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AbstractContext Qinggong Shoutao Wan (QGSTW) is a pill used as a traditional medicine to treat age-associated memory decline (AAMI). However, its potential mechanisms are unclear.Objective This study elucidates the possible mechanisms of QGSTW in treating AAMI.Materials and methods Network pharmacology and molecular docking approaches were utilized to identify the potential pathway by which QGSTW alleviates AAMI. C57BL/6J mice were divided randomly into control, model, and QGSTW groups. A mouse model of AAMI was established by d-galactose, and the pathways that QGSTW acts on to ameliorate AAMI were determined by ELISA, immunofluorescence staining and Western blotting after treatment with d-gal (100 mg/kg) and QGSTW (20 mL/kg) for 12 weeks.Results Network pharmacology demonstrated that the targets of the active components were significantly enriched in the cAMP signaling pathway. AKT1, FOS, GRIN2B, and GRIN1 were the core target proteins. QGSTW treatment increased the discrimination index from −16.92 ± 7.06 to 23.88 ± 15.94% in the novel location test and from −19.54 ± 5.71 to 17.55 ± 6.73% in the novel object recognition test. ELISA showed that QGSTW could increase the levels of cAMP. Western blot analysis revealed that QGSTW could upregulate the expression of PKA, CREB, c-Fos, GluN1, GluA1, CaMKII-α, and SYN. Immunostaining revealed that the expression of SYN was decreased in the CA1 and DG.Discussion and conclusions This study not only provides new insights into the mechanism of QGSTW in the treatment of AAMI but also provides important information and new research ideas for the discovery of traditional Chinese medicine compounds that can treat AAMI.

Abstract As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a scalable parameterized quantum circuits classifier (SPQCC), which performs per-channel PQC and combines the measurements as the output of the trainable parameters of the classifier. By minimizing the cross-entropy loss through optimizing the trainable parameters of PQC, SPQCC leads to a fast convergence of the classifier. The parallel execution of identical PQCs on different quantum machines with the same structure and scale reduces the complexity of classifier design. Classification simulations performed on the MNIST Dataset show that the accuracy of our proposed classifier far exceeds that of other quantum classification algorithms, achieving the state-of-the-art simulation result and surpassing/reaching classical classifiers with a considerable number of trainable parameters. Our classifier demonstrates excellent scalability and classification performance.

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduce the memory consumption of classical models for vulnerability detection tasks and improve the performance of quantum natural language processing (QNLP) methods. We show that the performance of RQENN achieves the above goals. Compared with the classic model, the space complexity of each stage of its execution is exponentially reduced, and the number of parameters used and the number of bits consumed are significantly reduced. Compared with other QNLP methods, RQENN uses fewer qubit resources and achieves a 15.7% higher accuracy in vulnerability detection.

Coronary microvascular disease (CMVD) is common in patients with cardiovascular risk factors and is linked to an elevated risk of adverse cardiovascular events. Although modern medicine has made significant strides in researching CMVD, we still lack a comprehensive understanding of its pathophysiological mechanisms due to its complex and somewhat cryptic etiology. This greatly impedes the clinical diagnosis and treatment of CMVD. The primary pathological mechanisms of CMVD are structural abnormalities and/or dysfunction of coronary microvascular endothelial cells. The development of CMVD may also involve a variety of inflammatory factors through the endothelial cell injury pathway. This paper first reviews the correlation between the inflammatory response and CMVD, then summarizes the possible mechanisms of inflammatory response in CMVD, and finally categorizes the drugs used to treat CMVD based on their effect on the inflammatory response. We hope that this paper draws attention to CMVD and provides novel ideas for potential therapeutic strategies based on the inflammatory response.

