Novel potential urinary biomarkers for effective diagnosis and prognostic evaluation of high-grade bladder cancer.

Background: High-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach. Methods: The gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database. Results: A total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes. Conclusions: Our study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

Externalizing traits: Shared causalities for COVID-19 and Alzheimer's dementia using Mendelian randomization analysis.

Externalizing traits have been related with the outcomes of coronavirus disease 2019 (COVID-19) and Alzheimer's dementia (AD); however, whether these associations are causal remains unknown. We used the two-sample Mendelian randomization (MR) approach with more than 200 single-nucleotide polymorphisms (SNPs) for externalizing traits to explore the causal associations of externalizing traits with the risk of COVID-19 (infected COVID-19, hospitalized COVID-19, and severe COVID-19) or AD based on the summary data. The inverse variance-weighted method (IVW) was used to estimate the main effect, followed by several sensitivity analyses. IVW analysis showed significant associations of externalizing traits with COVID-19 infection (odds ratio [OR] = 1.456, 95% confidence interval [95% CI] = 1.224-1.731), hospitalized COVID-19 (OR = 1.970, 95% CI = 1.374-2.826), and AD (OR = 1.077, 95% CI = 1.037-1.119). The results were consistent using weighted median (WM), penalized weighted median (PWM), MR-robust adjusted profile score (MR-RAPS), and leave-one-out sensitivity analyses. Our findings assist in exploring the causal effect of externalizing traits on the pathophysiology of infection and severe infection of COVID-19 and AD. Furthermore, our study provides evidence that shared externalizing traits underpin the two diseases.

AKTeO2(CO3) (A = Li, Na): The First Carbonatotellurites Featuring a Zero-Dimensional [Te2C2O10]4- Cluster and a Wide Band Gap.

The first carbonatotellurites, AKTeO2(CO3) (A = Li, Na), have been successfully synthesized by using boric acid as the mineralizer. AKTeO2(CO3) (A = Li, Na) crystallize in the monoclinic space group P21/n (no. 14), and their structures exhibit the novel zero-dimensional (0D) [Te2C2O10]4- clusters, in which two [TeO4]4- groups form a [Te2O6]4- dimer via edge-sharing, with each side of the dimer attached by a [CO3]2- group via a Te-O-C bridge. The alkali metal cations occupy the voids between the 0D clusters and maintain the charge balance. The ultraviolet-visible-near-infrared diffuse reflectance spectra show that the short absorption cut-off edges of LiKTeO2(CO3) (LKTC) and NaKTeO2(CO3) (NKTC) are 248 and 240 nm, respectively, and LKTC exhibits the largest experimental band gap (4.58 eV) among all of the tellurites containing the π-conjugated anionic groups reported. Theoretical calculations revealed that they exhibit moderate birefringences of 0.029 and 0.040@1064 nm, respectively.

Leveraging IgG N-glycosylation to infer the causality between T2D and hypertension.

BACKGROUND: Observational studies demonstrated a bidirectional association between type 2 diabetes (T2D) and hypertension, whereas Mendelian randomization (MR) analyses supported the causality from T2D to hypertension but not causal from hypertension to T2D. We previously found that IgG N-glycosylation is associated with both T2D and hypertension, and thus IgG N-glycosylation might link the causality between them. METHODS: We carried out a genome-wide association study (GWAS) to identify IgG N-glycosylation-quantitative-trait loci (QTLs) integrating GWAS for T2D and hypertension and then performed bidirectional univariable and multivariable MR analyses to infer the causal association among them. The inverse-variance-weighted (IVW) analysis was performed as the primary analysis, followed by some sensitivity analyses to explore the stability of the results. RESULTS: Six putatively causal IgG N-glycans for T2D and four for hypertension were identified in the IVW method. Genetically predicted T2D increased the risk of hypertension (odds ratio [OR] = 1.177, 95% confidence interval (95% CI) = 1.037-1.338, P = 0.012) and vice versa (OR = 1.391, 95% CI = 1.081-1.790, P = 0.010). Multivariable MR showed that T2D remained at risk effect with hypertension ([OR] = 1.229, 95% CI = 1.140-1.325, P = 7.817 × 10-8) after conditioning on T2D-related IgG-glycans. Conversely, hypertension was associated with higher T2D risk (OR = 1.287, 95% CI = 1.107-1.497, P = 0.001) after adjusting for related IgG-glycans. No evidence of horizontal pleiotropy was observed, as MR‒Egger regression provided P values for intercept > 0.05. CONCLUSION: Our study validated the mutual causality between T2D and hypertension from the perspective of IgG N-glycosylation, further validating the "common soil" hypothesis underlying the pathogenesis of T2D and hypertension.

