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Backgrounds Colon cancer is the third most deadly and one of the most diagnosed diseases in the world. Although routine screening and early detection during last decades has improved the survival, colon cancer still claims hundreds of thousands lives each year worldwide. Surgery and chemotherapy is mainstay of current treatment, nevertheless toxicity associated with this treatment underscores the urgency of demand of a better therapeutics. Close to 50% of current chemotherapeutic drugs are direct or indirect descendants compounds isolated from medicinal plants, which indicate plants are great potential sources of novel therapeutics. In our literature review we found Eclipta alba to posses many pharmacological activities, including those with anticancer potentials. However, no study on anticancer activity of this kind has been reported. Methods Phytochemicals were extracted by maceration method from shade dried whole plant of Eclipta alba using methanol as a solvent. The anticancer effect of extract was investigated on various cancer cell lines like human colorectal carcinoma (HCT-116), human prostate cancer (PC-3), Michigan cancer foundation-breast cancer (MCF-7) and renal cell carcinoma (RCC-45). We have also studied the effects on normal human embryonic lung fibroblast cell (WI-38) using MTT (methyl thiazoldiphenyltetrazolium bromide) assay, clonogenic (colony formation) and migration assay. Finally obtained results were analyzed using ANNOVA and Dunnett’s test. Results Results obtained from MTT assay revealed that the methanolic extract of Eclipta alba carried significant (p < 0.005) specificity against HCT-116 cells as compared to the other cancer cells. This extract also showed minimal or nontoxicity to WI-38 cells. Migration as well as clonogenic assays also confirmed the anticancer potential of the extract against HCT-116 cells. Conclusion This is a unique finding of its kind because the specific anticancer effect with minimal toxicity on normal cells has not been reported on Eclipta alba extract. Finally this finding opens up a great possibility to develop a novel antitumor drug candidate against deadly colon cancer in the future.

Energy and exergy performances of natural circulation loop (NCL) with various water-based hybrid nanofluids (Al2O3 + TiO2, Al2O3 + CNT, Al2O3 + Ag, Al2O3 + Cu, Al2O3 + CuO, Al2O3 + graphene) with 1% volumetric concentration are compared in this study. New thermophysical property models have been proposed for hybrid nanofluids with different particle shapes and mixture ratio. Effects of power input, loop diameter, loop height, loop inclination and heater/cooler inclination on steady-state mass flow rate, effectiveness, and entropy generation are discussed as well. Results show that both the steady-state mass flow rate and energy–exergy performance are enhanced by using the hybrid nanofluids, except Al2O3 + graphene, which shows the performance decrement within the studied power range. Al2O3 + Ag hybrid nanofluid shows highest enhancement in mass flow rate of 4.8% compared to water. The shape of nanoparticle has shown a significant effect on steady-state performance; hybrid nanofluid having cylindrical and platelet shape nanoparticles yields lower mass flow rate than that of spherical shape. Mass flow rate increases with the increasing loop diameter and height, whereas decreases with the increasing loop and heater/cooler inclinations. Both effectiveness and entropy generation increase with the decreasing loop diameter and height, whereas increasing the loop and heater/cooler inclinations. This study reveals that the particle shape has a significant effect on the performance of hybrid nanofluids in NCL, and the use of hybrid nanofluid is more effective for higher power.

Deep learning is a subfield of machine learning and plays a vital role in the area of image processing, natural language processing, computer vision, etc. As compared to traditional machine learning methods, it has a strong ability of selflearning and self-debugging. Convolution neural network (CNN) is the most widely used technique of deep learning for better feature extraction from large datasets. Many researchers adopted CNN for object classification, face recognition, automatic handwritten, etc. In this paper, the detailed concepts behind CNN are discussed with their broad applications. Keywords Artificial neural network (ANN) • Machine learning • Deep learning •

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