The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.Porous nanomaterials with high surface area, tunable porosity, and large mesopores have recently received particular attention in cancer therapy and imaging. Introduction of additional pores to nanostructures not only endows the tunability of optoelectronic and optical features optimal for tumor treatment, but also modulates the loading capacity and controlled release of therapeutic agents. In recognition, increasing efforts have been made to fabricate various porous nanomaterials and explore their potentials in oncology applications. Thus, a systematic and comprehensive summary is necessary to overview the recent progress, especially in last ten years, on the development of various mesoporous nanomaterials for cancer treatment as theranostic agents. While outlining their individual synthetic mechanisms after a brief introduction of the structures and properties of porous nanomaterials, the current review highlighted the representative applications of three main categories of porous nanostructures (organic, inorganic, and organic-inorganic nanomaterials). In each category, the synthesis, representative examples, and interactions with tumors were further detailed. The review was concluded with deliberations on the key challenges and future outlooks of porous nanostructures in cancer theranostics.The family of coronavirus are named for their crown shape. Encoded by the genetic material inherited from the coronavirus itself, this intrinsic well-known "viral corona" is considered an "inherited corona". After contact with mucosa or the entrance into the host, bare coronaviruses can become covered by a group of dissolved biomolecules to form one or multiple layers of biomolecules. The layers acquired from the surrounding environment are named the "acquired corona". We highlight here the possible role of the acquired corona in the pathogenesis of coronaviruses, which will generate fresh insight into the nature of various coronavirus-host interactions.The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19.  https://www.selleckchem.com/products/PHA-793887.html  Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.To evaluate the efficacy and safety of Xinyue capsule (XYC) in the treatment of coronary artery disease (CAD) after percutaneous coronary intervention (PCI), databases including MEDLINE, EMBASE (Ovid), PubMed, Google Scholar, Cochrane Central Register of Controlled Trials (CENTRAL), China National Knowledge Infrastructure database (CNKI), Wanfang, and VIP were searched to identify randomized controlled trials (RCTs) on XYC in CAD after PCI published before October 2020. Data extraction, methodological quality assessment, and data analysis were performed according to the Cochrane standard. Dichotomous data were shown as risk ratios (RRs) with a 95% confidence interval (CI). All analyses were done with Review Manager, version 5.3. The quality of evidence was assessed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. A total of 9 related studies from 166 related articles were identified, which included 2979 patients. Compared with conventional treatment alone (or placebo ng cardiac function, and reducing serum NT-pro-BNP. This potential benefit requires a high-quality RCT to assess.Parkinson's disease is a common neurodegenerative disorder marked by the accumulation of the protein alpha synuclein. Studies have indicated the role of prolyl oligopeptidase (POP), a serine protease, in alpha synuclein accumulation. Therefore, POP emerges as an attractive medicinal target. Traditionally, most of the early medicines have been plant-based owing to their ready availability and negligible side effects. Alkaloids owing to their neurotransmitter modulatory, anti-amyloid, anti-oxidant, and anti-inflammatory activities have shown potential in neurodegenerative disease. In this work, we computationally evaluated alkaloid class of phytochemicals for their therapeutic efficacy against POP. Alkaloids were retrieved from the publically available database, Chemical Entities of Biological Interest (ChEBI), and screened for their drug likeness (Lipinski's rule of 5) and absorption, distribution, metabolism, and excretion, and toxicity (ADMET) in Discovery Studio by ensuring parameters suitable for a centralhibitors. The research conducted here, therefore, provides evidence for conducting in vitro POP inhibitory studies of these newly identified plant-based POP inhibitors.This study used a network pharmacology approach to investigate the potential active ingredients of Plantaginis Herba and its underlying mechanisms in hyperuricemia treatment. The potential active ingredients of Plantaginis Herba were obtained from TCMSP and ETCM databases, and the potential targets of the active ingredients were predicted using the Swiss TargetPrediction database. The potential therapeutic targets of hyperuricemia were retrieved from the GeneCards, DisGeNET, and Online Mendelian Inheritance in Man (OMIM) databases. Then, the integrative bioinformatics analyses of candidates were performed by GO analysis, KEGG analysis, and PPI network construction. There were 15 predicted active ingredients in Plantaginis Herba and 41 common targets that may be involved in the treatment of hyperuricemia. A total of 61 GO annotations and 35 signaling pathways were identified by enrichment analysis (P less then 0.01). The underlying mechanisms of Plantaginis Herba may be related to insulin resistance, PI3K/AKT, TNF, VEGF, AMPK, and glucagon signaling pathways.