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Non-small cell lung cancer (NSCLC) is reported to have a high incidence rate and is one of the most prevalent types of cancer contributing towards 85% of all incidences of lung cancer. Berberine is an isoquinoline alkaloid which offers a broad range of therapeutical and pharmacological actions against cancer. However, extremely low water solubility and poor oral bioavailability have largely restricted its therapeutic applications. To overcome these limitations, we formulated berberine-loaded liquid crystalline nanoparticles (LCNs) and investigated their in vitro antiproliferative and antimigratory activity in human lung epithelial cancer cell line (A549). 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), trypan blue staining, and colony forming assays were used to evaluate the anti-proliferative activity, while scratch wound healing assay and a modified Boyden chamber assay were carried out to determine the anti-migratory activity. We also investigated major proteins associated with lung cancer progression. The developed nanoparticles were found to have an average particle size of 181.3 nm with spherical shape, high entrapment efficiency (75.35%) and have shown sustained release behaviour. The most remarkable findings reported with berberine-loaded LCNs were significant suppression of proliferation, inhibition of colony formation, inhibition of invasion or migration via epithelial mesenchymal transition, and proliferation related proteins associated with cancer progression. Our findings suggest that anti-cancer compounds with the problem of poor solubility and bioavailability can be overcome by formulating them into nanotechnology-based delivery systems for better efficacy. Further in-depth investigations into anti-cancer mechanistic research will expand and strengthen the current findings of berberine-LCNs as a potential NSCLC treatment option., (© 2022. The Author(s).)

Epilepsy is a neurological disorder that affects people of any age, which can be detected by Electroencephalogram (EEG) signals. This paper proposes a novel method called Volume of Phase Space Representation (VOPSR) to classify seizure and seizure-free EEG signals automatically. Primarily, the recorded EEG signal is disintegrated into several Intrinsic Mode Functions (IMFs) using the Empirical Mode Decomposition (EMD) method and the three-dimensional phase space have been reconstructed for the obtained IMFs. The volume is measured for the obtained 3D-PSR for different IMFs called VOPSR, which is used as a feature set for the classification of Epileptic seizure EEG signals. Support vector machine (SVM) is used as a classifier for the classification of epileptic and epileptic-free EEG signals. The classification performance of the proposed method is evaluated under different kernels such as Linear, Polynomial and Radial Basis Function (RBF) kernels. Finally, the proposed method outperforms noteworthy state-of-the-art classification methods in the context of epileptic EEG signals, achieving 99.13% accuracy (average) with the Linear, Polynomial, and RBF kernels. The proposed technique can be used to detect epilepsy from the EEG signals automatically without human intervention.