the effects of benzidine on bladder CSCs (BCSCs) and the possible mechanism underlying its action. The bladder cancer cell lines UM-UC-3 and EJ were maintained in serum-free medium and cells forming three-dimensional spheres were characterized as BCSCs. The sphere-forming cells were exposed to different concentrations of benzidine and vismodegib, and western blotting was performed to evaluate the expression of markers associated with CSCs and the Sonic hedgehog (SHH) signaling pathway. Flow cytometry was used to detect the distribution of cells in different phases of the cell cycle, and immunofluorescence staining was used to detect the protein expression of CD44. The results revealed that the levels of BCSC markers, namely CD133, CD44, aldehyde dehydrogenase 1-A1, Nanog and octamer-binding transcription factor-4, in the cell spheres were markedly elevated compared with those in cells cultured in serum-supplemented medium. Furthermore, benzidine increased the expression of BCSC markers and promoted the sphere-forming ability of the cells. In addition, it was observed that benzidine activated the SHH pathway, while inhibition of the Shh pathway using vismodegib diminished the promoting effects of benzidine on BCSCs. The findings of the present study indicate that benzidine promoted the stemness of BCSCs via activation of the SHH pathway, which may support further exploration of the molecular basis of the association between benzidine exposure and bladder oncogenesis. [ABSTRACT FROM AUTHOR]

In this study, a method for optimizing equipment resources based on efficiency evaluation and the relative advantage relationship is proposed. An efficiency evaluation and optimization model are also constructed, and the corresponding algorithm is designed. The effects of altering the decision-matrix canonical method and quantity discount on multi-attribute decision-making are studied by investigating the available feasible solutions. The results show that the proposed efficiency evaluation and optimization model can yield several groups of optimal feasible solutions. Additionally, combining it with a multi-attribute decision model can enable it to effectively select and rank feasible solutions while avoiding the adverse effect of different decision-specification methods on the decision-making result. The decision method and quantity discount considerably influence the multi-attribute decision-making results, implying their importance when optimizing the equipment resources with respect to a manufacturing process. [ABSTRACT FROM AUTHOR]

Wind power is one of the most representative renewable energy and has attracted wide attention in recent years. With the increasing installed capacity of global wind power, its nature of randomness and uncertainty has posed a serious risk to the safe and stable operation of the power system. Therefore, accurate wind power prediction plays an increasingly important role in controlling the impact of the fluctuations of wind power to in system dispatch planning. Recently, with the rapid accumulation of data resource and the continuous improvement of computing power, data-driven artificial intelligence technology has been popularly applied in many industries. AI-based models in the field of wind power prediction have become a cutting-edge research subject. This paper comprehensively reviews the AI-based models for wind power prediction at various temporal and spatial scales, covering from wind turbine level to regional level. To obtain in-depth insights on performance of various prediction methods, we review and analyze performance evaluation metrics of both deterministic models and probabilistic models for wind power prediction. In addition, challenges arising in data quality control, feature engineering, and model generalization for the data-driven wind power prediction methods are discussed. Future research directions to improving the accuracy of data-driven wind power prediction are also addressed.

Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF. [ABSTRACT FROM AUTHOR]