Research progress of therapeutic effects and drug resistance of immunotherapy based on PD-1/PD-L1 blockade.

The binding of programmed death-1 (PD-1) on the surface of T cells and PD-1 ligand 1 (PD-L1) on tumor cells can prevent the immune-killing effect of T cells on tumor cells and promote the immune escape of tumor cells. Therefore, immune checkpoint blockade targeting PD-1/PD-L1 is a reliable tumor therapy with remarkable efficacy. However, the main challenges of this therapy are low response rate and acquired resistance, so that the outcomes of this therapy are usually unsatisfactory. This review begins with the description of biological structure of the PD-1/PD-L1 immune checkpoint and its role in a variety of cells. Subsequently, the therapeutic effects of immune checkpoint blockers (PD-1 / PD-L1 inhibitors) in various tumors were introduced and analyzed, and the reasons affecting the function of PD-1/PD-L1 were systematically analyzed. Then, we focused on analyzing, sorting out and introducing the possible underlying mechanisms of primary and acquired resistance to PD-1/PD-L1 blockade including abnormal expression of PD-1/PD-L1 and some factors, immune-related pathways, tumor immune microenvironment, and T cell dysfunction and others. Finally, promising therapeutic strategies to sensitize the resistant patients with PD-1/PD-L1 blockade treatment were described. This review is aimed at providing guidance for the treatment of various tumors, and highlighting the drug resistance mechanisms to offer directions for future tumor treatment and improvement of patient prognosis.

Nitrate Quantification in Fresh Vegetables in Shanghai: Its Dietary Risks and Preventive Measures.

To investigate nitrate and nitrite content in fresh vegetables, 264 samples were randomly collected in the farmers' markets in Shanghai, Southeast China. The results indicate that 25.0% of the fresh vegetables were critically or more contaminated by nitrate [>1440 mg/kg FW (Fresh weight)]. Generally, leafy vegetables were more highly enriched in nitrate than root-tuber and fruit vegetables. About 22.6% of the leafy vegetables had a nitrate content exceeding the limit for edible permission (>3000 mg/kg FW). Nitrite content in the fresh vegetables was all within the safe level (<1 mg/kg FW). It was estimated that the daily nitrate intake through eating vegetables in Shanghai exceeded the WHO/FAO allowable limit. The field experiment indicated that the hyper-accumulation of nitrate and nitrite in the vegetables was mainly attributed to the excessive application of chemical fertilizers. The maxima of nitrate and nitrite in the vegetables were attained one week after applying chemical fertilizer, and thus they cannot be picked for dietary use. Applying organic manure can effectively lower the risk of nitrate and nitrite contamination in vegetables. The old leaves and leaf petioles were more easily enriched in nitrate due to their weaker metabolic activity. Vegetables with high nitrate content had a high risk of nitrite toxicity during storage due to the biological conversion of nitrate into nitrite, which is easily triggered by suitable temperature and mechanical damage processing. Therefore, fresh vegetables should be stored by rapid cooling and in undamaged forms to prevent nitrite accumulation.

Network Pharmacology-Based Exploration of the Therapeutic Mechanisms of Cordyceps cicadae in Renal Ischemia/Reperfusion.