Abstract Background Little evidence exists regarding the prevalence of pathogens in bloodstream infections (BSIs), the mortality risk, and the benefit of combination therapy over monotherapy. This study aims to describe patterns of empiric antimicrobial therapy, and the epidemiology of Gram-negative pathogens, and to investigate the effect of appropriate therapy and appropriate combination therapy on the mortality of patients with BSIs. Methods This was a retrospective cohort study including all patients with BSIs of Gram-negative pathogens from January 2017 to December 2022 in a Chinese general hospital. The in-hospital mortality was compared between appropriate and inappropriate therapy, and between monotherapy and combination therapy for patients receiving appropriate therapy. We used Cox regression analysis to identify factors independently associated with in-hospital mortality. Results We included 205 patients in the study, of whom 147 (71.71%) patients received appropriate therapy compared with 58 (28.29%) who received inappropriate therapy. The most common Gram-negative pathogen was Escherichia coli (37.56%). 131 (63.90%) patients received monotherapy and 74 (36.10%) patients received combination therapy. The in-hospital mortality was significantly lower in patients administered appropriate therapy than inappropriate therapy (16.33% vs. 48.28%, p = 0.004); adjusted hazard ratio [HR] 0.55 [95% CI 0.35–0.84], p = 0.006). In-hospital mortality was also not different in combination therapy and monotherapy in the multivariate Cox regression analyses (adjusted HR 0.42 [95% CI 0.15–1.17], p = 0.096). However, combination therapy was associated with lower mortality than monotherapy in patients with sepsis or septic shock (adjusted HR 0.94 [95% CI 0.86–1.02], p = 0.047). Conclusions Appropriate therapy was associated with a protective effect on mortality among patients with BSIs due to Gram-negative pathogens. Combination therapy was associated with improved survival in patients with sepsis or septic shock. Clinicians need to choose optical empirical antimicrobials to improve survival outcomes in patients with BSIs.

Coronary artery disease has one of the highest mortality rates in the country, and methods such as thrombolysis and percutaneous coronary intervention (PCI) can effectively improve symptoms and reduce mortality, but most patients still experience symptoms such as chest pain after PCI, which seriously affects their quality of life and increases the incidence of adverse cardiovascular events (myocardial ischaemiareperfusion injury, MIRI). MIRI has been shown to be closely associated with circadian rhythm disorders and mitochondrial dysfunction. Mitochondria are a key component in the maintenance of normal cardiac function, and new research shows that mitochondria have circadian properties. Traditional Chinese medicine (TCM), as a traditional therapeutic approach characterised by a holistic concept and evidence-based treatment, has significant advantages in the treatment of MIRI, and there is an interaction between the yin-yang theory of TCM and the circadian rhythm of Western medicine at various levels. This paper reviews the clinical evidence for the treatment of MIRI in TCM, basic experimental studies on the alleviation of MIRI by TCM through the regulation of mitochondria, the important role of circadian rhythms in the pathophysiology of MIRI, and the potential mechanisms by which TCM regulates mitochondrial circadian rhythms to alleviate MIRI through the regulation of the biological clock transcription factor. It is hoped that this review will provide new insights into the clinical management, basic research and development of drugs to treat MIRI.

As aromatic compounds can be obtained easily, their functionalization is a cornerstone of organic synthesis. Here, the authors report a dearomatization of aromatic isocyanides, installing three carbon-carbon bonds, two quaternary stereocenters, and three orthogonal functionalities in one operation.

BackgroundType 2 diabetes mellitus (T2DM) is a prevalent metabolic disease that causes multi-organ complications, seriously affecting patients’ quality of life and survival. Understanding its pathogenesis remains challenging, with current clinical treatment regimens often proving ineffective.MethodsIn this study, we established a mouse model of T2DM and employed 16s rDNA sequencing to detect changes in the species and structure of gut flora. Additionally, we used UPLC-Q-TOF-MS to identify changes in urinary metabolites of T2DM mice, analyzed differential metabolites and constructed differential metabolic pathways. Finally, we used Pearman correlation analysis to investigate the relationship between intestinal flora and differential metabolites in T2DM mice, aiming to elucidate the pathogenesis of T2DM and provide an experimental basis for its clinical treatment.ResultsOur findings revealed a reduction in both the species diversity and abundance of intestinal flora in T2DM mice, with significantly decreased levels of beneficial bacteria such as Lactobacillus and significantly increased levels of harmful bacteria such as Helicobacter pylori. Urinary metabolomics results identified 31 differential metabolites between T2DM and control mice, including Phosphatidylcholine, CDP-ethanolamine and Leukotriene A4, which may be closely associated with the glycerophospholipid and arachidonic acid pathways. Pearman correlation analysis showed a strong correlation between dopamine and gonadal, estradiol and gut microbiota, may be a novel direction underlying T2DM.ConclusionIn conclusion, our study suggests that alterations in gut microbiota and urinary metabolites are characteristic features of T2DM in mice. Furthermore, a strong correlation between dopamine, estradiol and gut microbiota, may be a novel direction underlying T2DM, the aim is to provide new ideas for clinical treatment and basic research.

