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

Coordination of bioactive phytochemicals from Aloe vera extracts to metal ions; investigation of the metal complexes and bioactive compound formed

The bioactive compounds contained within many plants account for their pharmacological values. Aloe vera has a wide range of organic and inorganic components, including carbohydrate polymers, glucomannans, and a variety of other natural and synthetic materials. The study aims to take a look into the characteristics of some metal complexes produced from Aloe vera extracts. The extracts from Aloe vera were derived by means of acetone, distilled water and ethanol. The solubility of the metal complexes with the ligand at varying temperatures was established. FT-IR was used to carry out the infra-red examination of the ligand. The results revealed that alcoholic extract of Aloe vera leaf was not soluble in Cu, Fe, or Zn but only soluble in Fe, the extract by distilled water was soluble in Cu, Fe and Zn. However, the Aloe vera in acetone as well as in the Zn (II) and Cu (II) composites displayed a bending that was found at 1430.97 cm-1, 1500.01 cm-1 and 1615.90 cm-1.every functional groups are assigned to be coordinating sites as a result of increase or decrease in the wave number, and absorption band. Findings from the investigation reveal that the complexion of the metal salts with diverse donor sites in the extract is indicated by an increase in the absorption peak of the functional groups in the metal composites of the extracts.

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.

Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models-such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier-is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.

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.