Pyrazoline analogs as potential anticancer agents and their apoptosis, molecular docking, MD simulation, DNA binding and antioxidant studies.

N-formyl pyrazoline derivatives (3a-3l) were designed and synthesized via Michael addition reaction through cyclization of chalcones with hydrazine hydrate in presence of formic acid. The structural elucidation of N-formyl pyrazoline derivatives was carried out by various spectroscopic techniques such as 1H, 13C NMR, FT-IR, UV-visible spectroscopy, mass spectrometry and elemental analysis. Anticancer activity of the pyrazoline derivatives (3a-3l) was evaluated against human lung cancer (A549), fibrosarcoma cell lines (HT1080) and human primary normal lung cells (HFL-1) by MTT assay. The results of anticancer activity showed that potent analogs 3b and 3d exhibited promising activity against A549 (IC50 = 12.47 ± 1.08 and 14.46 ± 2.76 µM) and HT1080 (IC50 = 11.40 ± 0.66 and 23.74 ± 13.30 µM) but low toxic against the HFL-1 (IC50 = 116.47 ± 43.38 and 152.36 ± 22.18 µM). The anticancer activity of potent derivatives (3b and 3d) against A549 cancer cell line was further confirmed by flow cytometry based approach. DNA binding interactions of the pyrazoline derivatives 3b and 3d have been carried out with calf thymus DNA (Ct-DNA) using absorption, fluorescence and viscosity measurements, circular dichroism and cyclic voltammetry. Antioxidant potential of N-formyl pyrazoline derivatives (3a-3l) has been also estimated through DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical and H2O2. Results revealed that all the compounds exhibited significant antioxidant activity. In silico molecular modelling and ADMET properties of pyrazoline derivatives were also studied using PyRx software against topoisomerase II receptor with PDB ID: 1ZXM to explore their best hits. MD simulation of 3b and 3d was also carried out with topoisomerase II for structure-function correlation in a protein. HuTopoII inhibitory activity of the analogs (3a-3l) was examined by relaxation assay at varying concentrations 100-1000 µM.

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals.

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman-Harris window), EEG time window choices (-750 ms to 0 ms and -250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch's method, Fast Fourier Transform, and Burg's method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

Design, synthesis, cytotoxicity, HuTopoIIα inhibitory activity and molecular docking studies of pyrazole derivatives as potential anticancer agents.

In an attempt to find potential anticancer agents, a series of novel ethyl 4-(3-(aryl)-1-phenyl-1H-pyrazol-4-yl)-2-oxo-6-(pyridin-3-yl)cyclohex-3-enecarboxylates 5a-i and 5-(3-(4-fluorophenyl)-1-phenyl-1H-pyrazol-4-yl)-3-(pyridin-3-yl)-4,5-dihydropyrazole-1-carbothioamides 6a-i were designed, synthesized and evaluated for their topoisomerase IIα inhibitory activity and in vitro cytotoxicity against a panel of cancerous cell lines (MCF-7, NCI-H460, HeLa) and a normal cell line (HEK-293T). Molecular docking studies of all the synthesized compounds into the binding site of topoisomerase IIα protein (PDB ID: 1ZXM) were performed to gain a comprehensive understanding into plausible binding modes. These compounds were also screened for in silico drug-likeliness properties on the basis of the absorption, distribution, metabolism and excretion (ADME) prediction. Among all the synthesized compounds, analogue 5d showed superior cytotoxicity with an IC50 value of 7.01±0.60µM for HeLa, 8.55±0.35µM for NCI-H460 and 14.31±0.90 for MCF-7 cancer cell lines. Further, compound 5d showed 70.82% inhibition of topoisomerase IIα at a concentration of 100µM with maximum docking score of -8.24. Results of ADME prediction revealed that most of these compounds showed in silico drug-likeliness properties within the ideal range.