Objectives To evaluate the efficacy and safety of anti-vascular endothelial growth factor (anti-VEGF) therapy for myopia choroidal neovascularisation (CNV), and to compare the efficacy of two different anti-VEGF retreatment criteria.Data sources PubMed, EMBASE, the Cochrane Library and ClinicalTrials.gov were searched from inception to 31 July 2022.Study selection Randomised controlled trials (RCTs) comparing anti-VEGF with sham, photodynamic therapy (PDT) or PDT combination therapy in patients with myopia CNV were reviewed and selected. RCTs comparing visual acuity (VA) stabilisation or disease activity as anti-VEGF retreatment criteria were also included in the study.Data extraction and synthesis Two reviewers independently conducted data extraction and quality assessment. We used a random-effects model for all analyses. Primary outcomes included best-corrected visual acuity (BCVA) and central foveal thickness. Secondary outcomes included number of patients who gained more than three lines in BCVA, number of anti-VEGF injections and ocular adverse event (AE).Results Seven RCTs involving 1007 patients were included. Compared with sham and PDT therapy, anti-VEGF therapy achieved better BCVA gains of −0.28 logMAR (95% CI −0.36 to −0.20, p

In the field of machine learning, the multi-category classification problem plays a crucial role. Solving the problem has a profound impact on driving the innovation and development of machine learning techniques and addressing complex problems in the real world. In recent years, researchers have begun to focus on utilizing quantum computing to solve the multi-category classification problem. Some studies have shown that the process of processing information in the brain may be related to quantum phenomena, with different brain regions having neurons with different structures. Inspired by this, we design a quantum multi-category classifier model from this perspective for the first time. The model employs a heterogeneous population of quantum neural networks (QNNs) to simulate the cooperative work of multiple different brain regions. When processing information, these heterogeneous clusters of QNNs allow for simultaneous execution on different quantum computers, thus simulating the brain’s ability to utilize multiple brain regions working in concert to maintain the robustness of the model. By setting the number of heterogeneous QNN clusters and parameterizing the number of stacks of unit layers in the quantum circuit, the model demonstrates excellent scalability in dealing with different types of data and different numbers of classes in the classification problem. Based on the attention mechanism of the brain, we integrate the processing results of heterogeneous QNN clusters to achieve high accuracy in classification. Finally, we conducted classification simulation experiments on different datasets. The results show that our method exhibits strong robustness and scalability. Among them, on different subsets of the MNIST dataset, its classification accuracy improves by up to about 5% compared to other quantum multiclassification algorithms. This result becomes the state-of-the-art simulation result for quantum classification models and exceeds the performance of classical classifiers with a considerable number of trainable parameters on some subsets of the MNIST dataset.

There have been numerous quantum neural networks reported, but they struggle to match traditional neural networks in accuracy. Given the huge improvement of the neural network models’ accuracy by two-dimensional tensor network (TN) states in classical tensor network machine learning (TNML), it is promising to explore whether its application in quantum machine learning can extend the performance boundary of the models. Here, we transform two-dimensional TNs into quantum circuits for supervised learning. Specifically, we encode two-dimensional TNs into quantum circuits through rigorous mathematical proofs for constructing model ansätze, including string-bond states, entangled-plaquette states and isometric TN states. In addition, we propose adaptive data encoding methods and combine with TNs. We construct a tensor-network-inspired quantum circuit (TNQC) supervised learning framework for transferring TNML from classical to quantum, and build several novel two-dimensional TN-inspired quantum classifiers based on this framework. Finally, we propose a parallel quantum machine learning method for multi-class classification to construct 2D TNQC-based multi-class classifiers. Classical simulation results on the MNIST benchmark dataset show that our proposed models achieve the state-of-the-art accuracy performance, significantly outperforming other quantum classifiers on both binary and multi-class classification tasks, and beat simple convolutional classifiers on a fair track with identical inputs. The noise resilience of the models makes them successfully run and work in a real quantum computer.