BACKGROUND Cordyceps cicadae is beneficial in treating renal diseases, especially in inhibiting renal ischemia/reperfusion injury (IRI). The aim of this study was to systematically analyze and predict the potential mechanism of Cordyceps cicadae in renal IRI therapy using network pharmacology. MATERIAL AND METHODS Cordycepin, adenosine, and cordycepic acid are the 3 major medicinal ingredients in Cordyceps cicadae. Based on network pharmacology, the 3D structure of the 3 compounds were obtained, and then the common targets between these compounds and renal IRI were analyzed and determined. We used the ingredient-target (I-T), protein-protein interaction (PPI) networks, the enrichment analysis of Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to find the possible pharmacological mechanism of Cordyceps cicadae in treating renal IRI. RESULTS Through target fishing and analysis, the 3 active ingredients of Cordyceps cicadae shared 81 target genes with renal IRI. I-T network showed that adenosine had the highest degree, and 5 genes were associated with the 3 active ingredients. PPI network analysis showed that ALB, GAPDH, CASP3, MAPK1, FN1, and IL-10 play a pivotal role. The enrichment analysis of GO and KEGG showed that Cordyceps cicadae can treat renal IRI through MAPK, cAMP, PPAR, Rap1, and HIF-1 signaling pathways. CONCLUSIONS Cordyceps cicadae exerts its therapeutic effect on renal IRI via multiple targets and pathways. Nevertheless, further experimentation is needed to verify this. The method of network pharmacology provides an effective method of determining the comprehensive action mechanism of Traditional Chinese Medicine (TCM).

Characterization and determinant factors of critical illness and in-hospital mortality of COVID-19 patients: A retrospective cohort of 1,792 patients in Kenya.

Limited data is available on the coronavirus disease 2019 (COVID-19), critical illness rate, and in-hospital mortality in the African setting. This study investigates determinants of critical illness and in-hospital mortality among COVID-19 patients in Kenya. We conducted a retrospective cohort study at Kenyatta National Hospital (KNH) in Kenya. Multivariate logistic regression and Cox proportional hazard regression were employed to determine predictor factors for intensive care unit (ICU) admission and in-hospital mortality, respectively. In addition, the Kaplan-Meier model was used to compare the survival times using log-rank tests. As a result, 346 (19.3%) COVID-19 patients were admitted to ICU, and 271 (15.1%) died. The majority of those admitted to the hospital were male, 1,137 (63.4%) and asymptomatic, 1,357 (75.7%). The most prevalent clinical features were shortness of breath, fever, and dry cough. In addition, older age, male, health status, patient on oxygen (O2), oxygen saturation levels (SPO2), headache, dry cough, comorbidities, obesity, cardiovascular diseases (CVDs), diabetes, chronic lung disease (CLD), and malignancy/cancer can predicate the risk of ICU admission, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.90 (95% confidence interval [CI]: 0.88-0.92). Survival analysis indicated 271 (15.1%) patients died and identified older age, male, headache, shortness of breath, health status, patient on oxygen, SPO2, headache, comorbidity, CVDs, diabetes, CLD, malignancy/cancer, and smoking as risk factors for mortality (AUC-ROC: 0.90, 95% CI: 0.89-0.91). This is the first attempt to explore predictors for ICU admission and hospital mortality among COVID-19 patients in Kenya.

NaBa3[M2B7O16(OH)2]F2 (M = Ge, Si): Two Synthetic F Analogues of Garrelsite with [B7O16(OH)2]13- Polyanions and Deep-Ultraviolet Cutoff Edges.

