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Calotropis gigantea essential oil is utilized in outmoded medicine, therapeutics, and the cosmetic industries. However, the extreme volatility, oxidation susceptibility, and instability of this oil restricts its application. Thus, encapsulation is a more effective method of shielding this oil from unfavorable circumstances. The creation of oil/water (O/W) nanoemulsions based on Calotropis gigantea essential oil (CEO), known as CNE ( Calotropis gigantea essential oil nanoemulsions), and an assessment of its biological potential were the goals of this work. UV, fluorescence, and FT-IR methods were used for physiological characterization. Biological activities, including anti-inflammatory, anti-diabetic, and anti-cancer effects. Studies on the pharmacokinetics of CNE were conducted. CNEs encapsulation efficiency was found to be 92%. The CNE nanoemulsions had a spherical shape with polydispersity index of 0.531, size of 200 nm, and a zeta potential of -35.9 mV. Even after being stored at various temperatures for 50 days, CNE nanoemulsions remained stable. Numerous tests were used to determine the antioxidant capacity of CNE, and the following IC50 values (µl/mL) were found: iron chelating assay: 18, hydroxyl radical scavenging: 37, and nitric oxide radical scavenging activity: 58. The percentage of HeLa cells that remained viable after being treated with CNE was 41% at a higher dose of 1 µl. CNE inhibited α-amylase in a dose-dependent manner, with 72% inhibition at its higher dose of 250 µL. Research on the kinetics of drugs showed that nanoemulsions showed Higuchi pattern. This research showed potential use of Calotropis gigantea oil-based nanoemulsions in the food, cosmetic, and pharmaceutical industries.

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects., (© 2022. The Author(s).)