Exploration of the mechanism of Qi-Xian decoction in asthmatic mice using metabolomics combined with network pharmacology.

Introduction: Qi-Xian Decoction (QXD), a traditional Chinese medicine (TCM) formula consisting of eight herbs, has been clinically used to treat asthma. However, the underlying mechanisms have not been completely elucidated. This study aimed to combine metabolomics and network pharmacology to reveal the mechanism of action of QXD in asthma treatment. Methods: An ovalbumin (OVA)-induced asthma mouse model was constructed to evaluate the therapeutic effects of QXD. Serum metabolomics and network pharmacology were combined to study the mechanism of anti-asthma action as well as the potential target, and related biological functions were validated. Results: The QXD treatment has demonstrated significant protective effects in OVA-induced asthmatic mice, as evidenced by its ability to inhibit inflammation, IgE, mucus overproduction, and airway hyperreactivity (AHR). Metabolomic analysis has revealed a total of 140 differential metabolites associated with QXD treatment. In addition, network pharmacology has identified 126 genes that are linked to the effects of QXD, including TNF, IL-6, IL1ß, STAT3, MMP9, EGFR, JUN, CCL2, TLR4, MAPK3 and MAPK8. Through comprehensive gene-metabolite interaction network analysis, seven key metabolites have been identified and associated with the potential anti-asthmatic effect of QXD, with palmitic acid (PA) being the most notable among them. In vitro validation studies have confirmed the gene-metabolite interaction involving PA, IL-6, and MAPK8. Furthermore, our research has demonstrated that QXD treatment can effectively inhibit PA-promoted IL-6 expression in MH-S cells and reduce PA concentration in OVA-induced asthmatic mice. Conclusion: The regulation of metabolic pathways by QXD was found to be associated with its anti-asthmatic action, which provides insight into the mechanism of QXD in treating asthma.

Predicting the formation of mixed pattern hemorrhages in ruptured middle cerebral artery aneurysms based on a decision tree model: A multicenter study.

OBJECTIVE: Mixed-pattern hemorrhages (MPH) commonly occur in ruptured middle cerebral artery (MCA) aneurysms and are associated with poor clinical outcomes. This study aimed to predict the formation of MPH in a multicenter database of MCA aneurysms using a decision tree model. METHODS: We retrospectively reviewed patients with ruptured MCA aneurysms between January 2009 and June 2020. The MPH was defined as subarachnoid hemorrhages with intracranial hematomas and/or intraventricular hemorrhages and/or subdural hematomas. Univariate and multivariate logistic regression analyses were used to explore the prediction factors of the formation of MPH. Based on these prediction factors, a decision tree model was developed to predict the formation of MPH. Additional independent datasets were used for external validation. RESULTS: We enrolled 436 patients with ruptured MCA aneurysms detected by computed tomography angiography; 285 patients had MPH (65.4%). A multivariate logistic regression analysis showed that age, aneurysm size, multiple aneurysms, and the presence of a daughter dome were the independent prediction factors of the formation of MPH. The areas under receiver operating characteristic curves of the decision tree model in the training, internal, and external validation cohorts were 0.951, 0.927, and 0.901, respectively. CONCLUSION: Age, aneurysm size, the presence of a daughter dome, and multiple aneurysms were the independent prediction factors of the formation of MPH. The decision tree model is a useful visual triage tool to predict the formation of MPH that could facilitate the management of unruptured aneurysms in routine clinical work.

Advanced nutrient removal and external sludge reduction: Demonstration in a pilot-scale sequencing batch reactor.

Although the addition of excess sludge fermentation products to improve nutrient removal from sewage is cost-effective, its application has rarely been demonstrated. In this study, the external sludge was first collected and fermented under a sludge retention time of 10 days, then introduced into SBR with a 1:15 sewage ratio. The results revealed a gradual increase in the nitrite accumulation ratio to 34.7% in the SBR at the end of the oxic stage after 64 days of adding fermented sludge products. In addition, the average effluent total nitrogen and phosphorous decreased to 7.3 and 0.5 mg/L, corresponding to removal efficiencies of 86.7% and 95.5%, respectively. On the other hand, the use of the fermented sludge products as external organic carbon sources in the SBR increased the external sludge reduction ratio to 42.5%. High-throughput sequencing demonstrated that the increase in the endogenous denitrifier community, polyphosphate-accumulating organisms, and fermentation bacteria were the main factors contributing to the increase in nutrient removal and excess sludge reduction. The economic evaluation indicated that the operational cost of the pilot-scale system saves 0.011$/m3 of sewage treated. PRACTITIONER POINTS: Fermented sludge addition effectively enhanced nutrient removal in pilot-scale SBR. Average effluent TN and PO4 3- -P decreased to 7.3 and 0.5 mg/L, respectively. Highest external sludge reduction rate was 42.5% in pilot-scale reactor. Sewage treatment cost can save 0.011$/m3 under advanced nutrient removal.

