Upregulation of AT1 Receptor Mediates a Pressor Effect Through ROS-SAPK/JNK Signaling in Glutamatergic Neurons of Rostral Ventrolateral Medulla in Rats With Stress-Induced Hypertension

The present study examined whether angiotensin II (Ang II) mediates the pressor effect through nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-derived reactive oxygen species (ROS)-mitogen-activated protein kinase (MAPK) signaling in the glutamatergic neurons of the rostral ventrolateral medulla (RVLM) in stress-induced hypertensive rats (SIHR). The SIHR model was established using electric foot-shocks combined with noises for 15 days. We observed that Ang II type 1 receptor (AT1R) and the glutamatergic neurons co-localized in the RVLM of SIHR. Furthermore, glutamate levels in the intermediolateral column of the spinal cord were higher in SIHR than in controls. Microinjection of Ang II into the RVLM of SIHR activated stress-activated protein kinase/Jun N-terminal kinase (SAPK/JNK), extracellular signal-regulated protein kinase (ERK) 1/2, and p38MAPK. Compared with controls, the activation of SAPK/JNK, ERK1/2, p38MAPK, and ROS in the RVLM were higher in SIHR, an effect that was blocked by an NADPH oxidase inhibitor (apocynin) and an AT1R antagonist (candesartan). RVLM microinjection of apocynin or a SAPK/JNK inhibitor (SP600125), but not an ERK1/2 inhibitor (U0126) or a p38MAPK inhibitor (SB203580), decreased AT1R mRNA and mean arterial blood pressure (MABP) in SIHR. The increase of AT1R protein expression and MABP was inhibited by intracerebroventricular infusion (ICV), for 14 days, of SP600125, but not U0126 or SB203580 in SIHR. We conclude that Ang II modulates the pressor effect through AT1R-dependent ROS-SAPK/JNK signaling in glutamatergic neurons in the RVLM of SIHR

Monitoring Prevalence and Persistence of Environmental Contamination by SARS-CoV-2 RNA in a Makeshift Hospital for Asymptomatic and Very Mild COVID-19 Patients

Objective: To investigate the details of environmental contamination status by SARS-CoV-2 in a makeshift COVID-19 hospital.Methods: Environmental samples were collected from a makeshift hospital. The extent of contamination was assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) for SARS-CoV-2 RNA from various samples.Results: There was a wide range of total collected samples contaminated with SARS-CoV-2 RNA, ranging from 8.47% to 100%. Results revealed that 70.00% of sewage from the bathroom and 48.19% of air samples were positive. The highest rate of contamination was found from the no-touch surfaces (73.07%) and the lowest from frequently touched surfaces (33.40%). The most contaminated objects were the top surfaces of patient cubic partitions (100%). The median Ct values among strongly positive samples were 33.38 (IQR, 31.69–35.07) and 33.24 (IQR, 31.33–34.34) for ORF1ab and N genes, respectively. SARS-CoV-2 relic RNA can be detected on indoor surfaces for up to 20 days.Conclusion: The findings show a higher prevalence and persistence in detecting the presence of SARS-CoV-2 in the makeshift COVID-19 hospital setting. The contamination mode of droplet deposition may be more common than contaminated touches

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

Objective: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC. Methods: Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package. Results: In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups. Conclusion: In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients

The community composition variation of Russulaceae associated with the Quercus mongolica forest during the growing season at Wudalianchi City, China

Background Most species of the Russulaceae are ectomycorrhizal (ECM) fungi, which are widely distributed in different types of forest ecology and drive important ecological and economic functions. Little is known about the composition variation of the Russulaceae fungal community aboveground and in the root and soil during the growing season (June–October) from a Quercus mongolica forest. In this study, we investigated the changes in the composition of the Russulaceae during the growing season of this type of forest in Wudalianchi City, China. Methods To achieve this, the Sanger sequencing method was used to identify the Russulaceae aboveground, and the high-throughput sequencing method was used to analyze the species composition of the Russulaceae in the root and soil. Moreover, we used the Pearson correlation analysis, the redundancy analysis and the multivariate linear regression analysis to analyze which factors significantly affected the composition and distribution of the Russulaceae fungal community. Results A total of 56 species of Russulaceae were detected in the Q. mongolica forest, which included 48 species of Russula, seven species of Lactarius, and one species of Lactifluus. Russula was the dominant group. During the growing season, the sporocarps of Russula appeared earlier than those of Lactarius. The number of species aboveground exhibited a decrease after the increase and were significantly affected by the average monthly air temperature (r = −0.822, p = 0.045), average monthly relative humidity (r = −0.826, p = 0.043), monthly rainfall (r = 0.850, p = 0.032), soil moisture (r = 0.841, p = 0.036) and soil organic matter (r = 0.911, p = 0.012). In the roots and soils under the Q. mongolica forest, the number of species did not show an apparent trend. The number of species from the roots was the largest in September and the lowest in August, while those from the soils were the largest in October and the lowest in June. Both were significantly affected by the average monthly air temperature (r2 = 0.6083, p = 0.040) and monthly rainfall (r2 = 0.6354, p = 0.039). Moreover, the relative abundance of Russula and Lactarius in the roots and soils showed a linear correlation with the relative abundance of the other fungal genera

Table2_Monitoring Prevalence and Persistence of Environmental Contamination by SARS-CoV-2 RNA in a Makeshift Hospital for Asymptomatic and Very Mild COVID-19 Patients.XLSX

Objective: To investigate the details of environmental contamination status by SARS-CoV-2 in a makeshift COVID-19 hospital.Methods: Environmental samples were collected from a makeshift hospital. The extent of contamination was assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) for SARS-CoV-2 RNA from various samples.Results: There was a wide range of total collected samples contaminated with SARS-CoV-2 RNA, ranging from 8.47% to 100%. Results revealed that 70.00% of sewage from the bathroom and 48.19% of air samples were positive. The highest rate of contamination was found from the no-touch surfaces (73.07%) and the lowest from frequently touched surfaces (33.40%). The most contaminated objects were the top surfaces of patient cubic partitions (100%). The median Ct values among strongly positive samples were 33.38 (IQR, 31.69–35.07) and 33.24 (IQR, 31.33–34.34) for ORF1ab and N genes, respectively. SARS-CoV-2 relic RNA can be detected on indoor surfaces for up to 20 days.Conclusion: The findings show a higher prevalence and persistence in detecting the presence of SARS-CoV-2 in the makeshift COVID-19 hospital setting. The contamination mode of droplet deposition may be more common than contaminated touches.</p