This study involved the effect of the aqueous extracts of two plants, Origanum vulgare L.(1), Trigonella Foenum Graecum L. (Fenugreek) seeds(2) on the growth of cancer cell lines. Rhabdomyo sarcomas (RD) of human cell line and female intestine cells of Albino mice (L20B) in vitro System. These extracts were compared with the known anticancer drug Cis-platinum(Cis-Pt) as a positive control. The phytochemical tests were used for screening the active compounds in plants. The inhibition activity assay was used as a parameter of the cytotoxic effect of these extracts. Cancer cell lines were treated with four concentrations of Cis-platin, 31.25, 62.5, 125 and 250 ?g/ml for 72 hour exposure time. The same concentrations were used for the other extracts. This study found that the two aqueous extracts (1,2) have a cytotoxic effects on cancer cells as could be seen from their effects on inhibition percentage and the significant differences (p<0.05) which were observed for each extract (1,2) by the increased the inhibition percentage as the concentration was increased. The higher level of inhibition(51.63%) was obtained from 250 ?g/ml of Origanum vulgare extract (1) on RD line and 51.41% on cell line L20B at the same concentration. The cytotoxic effects of extract 1 and 2 on cancer cell line L20B were similar to that on RD cell line.There are no significant differences between two cancer cell line in all used concentrations. The strong relationship which to be found between the concentrations and the two aqueous extracts (1,2) comparable with Cis-Pt drug.
Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number
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