Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 95.14%±0.9%, 90.17%±1.83%, and 81.96%± 4.32% for WM, GM, and CSF. With the experiment results, the proposed algorithm can achieve better performances than other automatic segmentation methods. Further experiments are performed on the 200 3T&1.5T brain MR images of ADNI dataset and our proposed method shows promised performances.
 
  The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.
 The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.Schistosome infection is regarded as one of the most important and neglected tropical diseases associated with poor sanitation. Like other living organisms, schistosomes employ multiple biological processes, of which some are regulated by a post-translational modification called Adenosine diphosphate-ribosylation (ADP-ribosylation), catalyzed by ADPribosyltransferases. ADP-ribosylation is the addition of ADP-ribose moieties from nicotinamide adenine dinucleotide (NAD+) to various targets, which include proteins and nucleotides. It is crucial in biological processes such as DNA repair, apoptosis, carbohydrate metabolism and catabolism. In the absence of a vaccine against schistosomiasis, this becomes a promising pathway in the identification of drug targets against various forms of this infection. The tegument of the worm is an encouraging immunogenic target for anti-schistosomal vaccine development. Vaccinology, molecular modeling and target-based drug discovery strategies have been used for years in drug discovery and for vaccine development. In this paper, we outline ADP-ribosylation and other different approaches to drug discovery and vaccine development against schistosomiasis.
  Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining.  https://www.selleckchem.com/  Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS.
 
  This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article.
 
  By this thorough study, drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.
  Statins are the mainstay of treatment for low-density lipoprotein cholesterol (LDL-C) lowering, however, some patients cannot tolerate statins because of adverse effects. Ezetimibe and proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) are alternative treatment options. The purpose of this meta-analysis is to compare LDL-C reduction with ezetimibe vs PCSK9i in patients not on statins.
 
  PubMed and EMBASE were searched until 14 March 2020 for randomized clinical trials (RCTs) assessing the efficacy of ezetimibe vs PCSK9i in patients not on statins. The primary outcome was reduction in LDL-C levels. A subgroup analysis of statin intolerant patients was also performed.
 
  We identified 8 RCTs that enrolled a total of 1602 patients comparing the two pharmacotherapies. PCSK9i lowered LDL-C levels significantly more than ezetimibe (mean difference (MD) -36.5; 95% confidence interval (CI) [-38.3, -34.7, p<0.00001, I2=4%]. In the statin intolerant subgroup, PCSK9i showed significantly greater reduction in LDL-C levels compared with ezetimibe (MD -36.1; 95% CI [-39.2, -33.1, p<0.00001, I2=21%]. There were no significant differences in LDL-C reduction between different PCSK9i dosages (140 mg once every 2 weeks vs 420 mg once every 4 weeks) (MD - 1.87; 95% CI [-4.45, 0.71, p<0.16, I2=0].
 
  Among patients who are statin intolerant or not receiving statins, PCSK9i use is associated with significantly lower LDL-C levels than after treatment with ezetimibe. PCSK9i might be useful in the prevention and treatment of atherosclerotic cardiovascular disease (ASCVD) in this subset of patients.
 Among patients who are statin intolerant or not receiving statins, PCSK9i use is associated with significantly lower LDL-C levels than after treatment with ezetimibe. PCSK9i might be useful in the prevention and treatment of atherosclerotic cardiovascular disease (ASCVD) in this subset of patients.Phytocompounds are long known for their therapeutic uses due to their competence as antimicrobial agents. The antimicrobial activity of these bioactive compounds manifests their ability as an antibiofilm agent and is thereby proved to be competent to treat the wide spread of biofilm-associated chronic infections. Rapid development of antibiotic resistance in bacteria has made the treatment of these infections almost impossible by conventional antibiotic therapy, which forced in the switch over to the use of phytocompounds. The present overview deals with the classification of the huge array of phytocompounds according to their chemical nature, detection of their target pathogen, and elucidation of their mode of action.Traditional Chinese medicine (TCM) or herbs are widely used in the prevention and treatment of viral infectious diseases. However, the underlying mechanisms of TCMs remain largely obscure due to complicated material basis and multi-target therapeutics. TCMs have been reported to display anti-influenza activity associated with immunoregulatory mechanisms by enhancing host antiinfluenza immune responses. Previous studies have helped us understand the direct harm caused by the virus itself. In this review, we have tried to summarize recent progress in TCM-based anti-influenza research on the indirect harmful immune responses caused by influenza viruses. In particular, the phytochemicals from TCMs responsible for molecular mechanisms of action belonging to different classes, including phenolic compounds, flavonoids, alkaloids and polysaccharides, have been identified and demonstrated. In addition, this review focuses on the pharmacological mechanism, e.g., inflammatory responses and the interferon (IFN) signaling pathway, which can provide a theoretical basis and approaches for TCM based anti-influenza treatment.