Herbal Melanin Inhibits Real-Time Cell Proliferation, Downregulates Anti-Apoptotic Proteins and Upregulates Pro-Apoptotic p53 Expression in MDA-MB-231 and HCT-116 Cancer Cell Lines.

Background and Objectives: Cancer is the second-most-important deadly disease in the world, leading to severe socioeconomic consequences and posing a public threat. Consequently, breast and colorectal cancers are significant cancer types that affect women and men more commonly, respectively. Treatment failure or recurrent diseases frequently occur due to resistance, in addition to the side effects of the currently available anticancer agents. Therefore, in this study, herbal melanin anticancer activity was investigated against human breast adenocarcinoma (MDA-MB-231) and human colorectal (HCT 116) cell proliferation and the expression of downregulated anti-apoptotic proteins and upregulated pro-apoptotic p53. Materials and Methods: MDA-MB-231 and HCT 116 cells were monitored for their real-time proliferation properties using Xcelligence. Herbal melanin of various concentrations significantly inhibited MDA-MB-231 and HCT 116 cell proliferation. Then, the expression of proapoptotic and anti-apoptotic proteins such as p53, Bcl-2 and Bcl-xl was studied using Western blotting. Results: The Bcl-2 and Bcl-xl expressions were downregulated, while the p53 expression was upregulated after treatment with herbal melanin. Similarly, the expression of apoptotic proteins such as Bcl-2, Bcl-xl, XIAP, Survivin, Bid, Bax, p53, Cytochrome C, PARP genes and mRNA was studied after herbal melanin treatment using real-time PCR, which revealed the downregulation of Bcl-2, Bcl-xl, XIAP and Survivin and the upregulation of Bid, Bax, p53, Cytochrome C and PARP apoptotic protein expression. Also, caspase 3 and 9 expressions were monitored after the treatment with herbal melanin, which revealed the upregulation of both the MDA-MB-231 and HCT 116 cell types. Conclusions: Overall, herbal melanin can be used as an alternative anticancer agent against the MDA-MB-231 and HCT 116 cell types.

Theoretical and experimental spectroscopic studies and analysis for wave function on N-phenylmorpholine-4-carboxamide benzene-1,2-diamine with computational techniques.

The present study focuses on structural and chemical analyses of N-phenylmorpholine-4-carboxamide benzene-1,2-diamine (PMCBD) using quantum computational methods. The calculated bond angle, length, and dihedral angle between atoms were compared with measured values. The observed and stimulated FT-IR (Fourier Transform Infrared Spectroscopy) spectra parameters for vibrational wavenumbers and their associated PED (Potential Energy Distribution) values in percentage have been obtained from VEDA4 software. The electronic transitions of PMCBD were discussed by TD-SCF/DFT/B3LYP based on the 6-311++G(d,p) basis set with solvents such as chloroform, ethanol, and dimethyl sulfoxide (DMSO) and gas. Density functional computations were used to study the band energy between HOMO and LUMO using the B3LYP/6-311++G(d,p) level. Mulliken analysis and natural population analysis were used for a better understanding of charge levels on different atoms such as N, H and O. The natural bonding orbital (NBO) analysis proved helpful in studying molecular and bond strengths. (NBO). The ESP acquired data on the molecule's size, shape, charge density distribution, and chemical reactivity site. This was done by mapping electron density on the surface with electrostatic potential. Non-linear optical detection of PMCBD was also discussed. Aside from the electron localization function map, state densities are also mapped using Multiwfn software, a wave function analyzer.

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network.

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.