Vinorelbine (VIN) is a semi-synthetic vinca alkaloid and is one of the most active agents for the treatment of solid tumors. The drug is commonly administered through a peripheral vein. Although VIN is known to cause local venous toxicity, such as drug-induced phlebitis, the mechanism responsible for the toxicity remains unclear. To investigate the role of Toll-like receptor 4 (TLR4) in VIN-induced vascular endothelial injury, human umbilical vein endothelial cells (HUVECs) were prepared from umbilical cords recovered with the written informed consent of the parents and treated with VIN for 60 min. Following the washing away of the VIN, the cells were cultured for a further 6 and 12 h, and the changes in TLR4 expression in the HUVECs were detected by quantitative polymerase chain reaction and western blot analyses. Finally, the effects of VIN on the translocation of nuclear factor κB (NF-κB) were determined by immunofluorescence and confocal analysis. The VIN-treated cells were stretched, extended and irregular while the control cells exhibited normal morphology. The TLR4 mRNA and protein levels were significantly higher in the VIN-treated HUVECs than those in the control group (P<0.05). The rate of NF-κB p65 nuclear translocation in the VIN-treated HUVECs was significantly higher than that in the vehicle-treated group (P<0.05). These results indicated that TLR4 is involved in the development of VIN-induced vascular endothelial injury by affecting the translocation of NF-κB. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used in the field of fault diagnosis due to its excellent performance in feature extraction and has been gradually applied to solve various problems in fault diagnosis. Convolutional neural networks and transfer learning networks have been gradually employed to solve the problems of sample imbalance and domain adaptation under variable rotational speeds in fault diagnosis. However, there are still some weaknesses in current research. Firstly, sample imbalance in fault diagnosis is accompanied by the domain-adaptive problem in practice. Secondly, transfer learning cannot extract domain-invariant features that do not change with the rotational speed, which leads to poor diagnosis results when it is applied to other working conditions without transferring the rotating speed. To solve the above problems, an architecture named a convolutional transfer feature discrimination network (CTFDN) is proposed in this paper. It uses the scaled exponential linear unit activation function, and the main body adopts a two-branch network architecture of weight sharing, which mainly includes a deep feature extraction network for fusion feature extraction, a transfer learning network for domain-adaptive problems, and an unbalanced-sample feature discrimination network for feature similarity determination. Finally, two fault datasets are used to test the validity of the proposed model. The results show that the CTFDN model can extract domain-invariant features well in unbalanced fault sample information, and still has good diagnostic accuracy under variable rotational speeds. [ABSTRACT FROM AUTHOR]

Among various fault diagnosis methods, deep learning has shown state-of-the-art performance in processing mechanical big data. This paper investigates a reliable deep learning method known as autoencoder, which is most suitable for automatic feature extraction of fault signals. However, traditional autoencoders have two deficiencies: (1) the multi-layer structure of autoencoder has an internal covariate shift problem, which will cause great difficulty for the network training. (2) The application of autoencoder in the case of rotating speed fluctuation is not mature. To overcome the aforementioned deficiencies, batch normalization strategy is employed in every layer of the autoencoder network to obtain a steady distribution of activation values during training. It can regularize the network without parameter adjustment, and deal with the speed fluctuation problem perfectly. So, a new network named batch-normalized autoencoder is first proposed for intelligent fault diagnosis. The raw vibration signals are directly fed into the network and the extracted features are employed to train a softmax classifier for health state identification. A bearing and a gearbox data set are finally used to confirm the effectiveness of the proposed method. The results manifest that the proposed method can extract salient features from the raw signals and handle the fault diagnosis problem under the speed fluctuation problem. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing to their efficiency in extracting representative features. However, there is always an undesirable shift variant property embedded in raw vibration signals, which hinders the direct use of raw signals in fault diagnosis networks. A convolutional neural network (CNN) is a widely used and efficient method to extract features in various fields for its excellent sparse connectivity, equivalent representation and weight sharing properties. However, raw CNN is time-consuming and has a marginal problem. Heuristically, we construct a fault diagnosis framework called adaptive overlapping CNN (AOCNN) to deal with one dimension (1D) raw vibration signals directly. The shift variant problem is dealt with by the adaptive convolutional layer and the root-mean-square (RMS) pooling layer, and the marginal problem embedded in the CNN is relieved by employing the overlapping layer. Meanwhile, the AOCNN is also characterized by adopting different convolutional strides and diverse activation functions in feature extraction network training and usage. Furthermore, sparse filtering is embedded into the AOCNN, and experiments on a bearing dataset and a gearbox dataset are conducted to verify the validity of the proposed method separately. When compared with other state-of-the-art methods this method reveals its superiority. [ABSTRACT FROM AUTHOR]