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B-cell subsets in peripheral blood (PB) and tumor microenvironment (TME) were evaluated to determine myasthenia gravis (MG) severity in patients with thymoma-associated MG (TMG) and the distribution of B cells in type B TMG. The distribution of mature B cells, including Bm1–Bm5, CD19+ and CD20+ B cells and non-switched (NSMBCs) and switched (SMBCs) memory B cells, were determined in 79 patients with thymoma or TMG. Quantitative relationships between the T and TMG groups and the TMG-low and TMG-high subgroups were determined. NSMBCs and SMBCs were compared in TME and PB. Type B thymoma was more likely to develop into MG, with types B2 and B3 being especially associated with MG worsening. The percentage of CD19+ B cells in PB gradually increased, whereas the percentage of CD20+ B cells and the CD19/CD20 ratio were not altered. The (Bm2 + Bm2′)/(eBm5 + Bm5) index was significantly higher in the TMG-high than in thymoma group. The difference between SMBC/CD19+ and NSMBC/CD19+ B cell ratios was significantly lower in the thymoma than TMG group. NSMBCs assembled around tertiary lymphoid tissue in thymomas of patients with TMG. Few NSMBCs were observed in patients with thymoma alone, with these cells being diffusely distributed. MG severity in patients with TMG can be determined by measuring CD19+ B cells and Bm1-Bm5 in PB. The CD19/CD20 ratio is a marker of disease severity in TMG patients. Differences between NSMBCs and SMBCs in PB and TME of thymomas can synergistically determine MG severity in patients with TMG. [ABSTRACT FROM AUTHOR]

In order to study the theoretical mechanism of the impact of green technology progress on carbon emissions, this article constructs a theoretical mechanism of the impact of green technology progress on carbon emission growth. Explore the conditions for achieving carbon peak and carbon reduction. Based on the Cobb Douglas production function, construct a three sector model that includes capital, labor, and energy. Empirical methods were used to analyze the quantitative impact of green technology progress on carbon emission growth and the moderating effect of energy input share. This study mainly used provincial panel data from 1995 to 2020. Calculate carbon dioxide emissions based on energy consumption and carbon dioxide emission coefficients of various energy sources in different regions. Using the perpetual inventory method to calculate capital growth rate, green computing progress rate, etc., to provide data support for the green technology carbon reduction model. Empirical analysis of the impact of green technology progress on carbon emissions using the FGLS panel model. Theoretical and empirical analyses show that green technological progress promotes an increase in the carbon emission growth rate through the scale effect, with an impact coefficient of 0.607; it promotes a decrease in the carbon emission growth rate through the technological effect, with an impact coefficient of − 0.667; the combined effect promotes a decrease in growth rate of carbon emissions, with an impact coefficient of − 0.06. The share of energy inputs has a positive regulating effect on the scale effect. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic provides an opportunity to investigate how a new and long-lasting threat affects public risk perception and social distancing behavior, which is important for pandemic risk management and recovery of the tertiary industry. We have found that the mechanism that perception decides behavior changes over time. At the beginning of the pandemic, risk directly shapes people's willingness of going out. But under a persistent threat, perception no longer plays the direct role of shape people's willingness. Instead, perception indirectly influences the willingness by shaping people's judgment about the necessity of traveling. Switching from direct to indirect influence, perception's effect is enlarged, which partially prevents people from returning to normal life even if the governmental ban is removed in a zero-COVID community. [ABSTRACT FROM AUTHOR]

Magnesium silicate is an inorganic compound used as an ingredient in product formulations for many different purposes. Since its compatibility with other components is critical for product quality and stability, it is essential to characterize the integrity of magnesium silicate in different solutions used for formulations. In this paper, we have determined the magnitude of dissociation of synthetic magnesium silicate in solution with positively charged, neutral, and negatively charged compounds using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS). The EDS results were verified through Monte Carlo simulations of electron-sample interactions. The compounds chosen for this study were positively charged cetylpyridinium chloride (CPC), neutral lauryl glucoside, and negatively charged sodium cocoyl glutamate and sodium cocoyl glycinate since these are common compounds used in personal care and oral care formulations. Negatively charged compounds significantly impacted magnesium silicate dissociation, resulting in physio-chemical separation between magnesium and silicate ions. In contrast, the positively charged compound had a minor effect on dissociation due to ion competition, and the neutral compound did not have such an impact on magnesium silicate dissociation. Further, when the magnesium ions are dissociated from the synthetic magnesium silicate, the morphology is changed accordingly, and the structural integrity of the synthetic magnesium silicate is damaged. The results provide scientific confidence and guidance for product development using synthetic magnesium silicate. [ABSTRACT FROM AUTHOR]

Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered by high loss-to-follow-up rates. Novel methods for the automated, real-time diagnosis of AIN 2+ could enable "see and treat" strategies, reducing loss-to-follow-up rates. A previous retrospective study demonstrated that the accuracy of a high-resolution microendoscope (HRME) coupled with a deep learning model was comparable to expert clinical impression for diagnosis of AIN 2+ (sensitivity 0.92 [P = 0.68] and specificity 0.60 [P = 0.48]). However, motion artifacts and noise led to many images failing quality control (17%). Here, we present a high frame rate HRME (HF-HRME) with improved image quality, deployed in the clinic alongside a deep learning model and evaluated prospectively for detection of AIN 2+ in real-time. The HF-HRME reduced the fraction of images failing quality control to 4.6% by employing a high frame rate camera that enhances contrast and limits motion artifacts. The HF-HRME outperformed the previous HRME (P < 0.001) and clinical impression (P < 0.0001) in the detection of histopathologically confirmed AIN 2+ with a sensitivity of 0.91 and specificity of 0.87. [ABSTRACT FROM AUTHOR]

Cell specific-targeted therapy (CSTT) for acute ischemic stroke remains underdeveloped. Cerebrovascular endothelial cells (CECs) are key components of the blood–brain barrier and are the first brain cells affected by ischemic stroke. After stroke, CEC injury causes insufficient energy supply to neurons and leads to cytotoxic and vasogenic brain edema. Aptamers are short single-stranded RNA or DNA molecules that can bind to specific ligands for cell specific delivery. The expression of vascular cell adhesion molecule-1 (VCAM-1) is increased on CECs after stroke. Herein, we report that an RNA-based VCAM-1-aptamer can specifically target CECs in stroke brains following transient middle cerebral artery occlusion in mice. Our data demonstrate the potential of an RNA-based aptamer as an effective delivery platform to target CECs after stroke. We believe this method will allow for the development of CSTT for treatment of patients with stroke. [ABSTRACT FROM AUTHOR]