Synthesis, anticancer activity, and molecular docking of half-sandwich iron(II) cyclopentadienyl complexes with maleimide and phosphine or phosphite ligands.

In these studies, we designed and investigated the potential anticancer activity of five iron(II) cyclopentadienyl complexes bearing different phosphine and phosphite ligands. All complexes were characterized with spectroscopic analysis viz. NMR, FT-IR, ESI-MS, UV-Vis, fluorescence, XRD (for four complexes) and elemental analyses. For biological studies, we used three types of cells-normal peripheral blood mononuclear (PBM) cells, leukemic HL-60 cells and non-small-cell lung cancer A549 cells. We evaluated cell viability and DNA damage after cell incubation with these complexes. We observed that all iron(II) complexes were more cytotoxic for HL-60 cells than for A549 cells. The complex CpFe(CO)(P(OPh)3)(η1-N-maleimidato) 3b was the most cytotoxic with IC50 = 9.09 µM in HL-60 cells, IC50 = 19.16 µM in A549 and IC50 = 5.80 µM in PBM cells. The complex CpFe(CO)(P(Fu)3)(η1-N-maleimidato) 2b was cytotoxic only for both cancer cell lines, with IC50 = 10.03 µM in HL-60 cells and IC50 = 73.54 µM in A549 cells. We also found the genotoxic potential of the complex 2b in both types of cancer cells. However, the complex CpFe(CO)2(η1-N-maleimidato) 1 which we studied previously, was much more genotoxic than complex 2b, especially for A549 cells. The plasmid relaxation assay showed that iron(II) complexes do not induce strand breaks in fully paired ds-DNA. The DNA titration experiment showed no intercalation of complex 2b into DNA. Molecular docking revealed however that complexes CpFe(CO)(PPh3) (η1-N-maleimidato) 2a, 2b, 3b and CpFe(CO)(P(OiPr)3)(η1-N-maleimidato) 3c have the greatest potential to bind to mismatched DNA. Our studies demonstrated that the iron(II) complex 1 and 2b are the most interesting compounds in terms of selective cytotoxic action against cancer cells. However, the cellular mechanism of their anticancer activity requires further research.

Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation.

Segmentation architectures based on deep learning proficient extraordinary results in medical imaging technologies. Computed tomography (CT) images and Magnetic Resonance Imaging (MRI) in diagnosis and treatment are increasing and significantly support the diagnostic process by removing the bottlenecks of manual segmentation. Cardiac Magnetic Resonance Imaging (CMRI) is a state-of-the-art imaging technique used to acquire vital heart measurements and has received extensive attention from researchers for automatic segmentation. Deep learning methods offer high-precision segmentation but still pose several difficulties, such as pixel homogeneity in nearby organs. The motivated study using the attention mechanism approach was introduced for medical images for automated algorithms. The experiment focuses on observing the impact of the attention mechanism with and without pretrained backbone networks on the UNet model. For the same, three networks are considered: Attention-UNet, Attention-UNet with resnet50 pretrained backbone and Attention-UNet with densenet121 pretrained backbone. The experiments are performed on the ACDC Challenge 2017 dataset. The performance is evaluated by conducting a comparative analysis based on the Dice Coefficient, IoU Coefficient, and cross-entropy loss calculations. The Attention-UNet, Attention-UNet with resnet50 pretrained backbone, and Attention-UNet with densenet121 pretrained backbone networks obtained Dice Coefficients of 0.9889, 0.9720, and 0.9801, respectively, along with corresponding IoU scores of 0.9781, 0.9457, and 0.9612. Results compared with the state-of-the-art methods indicate that the methods are on par with, or even superior in terms of both the Dice coefficient and Intersection-over-union.