Two new borogermanate/borosilicate fluorides, namely, NaBa3[M2B7O16(OH)2]F2 (M = Ge, Si), have been successfully synthesized through a conventional mild hydrothermal method. They represent the first examples of mixed alkali and alkaline-earth borogermanate/borosilicate fluorides. NaBa3[M2B7O16(OH)2]F2 (M = Ge, Si) crystallize in the space group of C2/c, and their structures feature a unique 3D anionic framework composed of [B7O16(OH)2]13- polyanions corner-sharing with SiO4 or GeO4 tetrahedra, forming 1D 10-membered-ring tunnels along the b axis, which are filled by Na+, Ba2+, and F- ions. UV-vis-near-IR absorption spectra identify the title compounds possessing short deep-ultraviolet absorption edges (below 200 nm), while their birefringences were calculated to be 0.021 and 0.016 at 1064 nm, respectively. Optical property, thermal stability, and theoretical calculations have also been conducted on NaBa3[M2B7O16(OH)2]F2 (M = Ge, Si).

Cd2(IO3)(PO4) and Cd1.62Mg0.38(IO3)(PO4): metal iodate-phosphates with large SHG responses and wide band gaps.

The first NLO-active metal iodate-phosphates, namely, Cd2(IO3)(PO4) and Cd1.62Mg0.38(IO3)(PO4) (1 and 2), with three types of NLO groups, have been reported. 1 and 2 are isostructural and the structure of 1 features a 3D network formed by the Cd4(IO3)8/4(PO4)6/3 groups. 1 and 2 with strong SHG signals of 4 × and 3.5 × KH2PO4 are promising SHG materials in the visible region.

M(B(SeO3)3)H2O (M = Al, Ga): the first boroselenites with a unique sandwich like double-layer structure.

Exploration of new types of borates is important because of their promising applications in diverse fields. Two new boroselenites, namely, M(B(SeO3)3)H2O (M = Al, Ga), which represent the first IIIA metal boroselenite, were synthesized by hydrothermal reactions. M(B(SeO3)3)H2O (M = Al, Ga) possesses a unique sandwich like double-layer structure formed by two 2D [MSe2O8]5- layers interconnected by 1D [BSeO5]3- chains. More interestingly, both compounds display large band gaps (4.86/4.79 eV) and moderate birefringences (Δn = 0.063/0.064 at 1064 nm) based on density functional theory (DFT) calculations.

The Role of C-Reactive Protein and Fibrinogen in the Development of Intracerebral Hemorrhage: A Mendelian Randomization Study in European Population.

Background: The causal association of C-reactive protein (CRP) and fibrinogen on intracerebral hemorrhage (ICH) remains uncertain. We investigated the causal associations of CRP and fibrinogen with ICH using two-sample Mendelian randomization. Method: We used single-nucleotide polymorphisms associated with CRP and fibrinogen as instrumental variables. The summary data on ICH were obtained from the International Stroke Genetics Consortium (1,545 cases and 1,481 controls). Two-sample Mendelian randomization estimates were performed to assess with inverse-variance weighted and sensitive analyses methods including the weighted median, the penalized weighted median, pleiotropy residual sum and outlier (MR-PRESSO) approaches. MR-Egger regression was used to explore the pleiotropy. Results: The MR analyses indicated that genetically predicted CRP concentration was not associated with ICH, with an odds ratio (OR) of 1.263 (95% CI = 0.935-1.704, p = 0.127). Besides, genetically predicted fibrinogen concentration was not associated with an increased risk of ICH, with an OR of 0.879 (95% CI = 0.060-18.281; p = 0.933). No evidence of pleiotropic bias was detected by MR-Egger. The findings were overall robust in sensitivity analyses. Conclusions: Our findings did not support that CRP and fibrinogen are causally associated with the risk of ICH.

Association between telomere length in peripheral blood leukocytes and risk of ischemic stroke in a Han Chinese population: a linear and non-linear Mendelian randomization analysis.

