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The proper selection of a relevant mixer generally requires an effective assessment of several models against the application requirements. This is a complex task, as traditional evaluation methods generally focus only on a single aspect of performance, such as pressure loss, mixing characteristics, or heat transfer. This study assesses a urea-based selective catalytic reduction (SCR) system installed on a ship, where the installation space is limited and the distance between the urea aqueous solution injection position and the reactor is low; therefore, the static mixer installed in this pipeline has special performance requirements. In particular, four evaluation indices are used in this study: The B value, C value, pressure loss correction factor (Z′), and the ratio of the required distance to the equivalent diameter of the pipe (LV/D) when the velocity field after the mixer attains uniformity. Six types of static mixers were simulated with varying concentrations, flow speeds, and positions. A fuzzy comprehensive evaluation method was introduced to evaluate and compare the related advantages and disadvantages. The results showed that 1) mixing performance was related to the shape of the mixer and had no direct relationship with flow velocity. 2) For the same mixer position, the lower the urea concentration, the greater the difficulty of evenly mixing the solution. 3) At a constant urea concentration, the mixing performance improved when the mixer was closer to the injection inlet. 4) The installation of a GK mixer in the SCR system of a 9L20C diesel engine was best. [ABSTRACT FROM AUTHOR]

Background: The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. Methods: By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. Results: Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. Conclusions: In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC. [ABSTRACT FROM AUTHOR]

The urban spatial structure is a key feature of the distribution of social and economic resources. The spatial structure of an urban agglomeration is an abstract relationship expression of urbanization. Urban agglomerations develop for multiple reasons, including urban planning and natural evolution. To date, most research related to urban agglomeration has been based on single data source, which is a limitation. This research aims to propose a spatial structure identification method for urban agglomerations via a complex network based on nighttime light data and railway data. Firstly, we extracted the urban built-up area using defense meteorological satellite program/operational line scanner (DMSP/OLS) data, and divided it into urban objects to obtain the nighttime light urban network (NLUN) by borough. Secondly, we aggregated railway stations at municipal level using railway operation data to obtain the railway urban network (RUN). Following this, we established a composite urban network (CUN) consisting of the NLUN and the RUN based on the composite adjacency matrix. Finally, the Louvain algorithm and the comprehensive strength index (CSI) were used to detect the communities and central nodes of the CUN and obtain the urban agglomerations and core cities. The results show that urban agglomeration identification based on the CUN has the best accuracy, which is 5.72% and 15.94% higher than that of the NLUN and RUN, respectively. Core cities in the urban agglomeration identified by the CSI in the CUN are at least 3.04% higher than those in the single-source urban network. In addition, the distribution pattern of Chinese urban agglomerations in the study area is expressed as "three vertical", and the development level of urban agglomeration shows an unbalanced trend. [ABSTRACT FROM AUTHOR]

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit.  Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128., (©2024 American Association for Cancer Research.)

Background : The coronavirus disease 2019 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 210 million individuals globally and resulted in over 4 million deaths since the first report in December 2019. The early use of traditional Chinese medicine (TCM) for light and ordinary patients, can rapidly improve symptoms, shorten hospitalization days and reduce severe cases transformed from light and normal. Many TCM formulas and products have a wide application in treating infectious and non-infectious diseases. Polygonum cuspidatum Sieb. et Zucc. ( P. cuspidatum ), is an important Traditional Chinese Medicine with actions of clearing away heat and eliminating dampness, draining the gallbladder to relieve jaundice, removing blood stasis to alleviate pain, resolving phlegm and arrest cough. In the search for anti-SARS-CoV-2, P. cuspidatum was recommended as as a therapeutic drug of COVID-19 pneumonia.In this study, we aimed to identifies P. cuspidatum is the potential broad-spectrum inhibitor for the treatment of coronaviruses infections. Methods : In the present study , we infected human malignant embryonal rhabdomyoma (RD) cells with the OC43 strain of the coronavirus, which represent an alternative model for SARS-CoV-2 and then employed the cell viability assay kit for the antiviral activity. We combined computer aided virtual screening to predicte the binding site and employed Surface plasmon resonance analysis (SPR) to comfirm the interaction between drugs and coronavirus. We employed fluorescence resonance energy transfer technology to identify drug's inhibition in the proteolytic activity of 3CLpro and Plpro. Results : Based on our results, polydatin and resveratrol derived from P. cuspidatum significantly suppressed HCoV-OC43 replication. 50% inhibitory concentration (IC 50 ) values of polydatin inhibited SARS-CoV-2 Mpro and Plpro, MERS Mpro and Plpro were 18.66, 125, 14.6 and 25.42 μm, respectively. IC 50 values of resveratrol inhibited SARS-CoV-2 Mpro and Plpro, MERS Mpro and Plpro were 29.81 ,60.86, 16.35 and19.04 μM, respectively. Finally, SPR assay confirmed that polydatin and resveratrol had high affinity to SARS-CoV-2, SARS-CoV 3Clpro, MERS-CoV 3Clpro and PLpro protein. Conclusions : we identified the antiviral activity of flavonoids polydatin and resveratrol on RD cells. Polydatin and resveratrol were found to be specific and selective inhibitors for SARS-CoV-2, 3CLpro and PLpro, viral cysteine proteases. In summary, this study identifies P. cuspidatum as the potential broad-spectrum inhibitor for the treatment of coronaviruses infections., (© 2021 The Author(s). Published by BRI.)