Characterization of a novel endornavirus isolated from the phytopathogenic fungus Rhizoctonia solani.

Rhizoctonia solani endornavirus 8 (RsEV8) was isolated from strain XY175 of Rhizoctonia solani AG-1 IA. The full-length genome of RsEV8 is 16,147 nucleotides (nt) in length and contains a single open reading frame that encodes a large polyprotein of 5227 amino acids. The polyprotein contains four conserved domains: viral methyltransferase, putative DEAH box helicase, viral helicase, and RNA-dependent RNA polymerase (RdRp). RsEV8 has a shorter 3'-UTR (58 nt) and a longer 5'-UTR (404 nt). A multiple sequence alignment indicated that the RdRp of RsEV8 possesses eight typical RdRp motifs. According to a BLASTp analysis, RsEV8 shares 39.31% sequence identity with Rhizoctonia cerealis endornavirus-1084-7. Phylogenetic analysis demonstrated that RsEV8 clusters with members of the genus Betaendornavirus.

Association and temporal sequence of pneumonia and gastrointestinal bleeding after acute ischemic stroke.

BACKGROUND: Stroke-associated pneumonia (SAP) and gastrointestinal bleeding (GIB) are common medical complications after stroke. The previous study suggested a strong association between SAP and GIB after stroke. However, little is known about the time sequence of SAP and GIB. In the present study, we aimed to verify the association and clarify the temporal sequence of SAP and GIB after ischemic stroke. METHODS: Patients with ischemic stroke from in-hospital Medical Complication after Acute Stroke study were analyzed. Data on occurrences of SAP and GIB during hospitalization and the intervals from stroke onset to diagnosis of SAP and GIB were collected. Multiple logistic regression was used to evaluate the association between SAP and GIB. Kruskal-Wallis test was used to compare the time intervals from stroke onset to diagnosis of SAP and GIB. RESULTS: A total of 1129 patients with ischemic stroke were included. The median length of hospitalization was 14 days. Overall, 86 patients (7.6%; 95% CI, 6.1-9.2%) developed SAP and 47 patients (4.3%; 95% CI, 3.0-5.3%) developed GIB during hospitalization. After adjusting potential confounders, SAP was significantly associated with the development of GIB after ischemic stroke (OR = 5.13; 95% CI, 2.02-13.00; P < 0.001). The median time from stroke onset to diagnosis of SAP was shorter than that of GIB after ischemic stroke (4 days vs. 5 days; P = 0.039). CONCLUSIONS: SAP was associated with GIB after ischemic stroke, and the onset time of SAP was earlier than that of GIB. It is imperative to take precautions to prevent GIB in stroke patients with SAP.

Underground Mine Safety and Health: A Hybrid MEREC-CoCoSo System for the Selection of Best Sensor.

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of "Ease of Installation" as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model's performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

Carotid atherosclerotic plaque predicts progression of intracranial artery atherosclerosis: A MR imaging-based community cohort study.

PURPOSE: Intracranial artery atherosclerosis (ICAS) progression is associated with stroke. However, the association of carotid plaque with ICAS progression among stroke-free participants is still unclear. This study aimed to evaluate the association between carotid plaque and ICAS progression in stroke-free participants. METHOD: Stroke-free participants were recruited from a community-based cohort study. All participants underwent questionnaire interviews, blood tests, and high-resolution vessel wall magnetic resonance (MR) imaging at baseline and follow-up for around three years. The atherosclerotic plaque was defined as eccentric wall thickening on MR imaging. The presence, location, total number, and burden (maximum wall thickness, length, and stenosis) of carotid and intracranial plaque were evaluated. ICAS progression was defined as the number increased or plaque burden (maximum wall thickness, length, or stenosis increase) increased by ≥ 20 %. The association between carotid plaque and ICAS progression was evaluated using multivariable logistic regression. RESULTS: Of the 312 participants (mean age at baseline: 59.85 ± 13.04 years; 136 males) who completed baseline and follow-up studies with a mean time interval of 3.15 ± 0.59 years, 85 (27.24 %) had progression of ICAS during follow-up. At least one carotid plaque was detected at baseline in 167 (53.53 %) participants. In the multivariable logistic analysis, carotid plaque was a significant predictor for the progression of ICAS (odds ratio, 2.04; 95 % confidence interval, 1.06-3.92; P = 0.032). CONCLUSIONS: Carotid plaque is associated with intracranial artery atherosclerosis progression in stroke-free population. Our findings suggest that carotid plaque may be an effective predictor for intracranial artery atherosclerosis progression.