BACKGROUND: Many contradictory conclusions pertaining to the telomere length in peripheral leukocyte chromosomes as a potential biomarker for ischemic stroke (IS) risk have been reported by the various observational studies in previous years. This study aims to investigate whether the leukocyte telomere length is associated with an increased IS risk or not, based on the Mendelian randomization (MR) approach. METHODS: Based on the NHGRI-EBI GWAS Catalog database, the Chinese online genetic database as well as the previous published studies, twelve single nucleotide polymorphisms (SNPs) with minor allele frequency ≥ 0.05 were selected and the leukocyte telomere length was measured in 431 first-ever IS patients and 304 healthy controls (quantitative polymerase chain reaction). To explore linear and non-linear effect of telomere length on the IS risk, we preformed the linear MR analysis (the inverse-variance weighted method, the maximum likelihood method, and the mode-based estimation method), and the non-linear MR analysis (semiparametric method with three tests for non-linearity, including the quadratic test, Cochran's Q test, and the fractional polynomial test). RESULTS: Two verified SNPs (rs11125529 and rs412658) were chosen as instrumental variables. In linear MR analysis, the adjusted odds ratios and 95% confidence intervals of IS for genetically predicted telomere lengths, based on the two SNPs, were 1.312 (0.979 to 1.759), 1.326 (0.932 to 1.888) and 1.226 (0.844 to 1.781) for the inverse-variance weighted method, the maximum likelihood method, and the mode-based estimation method, respectively. Three tests for nonlinearity failed to reject the null exactly, indicating that the relationship between telomere length and IS risk is unlikely to be non-linear. CONCLUSION: This MR study based on individual data does not provide strong evidence for a positive linear or non-linear effect of telomere length on the IS risk.

The characteristics and mixing states of PM2.5 during a winter dust storm in Ningbo of the Yangtze River Delta, China.

Dust particulates play an essential role for the nucleation, hygroscopicity and also contribute to aerosol mass. We investigated the chemical composition, size distribution and mixing states of PM2.5 using a single-particle aerosol mass spectrometer (SPAMS), Monitor for AeRosols and Gases (MARGA), and off-line membrane sampling from 2018.1.24 to 2018.2.20 at a coastal supersite in Ningbo, a port city in Yangtze River Delta, China. During the study campaign, the eastern part of China had experienced a wide range of cooling, sandstorm, and snowfall processes. The entire sampling campaign was categorized into five sub-periods based on the levels of PM2.5 and the ratios of PM2.5/PM10, namely clean (T1), heavy pollution (T2), light pollution (T3), dust (sandstorm) (T4) and cleaning pollution (T5) period. After comparing the average mass spectrum for each period, it shows that the primary ions, such as Ca2+and SiO3-, rarely coexist with each other within a single particle, but secondary ions generally coexist with these primary ions. Furthermore, the coexistence of each two different ions within a particle does not show distinct variation for the whole study periods. All these suggest that the absorption and partitioning of gaseous contaminants into the surface of primary aerosol through heterogeneous reactions are the major pathways of aging and growth of aerosol; and the merging of particles through collisions usually is insignificant. Although the absolute concentrations of nitrate and sulfate all increased with the PM2.5 concentrations, the relative equivalent concentrations of NO3- and SO42- displayed opposite trends; the relative contribution of sulfate decreased and that of nitrate increased as the increase of pollution. During the dust period, the relative equivalent concentrations of calcium and/or potassium ions in PM2.5 are significantly higher. This study provided deep insights about the mixing states and characteristics of particulate after long-range transport and a visualization tool for aerosol study.

Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array.

This paper addresses the direction-of-arrival (DOA) estimation problem using a uniform rectangular array with electromagnetic vector-sensors in correlated/coherent signal environments. The polarization information is separated from the steering matrix to decorrelate the signals. By developing a tensorial structured received measurements of the array, we propose a tensor-based eigenvector DOA estimation method. Then we apply the forward-backward averaging to the tensor since it obeys the centro-Hermitian structure. In addition, a tensor-based polarization parameters estimation method is presented. The proposed algorithms are superior to the state-of-the-art algorithms in terms of estimation accuracy of coherent signals while only demand a modest computation burden comparing with the latter ones. Simulation results are given to demonstrate the effectiveness of the proposed methods under different scenarios.