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Query: UMLS:C0029463 (osteosarcoma)
 16,637 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

In 23 treatment courses of high-dose Methotrexate (MTX) with Leukovorin-rescue for osteogenic sarcoma serial EEG-examinations before and after MTX-infusion were done. In 11 course Vincristine had been administered additionally. Frequency-analysis of EEG-background activity was performed by computer-based FFT. At the end of the MTX-infusion EEGs were unchanged, compared with the findings before start of treatment. At 24 and 48 hours after start of infusion, there was a slight but statistically significant slowing, recognizable from an increase of the theta/alpha-ratio (median + 48%, range -7% to + 373%) and a drop of dominant frequency (median-8%, range +12% to -53%). There was a stronger trend towards normalisation in patients, not having received Vincristine. The greatest changes were found in a patient, whose serum-MTX-concentration at 24 hours exceeded the upper therapeutic limit. The EEG-findings are discussed as an equivalent of a subclinical MTX-encephalopathy. Acute encephalopathies with severe EEG-changes and morphological changes on CT have been observed in MTX-intoxications.
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PMID:[EEG-slowing after high-dose Methotrexate with citrovorumfactor-rescue. A spectralanalytic study (author's transl)]. 680 66

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the microscope images, we develop a new CNN (convolutional neural network) architecture SW-UNet (small-world U-Net), which is a CNN that reflects the concept of the small-world network. The SW-UNet shows better performance in wrinkle segmentation task compared to other methods: the error (Euclidean distance) of SW-UNet is 4.9 times smaller than the 2D-FFT (fast Fourier transform) based segmentation approach, and is 2.9 times smaller than U-Net. As a demonstration, here we compare the contractile force of U2OS (human osteosarcoma) cells and show that cells with a mutation in the KRAS oncogene show larger force compared to wild-type cells. Our new machine learning based algorithm provides us an efficient, automated and accurate method to evaluate the cell contractile force.
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PMID:Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet. 3264 8