Abstract Background Abnormal glycosylation in a variety of cancer types is involved in tumor progression and chemoresistance. Glycosyltransferase C1GALT1, the key enzyme in conversion of Tn antigen to T antigen, is involved in both physiological and pathological conditions. However, the mechanisms of C1GALT1 in enhancing oncogenic phenotypes and its regulatory effects via non-coding RNA are unclear. Methods Abnormal expression of C1GALT1 and its products T antigen in human bladder cancer (BLCA) were evaluated with BLCA tissue, plasma samples and cell lines. Effects of C1GALT1 on migratory ability and proliferation were assessed in YTS-1 cells by transwell, CCK8 and colony formation assay in vitro and by mouse subcutaneous xenograft and trans-splenic metastasis models in vivo. Dysregulated circular RNAs (circRNAs) and microRNAs (miRNAs) were profiled in 3 pairs of bladder cancer tissues by RNA-seq. Effects of miR-1-3p and cHP1BP3 (circRNA derived from HP1BP3) on modulating C1GALT1 expression were investigated by target prediction program, correlation analysis and luciferase reporter assay. Functional roles of miR-1-3p and cHP1BP3 on migratory ability and proliferation in BLCA were also investigated by in vitro and in vivo experiments. Additionally, glycoproteomic analysis was employed to identify the target glycoproteins of C1GALT1. Results In this study, we demonstrated upregulation of C1GALT1 and its product T antigen in BLCA. C1GALT1 silencing suppressed migratory ability and proliferation of BLCA YTS-1 cells in vitro and in vivo. Subsets of circRNAs and miRNAs were dysregulated in BLCA tissues. miR-1-3p, which is reduced in BLCA tissues, inhibited transcription of C1GALT1 by binding directly to its 3′-untranslated region (3′-UTR). miR-1-3p overexpression resulted in decreased migratory ability and proliferation of YTS-1 cells. cHP1BP3 was upregulated in BLCA tissues, and served as an miR-1-3p “sponge”. cHP1BP3 was shown to modulate migratory ability, proliferation, and colony formation of YTS-1 cells, and displayed tumor-suppressing activity in BLCA. Target glycoproteins of C1GALT1, including integrins and MUC16, were identified. Conclusions This study reveals the pro-metastatic and proliferative function of upregulated glycosyltransferase C1GLAT1, and provides preliminary data on mechanisms underlying dysregulation of C1GALT1 via miR-1-3p / cHP1BP3 axis in BLCA.

Hypertension has become one of the major public health problems in the world. At present, the pathogenesis of hypertension has still not been completely elucidated. In recent years, an increasing evidence shows that intestinal microecology is closely related to hypertension, which provides a new thinking for the prevention and treatment of hypertension. Traditional Chinese medicine (TCM) has unique advantages in the treatment of hypertension. Taking intestinal microecology as the target, it is possible to interpreting the scientific connotation of TCM prevention and treatment of hypertension by updating the treatment concept of hypertension, so as to improve the therapeutic effect. In our study, the clinical evidence for TCM treatment of hypertension was systematicly summarized. And the relationship among TCM, intestinal microecology and hypertension was analyzed. In addition, the methods by which TCM regulates intestinal microecology to prevent and treat hypertension were presented, to provide new research ideas for prevention and treatment of hypertension.

