Allosteric Inhibition of Factor XIIIa. Non-Saccharide Glycosaminoglycan Mimetics, but Not Glycosaminoglycans, Exhibit Promising Inhibition Profile

Factor XIIIa (FXIIIa) is a transglutaminase that catalyzes the last step in the coagulation process. Orthostery is the only approach that has been exploited to design FXIIIa inhibitors. Yet, allosteric inhibition of FXIIIa is a paradigm that may offer a key advantage of controlled inhibition over orthosteric inhibition. Such an approach is likely to lead to novel FXIIIa inhibitors that do not carry bleeding risks. We reasoned that targeting a collection of basic amino acid residues distant from FXIIIa’s active site by using sulfated glycosaminoglycans (GAGs) or non-saccharide GAG mimetics (NSGMs) would lead to the discovery of the first allosteric FXIIIa inhibitors. We tested a library of 22 variably sulfated GAGs and NSGMs against human FXIIIa to discover promising hits. Interestingly, although some GAGs bound to FXIIIa better than NSGMs, no GAG displayed any inhibition. An undecasulfated quercetin analog was found to inhibit FXIIIa with reasonable potency (efficacy of 98%). Michaelis-Menten kinetic studies revealed an allosteric mechanism of inhibition. Fluorescence studies confirmed close correspondence between binding affinity and inhibition potency, as expected for an allosteric process. The inhibitor was reversible and at least 9-fold- and 26-fold selective over two GAG-binding proteins factor Xa (efficacy of 71%) and thrombin, respectively, and at least 27-fold selective over a cysteine protease papain. The inhibitor also inhibited the FXIIIa-mediated polymerization of fibrin in vitro. Overall, our work presents the proof-of-principle that FXIIIa can be allosterically modulated by sulfated non-saccharide agents much smaller than GAGs, which should enable the design of selective and safe anticoagulants

Cloud Computing and Its Impact on Financial and Banking Domains

The world of computing has undergone significant transformation as a result of recent technological advancements. Distributed computing, cloud computing, grid computing, and parallel computing are just a few examples. One of the most significant developments in computer history may have been the evolution of cloud computing during the last several years. Regrettably, a lot of institutions are still reluctant to use cloud computing. Artificial Intelligence and cloud computing will have the largest effects on the banking sector. Cloud technology gives banks and credit unions the ability to react quickly to shifting market conditions, improving customer experience and operational productivity through the use of data and applied analytics. This allows for better corporate agility. Cloud computing therefore steps in to address these issues and make banking a dependable and trustworthy service. This essay explores the concept of cloud computing, its effects on banks and other financial organizations, and how heavily cloud computing is used

Database Security Issues and Challenges in Cloud Computing

The majority of enterprises have recently enthusiastically embraced cloud computing, and at the same time, the database has moved to the cloud. This cloud database paradigm can lower data administration expenses and free up new business to concentrate on the product that is being delivered. Furthermore, issues with scalability, flexibility, performance, availability, and affordability can be resolved with cloud computing. Security, however, has been noted as posing a serious risk to cloud databases and has been essential in fostering public acceptance of cloud computing. Several security factors should be taken into account before implementing any cloud database management system. These features comprise, but are not restricted to, data privacy, data isolation, data availability, data integrity, confidentiality, and defense against insider threats. In this paper, we discuss the most recent research that took into account the security risks and problems associated with adopting cloud databases. In order to better comprehend these problems and how they affect cloud databases, we also provide a conceptual model. Additionally, we look into these problems to the extent that they are relevant and provide two instances of vendors and security features that were used for cloud-based databases. Finally, we provide an overview of the security risks associated with open cloud databases and suggest possible future paths

A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification.

BACKGROUND: The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification. RESULTS: We have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment. CONCLUSIONS: Based on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments

Potentially Inappropriate Medication Use in Older Patient with Breast and Colorectal Cancer

