Synthesis of Biologically Active α-Tetralone Condensate: Growth of Single Crystal, Characterization by Experimental and Simulation Methods

The exocyclic double bonded α-tetralone condensate viz. (2E)-2-(4-propoxybenzylidene)-3,4-dihydro-1(2H)-naphthalene-1-one was synthesized by the Claisen–Schmidt reaction between α-Tetralone and 4-propoxybenzaldehyde in an alkaline medium. A slow evaporation technique was used to collect the single crystals. Researchers examined the detailed information provided by spectral studies. The inter- and intra-molecular interactions of the compound were identified using the single-crystal XRD investigation. Charge transfer inside organic molecules was used to calculate HOMO and LUMO energy values. In addition, MEP, NBO, NLO, topological charge distribution, and Mulliken population studies were performed for this compound. The Hirschfeld surface study showed that nonpolar or weakly polar interactions significantly contributed to the packing forces for molecules. Then, it was tested for its antioxidant, antidiabetic, and anti-inflammatory properties. The 6yb7 protein and the (2E)-2-(4-propoxybenzylidene)-3,4-dihydro-2H-naphthalen-1-one (PBDN) ligand were docked in molecular docking research.Crystal growth and spectral studies have been performed on (2E)-2-(4-propoxybenzylidene)-3,4-dihydro-2H-naphthalen-1-one (PBDN).Simulation studies were discussed.The compound PBDN has potential anti-inflammatory and anti-diabetic properties. In-silico method reveals that the PBDN is a moderate ligand for an unliganded active site on COVID-19's main protease (PDB code: 6yb7). [ FROM AUTHOR] Copyright of Polycyclic Aromatic Compounds is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques

Purpose The world is shifting towards the fourth industrial revolution (Industry 4.0), symbolising the move to digital, fully automated habitats and cyber-physical systems. Industry 4.0 consists of innovative ideas and techniques in almost all sectors, including Smart health care, which recommends technologies and mechanisms for early prediction of life-threatening diseases. Cardiovascular disease (CVD), which includes stroke, is one of the world's leading causes of sickness and deaths. As per the American Heart Association, CVDs are a leading cause of death globally, and it is believed that COVID-19 also influenced the health of cardiovascular and the number of patients increases as a result. Early detection of such diseases is one of the solutions for a lower mortality rate. In this work, early prediction models for CVDs are developed with the help of machine learning (ML), a form of artificial intelligence that allows computers to learn and improve on their own without requiring to be explicitly programmed. Design/methodology/approach The proposed CVD prediction models are implemented with the help of ML techniques, namely, decision tree, random forest, k-nearest neighbours, support vector machine, logistic regression, AdaBoost and gradient boosting. To mitigate the effect of over-fitting and under-fitting problems, hyperparameter optimisation techniques are used to develop efficient disease prediction models. Furthermore, the ensemble technique using soft voting is also used to gain more insight into the data set and accurate prediction models. Findings The models were developed to help the health-care providers with the early diagnosis and prediction of heart disease patients, reducing the risk of developing severe diseases. The created heart disease risk evaluation model is built on the Jupyter Notebook Web application, and its performance is calculated using unbiased indicators such as true positive rate, true negative rate, accuracy, precision, misclassification rate, area under the ROC curve and cross-validation approach. The results revealed that the ensemble heart disease model outperforms the other proposed and implemented models. Originality/value The proposed and developed CVD prediction models aims at predicting CVDs at an early stage, thereby taking prevention and precautionary measures at a very early stage of the disease to abate the predictive maintenance as recommended in Industry 4.0. Prediction models are developed on algorithms' default values, hyperparameter optimisations and ensemble techniques.

Thrombotic microangiopathies (TMA): In vivo evidence for platelet activation and endothelial cell injury, and potential for therapeutic intervention with defibrotide

Background : Endothelial cell (EC) activation and injury and platelet activation characterize thrombotic thrombocytopenic purpura (TTP) and atypical hemolytic uremic syndrome (aHUS). We found that 5 μg/ml defibrotide inhibits TMA plasma-mediated caspase 8 activation of EC, an initial step in apoptotic injury (ASH 2019, Abstract 3676), but defibrotide was reported to inhibit agonist-induced platelet activation only at clinically unachievable doses of 100-1000 μg/ ml (ASH 2019, Abstract 3614). Aims : (1) Evaluate biomarkers of platelet activation and EC injury in TMA plasmas;(2) determine whether clinically relevant defibrotide concentrations block agonist-mediated platelet activation. Methods : (1) Biomarkers for platelet activation (platelet factor 4 (PF4), β-thromboglobulin (β-TG)) and EC injury (von Willebrand factor (vWF) antigen) were measured in TMA patient plasmas (9 aHUS, 8 TTP) by ELISA. (2) Washed human platelets were incubated with the PAR-1 agonist peptide RUJL or ADP (2 μM), alone or with 5 μg/ml defibrotide. Platelet aggregation was quantified by light transmission aggregometry. Results(1) A significant increase in PF4 levels was seen in TMA patients ( n = 15) vs. healthy controls ( n = 12) (Fig. 1). A significant difference in β-TG levels was not seen in TMA patients ( n = 15) vs. controls ( n = 7). The β-TG:PF4 ratio, a marker of in vivo platelet activation (Ann Rheum Dis 2005;64:484), was >2 in TMA and control plasmas, indicating some in vitro activation, but much more highly elevated in TMA (ratio = 19.4) vs. control plasmas (ratio = 5.6) ( P = 0.0058). vWF antigen levels were not significantly different in patients vs. controls. (2) Defibrotide blocked platelet aggregation induced by both RUJL and ADP at 5 μg/ml (Fig. 2). Conclusions : FIGURE 2 : Effect of defibrotide on PAR-1 agonist and ADP induced platelet aggregation The ability of defibrotide to block TMA plasma-mediated EC injury, shown previously, and now platelet activation has implications for TMA treatment as well as in progressive COVID-19, which presents features characteristic of TMAs and vaso-occlusive disease.