Purpose: Tangzhiqing formula (TZQ) is a traditional Chinese medicine prescribed to treat lipid metabolism disorders, atherosclerosis, diabetes and diabetic cardiomyopathy. However, some challenges and hurdles remain. TZQ showed promising results in treating diabetes and hyperlipidaemia. However, its effect on and mechanism of action in hyperlipidaemia complicated with myocardial ischaemia (HL-MI) remain unknown. Methods: In this study, a network pharmacology-based strategy integrating target prediction was adopted to predict the targets of TZQ relevant to the treatment of HL-MI and to further explore the involved pharmacological mechanisms. Results: A total of 104 potential therapeutic targets were obtained, including MMP9, Bcl-2, and Bax, which may be related to the apoptosis and PI3K/AKT signalling pathways. Then, we confirmed these potential targets and pathways with animal experimentation. TZQ reduced lipid levels, increased the expression levels of Bcl-2, decreased Bax, caspase-3 and caspase-9 expression levels, and activated the PI3K/AKT signalling pathway. Conclusion: In conclusion, this study provides new insights into the protective mechanisms of TZQ against HL-MI through network pharmacology and pharmacological approaches.

All species have a physiological need for sleep, and sleep is crucial for the preservation and restoration of many physiological processes in the body. Recent research on the effects of gut microbiota on brain function has produced essential data on the relationship between them. It has been discovered that dysregulation of the gut-brain axis is related to insomnia. Certain metabolites of gut microbiota have been linked to insomnia, and disturbances in gut microbiota can worsen insomnia. Traditional Chinese medicine (TCM) has unique advantages for the treatment of insomnia. Taking the gut microbiota as the target and determining the scientific relevance of TCM to the prevention and treatment of insomnia may lead to new concepts for the treatment of sleep disorders and improve the therapeutic effect of sleep. Taking the gut microbiota as an entry point, this paper reviews the relationship between gut microbiota and TCM, the relationship between gut microbiota and insomnia, the mechanism by which gut microbiota regulate sleep, and the mechanism by which TCM regulates gut microbiota for insomnia prevention and treatment. This review provides new ideas for the prevention and treatment of insomnia through TCM and new ideas for drug development.

Abstract In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

Low-carbon pilot policies are an important way to achieve the goal of "peak carbon neutrality" and are of great significance to China's international commitments. Based on a sample of 282 prefecture-level cities from 2006 to 2020, this paper investigates the impact of low-carbon pilot policies on urban carbon efficiency using a quasi-natural experiment with three batches of low-carbon pilot cities in 2010, 2012, and 2017, respectively. It is found that: (1) low-carbon pilot cities can improve urban carbon emission efficiency, which is still valid after a series of robustness tests such as the parallel trend test, placebo test, PSM-DID, and counterfactual test; (2) low-carbon pilot cities can enhance urban carbon emission efficiency by promoting the level of urban innovation and advanced urban industrial structure; and (3) the impact of low-carbon pilot policies on urban carbon emission efficiency is heterogeneous across cities with different geographical locations, population sizes, and resource endowment types. The findings provide policy insights for the promotion of low-carbon pilot policies and strengthening the construction of low-carbon pilot cities.

Purpose To evaluate the efficacy and safety of antivascular endothelial growth factor (anti-VEGF) agents in treating macular oedema due to retinal vein occlusion (RVO-ME).Methods Studies of randomised controlled trials were searched in PubMed, EMBASE, Cochrane databases and ClinicalTrials.gov registry. RevMan V.5.4 software and GRADE were used to synthesise the data and validate the evidence, respectively.Results Seventeen studies were included in this meta-analysis. The anti-VEGF agents showed significant better mean best-corrected visual acuity (BCVA) improvement, more patients with ≧15 Early Treatment in Diabetic Retinopathy Study (ETDRS) letters gained and fewer patients with ≧15 ETDRS letters lost, more effectiveness at reducing central retinal thickness (CRT) and improving the quality of life than sham and steroids both at 6 and 12 months. Compared with laser, the anti-VEGF agents showed significant BCVA improvement and more effectiveness at CRT both at 6 and 12 months. The proportion of eyes gaining ≧15 ETDRS letters was greater in the anti-VEGF group at 6 months. The anti-VEGF and other three groups reported similar levels of non-ocular serious adverse events (SAEs). The incidence of eye pain was significantly higher in the anti-VEGF group than in the sham group. There was a significant increase in the incidence of ocular AEs and conjunctival haemorrhage in the anti-VEGF group compared with the laser group. The incidence of elevated intraocular pressure and cataract was significantly higher in the steroid group than in the anti-VEGF group.Conclusions This meta-analysis suggested that treatment of ME secondary to RVO with anti-VEGF improves visual and anatomical outcomes compared with other treatments. Thus, anti-VEGF treatment is the first choice for treating patients with ME secondary to RVO.