Our objective was to determine predictors of potentially inappropriate medication (PIM) use and its impact on outcomes (including ER visits, hospitalization, all cause death, and composite of three) in breast and colorectal cancer patients receiving chemotherapy. We used data from the SEER database linked to Medicare claims. Our cohort included patients ≥ 66 years diagnosed with of Stage II/III breast or colorectal cancer between 7/1/2007-12/31/2009. Baseline PIM was defined using the Drugs to Avoid in the Elderly list (DAE) or Beers criteria. Univariate and multivariable logistic regression were used to determine the associations of baseline PIMs with different covariates. Event-free survival (EFS) was defined from the initiation of chemotherapy to outcome, and estimated using the KM method. Cox proportional hazards modeling was used to determine the association of baseline PIMs with EFS. The final analysis included 1595 breast and 1528 colorectal patients. The frequency of baseline PIM was 22.2% (DAE) and 27.6% (Beers) in the breast cohort, and 15.5% (DAE) and 24.8% (Beers) in the colorectal cohort. Baseline PIM was associated with younger age, baseline ≥5 medications, and female gender. In the breast cohort, 37.5% patients had at least one composite outcome. One-year EFS rate was 49%, 62%, 96%, and 45% for ER, hospitalization, death, and composite respectively. Variables associated with increased risk of the composite outcome included baseline ≥5 medications, advanced stage, higher comorbidity, and baseline ER/hospitalization. Baseline PIM using DAE was associated with increased risk of death in the breast cohort, HR 2.31 (95% CI 1.07-4.96). 45% of patients in the colorectal cohort had at least one composite outcome. One-year EFS rate was 42%, 54%, 91%, and 38% respectively. Variables associated with an increased risk of the composite outcome in colorectal patients included baseline ≥ 5 medications, older age, female gender, higher comorbidity. In the time-to-event analysis, we found no association between baseline PIM and most outcomes in either group, aside from baseline PIM using DAE and death in the breast cohort during chemotherapy. Baseline ≥5 medications was associated with increased risks of adverse outcomes in both. Our findings require further prospective confirmation but call into doubt the need to reduce PIM in older patients during chemotherapy

Application of Fuzzy Logic on Understanding of Risks in Supply Chain and Supplier Selection

The aim of this research is firstly to determine the key risk factors of Supply Chain Management (SCM) and developing an efficient model to assess them. In this work, first the risks involved in SCM has been identified and arranged in a systematic hierarchical structure. Questionnaire surveys have been used for data collection from a managerial decision-making group of a case industry. Next, based on the obtained linguistic data, a fuzzy logic based assessment module has been designed for the evaluation of aggregated SC risks. Finally, various risk factors have been categorized; then ranked using ‘fuzzy maximizing and minimizing fuzzy set theory’ in order to identify/assess the major risk factors that need to be managed or controlled. The present trend in the market is no longer the competition among the enterprises but the supply chain. Supplier selection is the most critical decision of the whole procuring department. Selection of supplier is a complicated decision involving many criteria to take into consideration. In later part, this study tries to rank the suppliers centred on different risks and draw a compromise solution. In order to achieve this, understanding risks is of utmost important. In this work, risks associated with the supplier selection have been recognized and analyzed to rank candidate suppliers based on their affinity to risk using fuzzy based VIKOR method. These risks have varied probability of occurrence and impact on the supply chain. Risks have been represented by linguistic variables and then parameterized by Triangular Fuzzy Number (TFN). Fuzzy risk extent has been calculated and thereby Fuzzy Best Value (FBV) and Fuzzy Worst Value (FWV) have been determined. Fuzzy Utility value has been calculated and utilizing this, ranking has been made by closeness to FBV and farness to FWV. Best alternative has been preferred by maximizing utility group and minimizing regret group

Plasmin Regulation through Allosteric, Sulfated, Small Molecules

Plasmin, a key serine protease, plays a major role in clot lysis and extracellular matrix remodeling. Heparin, a natural polydisperse sulfated glycosaminoglycan, is known to allosterically modulate plasmin activity. No small allosteric inhibitor of plasmin has been discovered to date. We screened an in-house library of 55 sulfated, small glycosaminoglycan mimetics based on nine distinct scaffolds and varying number and positions of sulfate groups to discover several promising hits. Of these, a pentasulfated flavonoid-quinazolinone dimer 32 was found to be the most potent sulfated small inhibitor of plasmin (IC50 = 45 μM, efficacy = 100%). Michaelis-Menten kinetic studies revealed an allosteric inhibition of plasmin by these inhibitors. Studies also indicated that the most potent inhibitors are selective for plasmin over thrombin and factor Xa, two serine proteases in coagulation cascade. Interestingly, different inhibitors exhibited different levels of efficacy (40%–100%), an observation alluding to the unique advantage offered by an allosteric process. Overall, our work presents the first small, synthetic allosteric plasmin inhibitors for further rational design