A review of deep learning methods for ligand based drug virtual screening.

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

Inflammation and Adverse Outcomes in Patients With Acute Ischemic Stroke With and Without Chronic Kidney Disease.

BACKGROUND: Elevated white blood cell count, fibrinogen levels, and lower levels of albumin signify higher systemic inflammatory response, hypercoagulable state, and poorer nutritional status, respectively. However, a consistent conclusion could not be drawn on whether the association between inflammatory markers and cardiovascular disease was affected by the presence of chronic kidney disease (CKD). We aimed to explore the association between inflammation and adverse outcomes in patients with acute ischemic stroke (AIS), as well as whether this association differs due to the presence of CKD. METHODS AND RESULTS: This research was based on the Third China National Stroke Registry. The main adverse outcomes were poor functional outcome, stroke recurrence, and combined vascular event after 1 year. Inflammation was defined as the worst quartile of at least 2 of the aforementioned 3 markers. Finally, 8493 patients with AIS were enrolled in this study. The adjusted odds ratios/hazard ratios and 95% CIs of inflammation were 1.58 (1.34-1.86) for poor functional outcomes, 1.25 (1.06-1.47) for stroke recurrence, and 1.25 (1.06-1.46) for combined vascular event. The association between inflammation and adverse outcomes existed only in patients with AIS without CKD, although the interaction between CKD and inflammation was not statistically significant. (P for interaction >0.05). CONCLUSIONS: Inflammation, which was defined as a combination of fibrinogen, white blood cell count, and albumin, was associated with all 1-year adverse outcomes among patients with AIS. Routine assessment of these biomarkers could become a potential part of the clinical evaluation for patients with AIS, especially those without CKD, aiding clinicians in risk stratification and treatment decision-making.

Safety and efficacy of GD-11 in patients with ischaemic stroke: a multicentre, double-blind, randomised, placebo-controlled, phase 2 trial.

BACKGROUND: GD-11, a novel brain cytoprotective drug, was designed to be actively taken up and transported across the blood-brain barrier via the glucose transporter. This study aimed to evaluate the safety and efficacy of GD-11 for improving the recovery of patients with acute ischaemic stroke (AIS). METHODS: A double-blind, randomised, placebo-controlled, phase 2 trial was conducted at 15 clinical sites in China. Patients aged 18-80 years with AIS within 48 hours were randomly assigned (1:1:1) to receive 160 mg GD-11, 80 mg GD-11 and placebo, two times a day for 10 days. The primary endpoint was a modified Rankin Scale (mRS) score of 0-1 at 90 days after treatment. The safety outcome was any adverse events within 90 days. RESULTS: From 17 November 2022 to 22 March 2023, a total of 80 patients in the 160 mg GD-11 group, 79 patients in the 80 mg GD-11 group and 80 patients in the placebo group were included. The proportion of an mRS score of 0-1 at day 90 was 77.5% in the 160 mg GD-11 group, 72.2% in the 80 mg GD-11 group and 67.5% in the placebo group. Though no significant difference was found (p=0.3671), a numerically higher proportion was observed in the GD-11 group, especially in the 160 mg GD-11 group. The incidence of adverse events was similar across the three groups (p=0.1992). CONCLUSION: GD-11 was safe and well-tolerated. A dosage of GD-11 160 mg two times a day was recommended for a large trial to investigate the efficacy.