Based on the data of 85 cities in the three major urban agglomerations in the Yangtze River Economic Belt from 2011 to 2020, this paper measures the level of high-quality economic development of each city through the improved entropy-weighted TOPSIS method and uses the kernel density estimation method, Dagum Gini coefficient, and convergence model to further study the dynamic evolution, regional differences, and convergence state of the high-quality economic development level of the three major urban agglomerations in the Yangtze River Economic Belt. The study finds that: 1) the high-quality economic development level of each city within the three major urban agglomerations of the Yangtze River Economic Belt has been continuously improved over time, but the development level of each urban agglomeration is not the same; 2) the high-quality economic development level of the three urban agglomerations in the Yangtze River Economic Belt has spatial differences, that is, the high-quality economic development level of the Yangtze River Delta urban agglomeration is better than the two urban agglomerations; 3) the σ convergence and β convergence characteristics exist in the three major urban agglomerations as a whole as well as in each urban agglomeration, and the β convergence rates of the urban agglomerations are not consistent. On the whole, the convergence rate of the middle reaches of the Yangtze River urban agglomeration is the fastest, followed by the Chengdu-Chongqing urban agglomeration, and the Yangtze River Delta urban agglomeration is the slowest. Finally, based on the findings of the study, targeted policy recommendations to promote the high-quality economic development of the three major urban agglomerations in the Yangtze River Economic Belt are proposed.

Tangzhiqing formula (TZQ) is a traditional Chinese medicine prescribed to treat glucose and lipid metabolism disorders. A significant effect of TZQ on diabetes and hyperlipidemia has been demonstrated, but its effect on atherosclerosis (AS) remains unknown. This study combines pyroptosis with metabolomics to elucidate the effect and mechanism of TZQ on AS. A model of AS was developed using ApoE−/− mice fed a high-fat diet for 8 weeks. After 6 weeks of atorvastatin (Ator) or TZQ treatment, aortic lumen diameter, aortic lesion size, serum lipid profile, cytokines, and Nod-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis were analyzed. Serum metabolomics profiles were obtained to examine the effect of TZQ on AS and the correlation between pyroptosis and metabolites was further analyzed. As a result, TZQ significantly reduced the diameter of the common carotid artery during diastole and the blood flow velocity in the aorta during systole; reduced blood lipid levels, arterial vascular plaques, and the release of inflammatory cytokines; and inhibited the NLRP3 inflammasome-mediated pyroptosis. According to metabolomics profiling, TZQ is engaged in the treatment of AS via altering arachidonic acid metabolism, glycerophospholipid metabolism, steroid hormone production, and unsaturated fatty acid biosynthesis. The cytochrome P450 enzyme family and cyclooxygenase 2 (COX-2) are two major metabolic enzymes associated with pyroptosis.

Intestinal macrophages are the main participants of intestinal immune homeostasis and intestinal inflammation. Under different environmental stimuli, intestinal macrophages can be polarized into classical activated pro-inflammatory phenotype (M1) and alternative activated anti-inflammatory phenotype (M2). Its different polarization state is the “guide” to promoting the development and regression of inflammation. Under normal circumstances, intestinal macrophages can protect the intestine from inflammatory damage. However, under the influence of some genetic and environmental factors, the polarization imbalance of intestinal M1/M2 macrophages will lead to the imbalance in the regulation of intestinal inflammation and transform the physiological inflammatory response into pathological intestinal injury. In UC patients, the disorder of intestinal inflammation is closely related to the imbalance of intestinal M1/M2 macrophage polarization. Therefore, restoring the balance of M1/M2 macrophage polarization may be a potentially valuable therapeutic strategy for UC. Evidence has shown that traditional Chinese medicine (TCM) has positive therapeutic effects on UC by restoring the balance of M1/M2 macrophage polarization. This review summarizes the clinical evidence of TCM for UC, the vital role of macrophage polarization in the pathophysiology of UC, and the potential mechanism of TCM regulating macrophage polarization in the treatment of UC. We hope this review may provide some new enlightenment for the clinical treatment, fundamental research, and research and development of new Chinese medicine of UC.

