This study aims to solve the credit problems in the supply chain commodity and currency circulation links from the perspective of the ledger, while the game model method has been adopted. The research firstly reviews the relationship between distributed ledger technology and the essential functions of currency. Then, by constructing two-agent single-period and multi-period game models in the entire supply chain, the researchers analysed the incentive mechanism and equilibrium solution of distributed nodes of Central Bank Digital Currency (CBDC). The results of this study include the incentive mechanism and optimization of distributed nodes based on licensed distributed ledger technology, which is an important issue that CBDC faces when performing currency functions. The implications of this study mainly cover the limitations of the underlying technology of the public chain and its reward mechanism in the supply chain management and provide support for the rationality of the CBDC issuance mechanism based on state-owned commercial banks, which provides a reference for the CBDC practice. The main value of the research not only serves the decision-making department of the CBDC issuance but also provides ideas on the operation mode of digital currency for the field of digital currency research.Nowadays, the health level of residents has become the focus of people's attention. Under the background of the development of health service from "disease-centered" to "health-centered," it is very important to improve the level of urban health and clarify the factors affecting urban health. Therefore, this paper quantifies the relationship between residents' health literacy level and environment, average life expectancy, infectious disease mortality, and other indicators by selecting appropriate indicators and establishing a mathematical model. Based on the reciprocal linear combination of the collected index data and the corresponding health level value, the prediction model of social health literacy level (SPM) was established, and the qualitative prediction and quantitative analysis of citizens' health literacy level were studied in depth. Based on the SPM model, we can roughly predict the level of health literacy in a region only based on the main variables identified in this paper. The consistency of the experiment shows that the model is effective and robust, and it reveals that environmental factors are the most important factors affecting residents' health literacy level. The actual data show that THE SPM model is a timely and reasonable framework to measure the health literacy level of residents.In this paper, we mainly use random forest and broad learning system (BLS) to predict rectal cancer. A total of 246 participants with computed tomography (CT) image records were enrolled. The total model in the training set (combined with imaging and clinical indicators) has the best prediction result, with the area under the curve (AUC) of 0.999 (95% confidence internal (CI) 0.996-1.000) and the accuracy of 0.990 (95%CI 0.976-1.000). Model 3, the general model in the test set, has the best prediction result, with the AUC of 0.962 (95%CI 0.915-1.000) and the accuracy of 0.920 (95%CI 0.845-0.995). The results of the model using random forest prediction are compared with those using BLS prediction. It can be found that there is no statistical difference between the two results. Our prediction model combined with image features has a good prediction result, and this image feature is the most important among all features. Consequently, we can successfully predict rectal cancer through a combination of the clinical indicators and the comprehensive indicators of CT image characteristics in four different periods (plain scan, vein, artery, and excretion).Inclusion body myopathy (IBM) with Paget's disease of bone (PDB) and/or frontotemporal dementia (FTD) (IBMPFD) was recently identified as rare autosomal dominant disorder due to mutations in VCP gene. However, VCP mutations have also been documented in patients with amyotrophic lateral sclerosis (ALS), Charcot-Marie-Tooth type 2 (CMT2) disease, and hereditary spastic paraplegia (HSP), underlining the heterogeneity of the phenotypes due to VCP mutations. In this study, we reported a novel missense heterozygous variant c.1184A > C (p.D395A) in exon 10 of VCP gene identified in three patients (two sisters and one brother) belonging to an Italian family. The patients underwent a detailed clinical evaluation including medical history, neurological examination, and neuropsychological assessment. Brain's morphologic and functional analysis was also performed. The whole picture was consistent with the criteria of behavioral variant frontotemporal dementia (bvFTD) without IBM and PBD. Our report confirms the high degree of heterogeneity of VCP disease. A VCP analysis should be considered for the genetic screening of familial bvFTD with an early onset also in absence of IBM or PDB signs.Variants in the GLIS family zinc finger protein 2 (GLIS2) are a rare cause of nephronophthisis-related ciliopathies (NPHP-RC). A reduction in urinary concentration and a progressive chronic tubulointerstitial nephropathy with corticomedullary cysts are the major characteristic features of NPHP. NPHP demonstrates phenotypic and genetic heterogeneity with at least 25 different recessive genes associated with the disease. We report a female, from a consanguineous family, who presented age 9 years with echogenic kidneys with loss of cortico-medullary differentiation and progressive chronic kidney disease reaching kidney failure by 10 years of age. A novel homozygous in-frame deletion (NM_032,575.3 c.560_574delACCATGTCAACGATT, p.H188_Y192del) in GLIS2 was identified using whole exome sequencing (WES) that segregated from each parent. The five amino acid deletion disrupts the alpha-helix of GLIS2 zinc-finger motif with predicted misfolding of the protein leading to its predicted pathogenicity. This study broadens the variant spectrum of GLIS2 variants leading to NPHP-RC. WES is a suitable molecular tool for children with kidney failure suggestive of NPHP-RC and should be part of routine diagnostics in kidney failure of unknown cause, especially in consanguineous families.Atrial fibrillation (AF) is an abnormal heart rhythm related to an increased risk of heart failure, dementia, and stroke. The distinction between valvular and non-valvular AF remains a debate. In this study, proteomics and metabolomics were integrated to describe the dysregulated metabolites and proteins of AF patients relative to sinus rhythm (SR) patients. Totally 47 up-regulated and 41 down-regulated proteins in valvular AF, and 59 up-regulated and 149 down-regulated proteins in non-valvular AF were recognized in comparison to SR patients. Moreover, 58 up-regulated and 49 significantly down-regulated metabolites in valvular AF, and 47 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF patients were identified in comparison to SR patients. https://www.selleckchem.com/products/GDC-0449.html Based on analysis of differential levels of metabolites and proteins, 15 up-regulated and 22 down-regulated proteins, and 13 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF were identified relative to valvular AF. KF and non-valvular persistent AF from SR samples, with areas under curve of 0.75 and 0.707, respectively. Hence, these metabolites and proteins can be used as potential clinical molecular markers to discriminate two types of AF from SR samples. In summary, this study provides novel insights to understanding the mechanisms of AF progression and identifying novel biomarkers for prognosis of non-valvular AF and valvular AF by using metabolomics and proteomics analyses.Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.Background Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC. Materials and Methods Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algore responsive to immunotherapy and targeted therapy than the high-risk group. Conclusion We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.The inhibitory regulators, known as immune checkpoints, prevent overreaction of the immune system, avoid normal tissue damage, and maintain immune homeostasis during the antimicrobial or antiviral immune response. Unfortunately, cancer cells can mimic the ligands of immune checkpoints to evade immune surveillance. Application of immune checkpoint blockade can help dampen the ligands expressed on cancer cells, reverse the exhaustion status of effector T cells, and reinvigorate the antitumor function. Here, we briefly introduce the structure, expression, signaling pathway, and targeted drugs of several inhibitory immune checkpoints (PD-1/PD-L1, CTLA-4, TIM-3, LAG-3, VISTA, and IDO1). And we summarize the application of immune checkpoint inhibitors in tumors, such as single agent and combination therapy and adverse reactions. At the same time, we further discussed the correlation between immune checkpoints and microorganisms and the role of immune checkpoints in microbial-infection diseases. This review focused on the current knowledge about the role of the immune checkpoints will help in applying immune checkpoints for clinical therapy of cancer and other diseases.