Plasma thrombopoietin levels as additional tool in clinical management of thrombocytopenic neonates

Computational prognostic evaluation of Alzheimer's drugs from FDA-approved database through structural conformational dynamics and drug repositioning approaches

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

Evolution and Utility of Antiplatelet Autoantibody Testing in Patients with Immune Thrombocytopenia

To this day, immune thrombocytopenia (ITP) remains a clinical diagnosis made by exclusion of other causes for thrombocytopenia. Reliable detection of platelet autoantibodies would support the clinical diagnosis, but the lack of specificity and sensitivity of the available methods for platelet autoantibody testing limits their value in the diagnostic workup of thrombocytopenia. The introduction of methods for glycoprotein-specific autoantibody detection has improved the specificity of testing and is acceptable for ruling in ITP but not ruling it out as a diagnosis. The sensitivity of these assays varies widely, even between studies using comparable assays. A review of the relevant literature combined with our own laboratory's experience of testing large number of serum and platelet samples makes it clear that this variation can be explained by variations in the characteristics of the tests, including in the glycoprotein-specific monoclonal antibodies, the glycoproteins that are tested, the platelet numbers used in the assay and the cutoff levels for positive and negative results, as well as differences in the tested patient populations. In our opinion, further standardization and optimization of the direct autoantibody detection methods to increase sensitivity without compromising specificity seem possible but will still likely be insufficient to distinguish the often very weak specific autoantibody signals from background signals. Further developments of autoantibody detection methods will therefore be necessary to increase sensitivity to a level acceptable to provide laboratory confirmation of a diagnosis of ITP.