Grand Canonical Adaptive Resolution Simulation for Molecules with Electrons: A Theoretical Framework based on Physical Consistency

A theoretical scheme for the treatment of an open molecular system with electrons and nuclei is proposed. The idea is based on the Grand Canonical description of a quantum region embedded in a classical reservoir of molecules. Electronic properties of the quantum region are calculated at constant electronic chemical potential equal to that of the corresponding (large) bulk system treated at full quantum level. Instead, the exchange of molecules between the quantum region and the classical environment occurs at the chemical potential of the macroscopic thermodynamic conditions. T he Grand Canonical Adaptive Resolution Scheme is proposed for the treatment of the classical environment; such an approach can treat the exchange of molecules according to first principles of statistical mechanics and thermodynamic. The overall scheme is build on the basis of physical consistency, with the corresponding definition of numerical criteria of control of the approximations implied by the coupling. Given the wide range of expertise required, this work has the intention of providing guiding principles for the construction of a well founded computational protocol for actual multiscale simulations from the electronic to the mesoscopic scale.Comment: Computer Physics Communications (2017), in pres

Primers(5′-3′) and Targets Used for the Detection of Human Herpes Viruses in the Study.

<p>Primers(5′-3′) and Targets Used for the Detection of Human Herpes Viruses in the Study.</p

Demographic and aetiological data.

<p>Demographic and aetiological data.</p

Association of clinical signs in the FUO patients (n = 184) with the viruses detected.

<p>Association of clinical signs in the FUO patients (n = 184) with the viruses detected.</p

Distribution of HHVs among different age groups in this study.

<p>Distribution of HHVs among different age groups in this study.</p

Table12_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.XLSX

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Table1_Case Report: Combined transcatheter arterial embolization and aortic stent-graft have better efficacy for bronchial artery aneurysms.docx

Bronchial artery aneurysm (BAA) is a rare and fatal condition that requires immediate treatment. However, conventional surgical and transcatheter arterial embolization treatments are less effective. In the present case, a 76-year-old hypertensive woman was admitted with dizziness and diagnosed with an unruptured bronchial artery aneurysm, which was treated by transcatheter arterial embolization and aortic stent-graft. The patient's clinical status was stable during the 4-year follow-up. Simultaneously, we reviewed 79 research papers, analyzing past BAA cases for their etiology, symptoms, and treatment outcomes. We found that catheter arterial embolization and aortic stent-graft implantation, especially for BAA of short-necked and arterial tortuosity, demonstrate superior efficacy compared to other methods. Therefore, we consider this approach to be the preferred choice in clinical BAA treatment.</p

Table3_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.XLSX

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Image1_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.TIF

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p

Image3_A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.TIF

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited.Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts.Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.</p