As a primary cause of death in patients with hematological malignancies and transplant recipients, invasive aspergillosis (IA) is a condition that warrants attention. IA infections have been increasing, which remains a significant cause of morbidity and mortality in immunocompromised patients. During the past decade, antifungal drug resistance has emerged, which is especially concerning for management given the limited options for treating azole-resistant infections and the possibility of failure of prophylaxis in those high-risk patients. Histone posttranslational modifications (HPTMs), mainly including acetylation, methylation, ubiquitination and phosphorylation, are crucial epigenetic mechanisms regulating various biological events, which could modify the conformation of histone and influence chromatin-associated nuclear processes to regulate development, cellular responsiveness, and biological phenotype without affecting the underlying genetic sequence. In recent years, fungi have become important model organisms for studying epigenetic regulation. HPTMs involves in growth and development, secondary metabolite biosynthesis and virulence in Aspergillus. This review mainly aims at summarizing the acetylation, deacetylation, methylation, demethylation, and sumoylation of histones in IA and connect this knowledge to possible HPTMs-based antifungal drugs. We hope this research could provide a reference for exploring new drug targets and developing low-toxic and high-efficiency antifungal strategies.

Abstract Background Lung cancer is one of the common malignant tumors worldwide with high incidence and mortality. MicroRNA-423-3p (miR-423-3p) acts as an oncogene in several types of cancers. The aim of this study is to reveal the clinical significance and biological function of miR-423-3p in lung cancer. Methods The expression of miR-423-3p was detected in lung cancer specimens by reverse transcription-quantitative polymerase chain reaction (qRT-PCR) assay. Kaplan-Meier survival and Cox regression analyses were used to investigate the prognostic significance of miR-423-3p in lung cancer. CCK-8 and Transwell assays were used to determine the functional role of miR-423-3p in lung cancer. Results We observed that miR-423-3p was significantly upregulated in lung cancer tissues and cell lines. Overexpression of miR-423-3p was significantly associated with lymph node metastasis, TNM stage, and poor prognosis. Multivariate Cox regression analysis results showed that miR-423-3p was an independent prognostic indicator for lung cancer patients. Results of functional analyses revealed that overexpression of miR-423-3p promoted cell proliferation, migration, and invasion in lung cancer cells. Conclusions These results indicated that miR-423-3p acts as an oncogene and promotes cell proliferation migration, and invasion of lung cancer. And miR-423-3p may serve as a potential prognostic biomarker and therapeutic target for the treatment of lung cancer.

