Iron oxide nanoparticles synthesized usingMentha spicataextract and evaluation of its antibacterial, cytotoxicity and antimigratory potential on highly metastatic human breast cells.

Iron oxide nanoparticles (Fe2O3NPs) were synthesized utilizingMentha spicatasourced from Cyprus as a stabilizing agent. The study delved into assessing the antimicrobial, cytotoxic, anti-proliferative, and anti-migratory potential of Fe2O3 NPs through disc diffusion, trypan blue, and 3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide (MTT) assay, respectively. Characterization of the synthesized Fe2O3 NPs was conducted using Fourier-transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), UV-vis spectroscopy (UV-vis), scanning electron microscopy (SEM), and energy-dispersive x-ray spectroscopy (EDX). The FTIR, XRD, and SEM-EDX spectra confirmed the successful formation of Fe2O3 NPs. The analysis of UV-vis spectra indicates an absorption peak at 302 nm, thereby confirming both the successful synthesis and remarkable stability of the nanoparticles. The nanoparticles exhibited uniform spherical morphology and contained Fe, O, and N, indicating the synthesis of Fe2O3NPs. Additionally, the Fe2O3NPs formed through biosynthesis demonstrated antimicrobial capabilities againstEscherichia coliandBacillus cereus. The significant anti-migratory potential on MDA-MB 231 human breast cancer cells was observed with lower concentrations of the biosynthesized Fe2O3NPs, and higher concentrations revealed cytotoxic effects on the cells with an IC50of 95.7µg/ml. Stable Fe2O3NPs were synthesized usingMentha spicataaqueous extract, and it revealed antimicrobial activity onE. coliandB. cereus, cytotoxic, anti-proliferative and anti-migratory effect on highly metastatic human breast cancer cell lines.

A modified Tseng algorithm approach to restoring thoracic diseases' computerized tomography images.

It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases' computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models.

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 µg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 µg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.

Prediction of cell migration potential on human breast cancer cells treated with Albizia lebbeck ethanolic extract using extreme machine learning.

Cancer is one of the major causes of death in the modern world, and the incidence varies considerably based on race, ethnicity, and region. Novel cancer treatments, such as surgery and immunotherapy, are ineffective and expensive. In this situation, ion channels responsible for cell migration have appeared to be the most promising targets for cancer treatment. This research presents findings on the organic compounds present in Albizia lebbeck ethanolic extracts (ALEE), as well as their impact on the anti-migratory, anti-proliferative and cytotoxic potentials on MDA-MB 231 and MCF-7 human breast cancer cell lines. In addition, artificial intelligence (AI) based models, multilayer perceptron (MLP), extreme gradient boosting (XGB), and extreme learning machine (ELM) were performed to predict in vitro cancer cell migration on both cell lines, based on our experimental data. The organic compounds composition of the ALEE was studied using gas chromatography-mass spectrometry (GC-MS) analysis. Cytotoxicity, anti-proliferations, and anti-migratory activity of the extract using Tryphan Blue, MTT, and Wound Heal assay, respectively. Among the various concentrations (2.5-200 µg/mL) of the ALEE that were used in our study, 2.5-10 µg/mL revealed anti-migratory potential with increased concentrations, and they did not show any effect on the proliferation of the cells (P < 0.05; n ≥ 3). Furthermore, the three data-driven models, Multi-layer perceptron (MLP), Extreme gradient boosting (XGB), and Extreme learning machine (ELM), predict the potential migration ability of the extract on the treated cells based on our experimental data. Overall, the concentrations of the plant extract that do not affect the proliferation of the type cells used demonstrated promising effects in reducing cell migration. XGB outperformed the MLP and ELM models and increased their performance efficiency by up to 3% and 1% for MCF and 1% and 2% for MDA-MB231, respectively, in the testing phase.