Has China's Low-Carbon City Construction Enhanced the Green Utilization Efficiency of Urban Land?

China has implemented the low-carbon city pilot (LCCP) policy in the hopes of efficiently limiting carbon emission intensity to combat global warming and promote green economic growth. Urban land utilization, the second-largest source of carbon emissions, is key to the LCCP policy being able to have the desired effect, which has attracted widespread attention. Based on the panel data from prefecture-level cities in China from 2006 to 2019, this study used the propensity score matching difference-in-differences method (PSM-DID) to examine the impacts of LCCP policy on green utilization efficiency of urban land (GUEUL). The results reveal that LCCP policy has a beneficial impact on GUEUL and can effectively boost the future possibilities of green and low-carbon city development. Due to variances in regional economic and resource endowment level, the impacts of LCCP are different. The pilot has pushed GUEUL in the eastern region, western region, and growing resource-based cities, but has failed to improve GUEUL in other regions. Policymakers should adhere to the long-term sustainability of the LCCP policy and adopt differentiated action strategies to promote GUEUL when implementing it in different regions.

Rupture Risk Assessment for Anterior Communicating Artery Aneurysms Using Decision Tree Modeling.

Background: Although anterior communicating artery (ACoA) aneurysms have a higher risk of rupture than aneurysms in other locations, whether to treat unruptured ACoA aneurysms incidentally found is a dilemma because of treatment-related complications. Machine learning models have been widely used in the prediction of clinical medicine. In this study, we aimed to develop an easy-to-use decision tree model to assess the rupture risk of ACoA aneurysms. Methods: This is a retrospective analysis of rupture risk for patients with ACoA aneurysms from two medical centers. Morphologic parameters of these aneurysms were measured and evaluated. Univariate analysis and multivariate logistic regression analysis were performed to investigate the risk factors of aneurysm rupture. A decision tree model was developed to assess the rupture risk of ACoA aneurysms based on significant risk factors. Results: In this study, 285 patients were included, among which 67 had unruptured aneurysms and 218 had ruptured aneurysms. Aneurysm irregularity and vessel angle were independent predictors of rupture of ACoA aneurysms. There were five features, including size ratio, aneurysm irregularity, flow angle, vessel angle, and aneurysm size, selected for decision tree modeling. The model provided a visual representation of a decision tree and achieved a good prediction performance with an area under the receiver operating characteristic curve of 0.864 in the training dataset and 0.787 in the test dataset. Conclusion: The decision tree model is a simple tool to assess the rupture risk of ACoA aneurysms and may be considered for treatment decision-making of unruptured intracranial aneurysms.

Epidemiological Analysis From 2018 to 2020 in China and Prevention Strategy of Porcine Circovirus Type 2.

Porcine circovirus type 2 (PCV2) is one of the smallest known animal viruses and is the main pathogen of PCV-associated diseases (PCVAD). Epidemiological surveillance results have shown that the PCV2 infection rate is on the rise in China, thus, PCV2 disease prevention and control has become a huge challenge for the Chinese swine industry. We collected clinical samples from multiple different provinces in China from 2018 to 2020 and found that the positive rate of PCV2 was 53% (3619/6872), identity between the cloned 62 ORF2 genes was 84.4-100% and identity between the cloned 62 ORF2 sequences and reference sequence was 72.9-99.8%. Genetic evolution analysis found that PCV2d accounted for 79% (49/62 samples), PCV2a for 12.9% (8/62 samples), PCV2b for 8% (5/62 samples), and PCV2c and PCV2e genotypes were not found. However, most commercial PCV2 subunit vaccines are based on the PCV2a genotype, and there are very few vaccines based on PCV2b or PCV2d. Therefore, the homologous and heterologous protection ability of PCV2b and PCV2d Cap proteins based on the baculovirus against the PCV2b and PCV2d infections was evaluated, which is expected to design and develop excellent PCV2 protein vaccine candidates. This study found that both PCV2b and PCV2d Cap proteins can increase the level of humoral immunity and cellular immune response in mice. Importantly, both PCV2b and PCV2d cap proteins can provide homologous and heterologous protection against the PCV2b and PCV2d viruses. Overall, this study provides a reference for the prevention and control of PCVAD in mainland China and the development of PCV2 vaccines.

Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning.

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Effects of Angiotensin II Receptor Blockers and ACE (Angiotensin-Converting Enzyme) Inhibitors on Virus Infection, Inflammatory Status, and Clinical Outcomes in Patients With COVID-19 and Hypertension: A Single-Center Retrospective Study.

With the capability of inducing elevated expression of ACE2 (angiotensin-converting enzyme 2), the cellular receptor for severe acute respiratory syndrome coronavirus 2, angiotensin II receptor blockers (ARBs) or ACE inhibitors treatment may have a controversial role in both facilitating virus infection and reducing pathogenic inflammation. We aimed to evaluate the effects of ARBs/ACE inhibitors on coronavirus disease 2019 (COVID-19) in a retrospective, single-center study. One hundred twenty-six patients with COVID-19 and preexisting hypertension at Hubei Provincial Hospital of Traditional Chinese Medicine in Wuhan from January 5 to February 22, 2020, were retrospectively allocated to ARBs/ACE inhibitors group (n=43) and non-ARBs/ACE inhibitors group (n=83) according to their antihypertensive medication. One hundred twenty-five age- and sex-matched patients with COVID-19 without hypertension were randomly selected as nonhypertension controls. In addition, the medication history of 1942 patients with hypertension that were admitted to Hubei Provincial Hospital of Traditional Chinese Medicine from November 1 to December 31, 2019, before the COVID-19 outbreak were also reviewed for external comparison. Epidemiological, demographic, clinical, and laboratory data were collected, analyzed, and compared between these groups. The frequency of ARBs/ACE inhibitors usage in patients with hypertension with or without COVID-19 were comparable. Among patients with COVID-19 and hypertension, those received either ARBs/ACE inhibitors or non-ARBs/ACE inhibitors had comparable blood pressure. However, ARBs/ACE inhibitors group had significantly lower concentrations of hs-CRP (high-sensitivity C-reactive protein; P=0.049) and PCT (procalcitonin, P=0.008). Furthermore, a lower proportion of critical patients (9.3% versus 22.9%; P=0.061) and a lower death rate (4.7% versus 13.3%; P=0.216) were observed in ARBs/ACE inhibitors group than non-ARBs/ACE inhibitors group, although these differences failed to reach statistical significance. Our findings thus support the use of ARBs/ACE inhibitors in patients with COVID-19 and preexisting hypertension.

Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling.

BACKGROUND: Despite advances in the treatment of poor-grade aneurysmal subarachnoid hemorrhage (aSAH), predicting the long-term outcome of aSAH remains challenging, although essential. OBJECTIVE: To predict long-term outcomes after poor-grade aSAH using decision tree modeling. METHODS: This was a retrospective analysis of a prospective multicenter observational registry of patients with poor-grade aSAH with a World Federation of Neurosurgical Societies (WFNS) grade IV or V. Outcome was assessed by the modified Rankin Scale (mRS) at 12 mo, and an unfavorable outcome was defined as an mRS of 4 or 5 or death. Long-term prognostic models were developed using multivariate logistic regression and decision tree algorithms. An additional independent testing dataset was collected for external validation. Overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curves (AUC) were used to assess model performance. RESULTS: Of the 266 patients, 139 (52.3%) had an unfavorable outcome. Older age, absence of pupillary reactivity, lower Glasgow coma score (GCS), and higher modified Fisher grade were independent predictors of unfavorable outcome. Modified Fisher grade, pupillary reactivity, GCS, and age were used in the decision tree model, which achieved an overall accuracy of 0.833, sensitivity of 0.821, specificity of 0.846, and AUC of 0.88 in the internal test. There was similar predictive performance between the logistic regression and decision tree models. Both models achieved a high overall accuracy of 0.895 in the external test. CONCLUSION: Decision tree model is a simple tool for predicting long-term outcomes after poor-grade aSAH and may be considered for treatment decision-making.