13a-(S)-3-pivaloyloxyl-6,7-dimethoxyphenanthro(9,10-b)-indolizidine (CAT3) is a novel oral anti-glioma pro-drug with a potent anti-tumor effect against temozolomide-resistant glioma. 13a(S)-3-hydroxyl-6,7-dimethoxyphenanthro(9,10-b)-indolizidine (PF403) is the active in vivo lipase degradation metabolite of CAT3. Both CAT3 and PF403 can penetrate the blood–brain barrier to cause an anti-glioma effect. However, PF403, which is produced in the gastrointestinal tract and plasma, causes significant gastrointestinal side effects, limiting the clinical application of CAT3. The objective of this paper was to propose a metabolism modification for CAT3 using a self-microemulsifying drug delivery system (SMEDDS), in order to reduce the generation of PF403 in the gastrointestinal tract and plasma, as well as increase the bioavailability of CAT3 in vivo and the amount of anti-tumor substances in the brain. Thus, a CAT3-loaded self-microemulsifying drug delivery system (CAT3-SMEDDS) was prepared, and its physicochemical characterization was systematically carried out. Next, the pharmacokinetic parameters of CAT3 and its metabolite in the rats’ plasma and brain were measured. Furthermore, the in vivo anti-glioma effects and safety of CAT3-SMEDDS were evaluated. Finally, Caco-2 cell uptake, MDCK monolayer cellular transfer, and the intestinal lymphatic transport mechanisms of SMEDDS were investigated in vitro and in vivo. Results show that CAT3-SMEDDS was able to form nanoemulsion droplets in artificial gastrointestinal fluid within 1 min, displaying an ideal particle size (15–30 nm), positive charge (5–9 mV), and controlled release behavior. CAT3-SMEDDS increased the membrane permeability of CAT3 by 3.9-fold and promoted intestinal lymphatic transport. Hence, the bioavailability of CAT3 was increased 79% and the level of its metabolite, PF403, was decreased to 49%. Moreover, the concentrations of CAT3 and PF403 were increased 2–6-fold and 1.3–7.2-fold, respectively, in the brain. Therefore, the anti-glioma effect in the orthotopic models was improved with CAT3-SMEDDS compared with CAT3 in 21 days. Additionally, CAT3-SMEDDS reduced the gastrointestinal side effects of CAT3, such as severe diarrhea, necrosis, and edema, and observed less inflammatory cell infiltration in the gastrointestinal tract, compared with the bare CAT3. Our work reveals that, through the metabolism modification effect, SMEDDS can improve the bioavailability of CAT3 and reduce the generation of PF403 in the gastrointestinal tract and plasma. Therefore, it has the potential to increase the anti-glioma effect and reduce the gastrointestinal side effects of CAT3 simultaneously.

In this paper, it uses the technology of coagulation sedimentation – hydrolytic acidification – contact oxidation in treating the jujube processing wastewater, which makes membrane forming on the combined filler in reactor successfully by adopting domestic wastewater, and then dilutes jujube processing wastewater with domestic wastewater according to certain proportion to realize acclimatization of the biological membrane and complete starting of the reactor, and finally completes the biochemical treatment test of the jujube processing wastewater diluted by the feed water via backflow of the yielding water. The yielding water is stable during the test operation, and it has greater removal efficiency as to CODcr and ammonia nitrogen. Jujube is full of nutriments, has wide varieties, and possesses edible value, healthy-keeping value and medical value. Jujube processing has a long history in our country, there are numerous jujube processing enterprises, and the jujube is mainly processed to dried jujube, preserved fruit, jujube vinegar, jujube chips, jujube milk and jujube wine, etc. However, it has a greater difficulty to treat the wastewater generated in the jujube processing process, and the wastewater is one of the wastewaters with serious pollution in the food industry (Xu et al., 2014; Bi et al., 2010; Chen and Li, 2008; Wang et al., 2013). This test uses the technology of coagulation sedimentation – hydrolytic acidification – contact oxidation in treating the jujube processing wastewater, and achieves certain effects.

With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease, so we propose a new hybrid quantum neural network with signal feature overlay projection (HQNN-SFOP), which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal, introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features, and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features, thus improving the accuracy and migration generalization ability of drone detection. In order to validate the effectiveness of the proposed method, we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment. The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB. On the time-domain feature data set, HQNN-SFOP obtains the highest accuracy compared to other conventional methods. In addition, HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions. Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing, it provides a highly efficient method for the drone detection using radar return signals. [ABSTRACT FROM AUTHOR]

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST) with Program Dependence Graphs (PDG). Furthermore, we introduce a Tree-Graph-Gated- Attention (TGGA) network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection (HCRVD) system. This system enables cross-language vulnerability detection at the function-level. The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities. It benefits from the hierarchical code representation learning method, and outperforms baseline in cross-language vulnerability detection by 9.772% and 11.819% in the C/C++ and Java datasets, respectively. Moreover, HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects. HCRVD shows good validity, generality and practicality. [ABSTRACT FROM AUTHOR]