Malignancies are still responsible for a large share of lethalities. Macroscopical evaluation of the surgical resection margins is uncertain. Big data based imaging approaches have emerged in the recent decade (mass spectrometry, two-photon microscopy, infrared and Raman spectroscopy). Indocianine green labelled MS is the most common approach, however, label free midinfrared imaging is more promising for future practical application. We aimed to identify and separate different transformed (A-375, HT-29) and non-transformed (CCD986SK) cell lines by a label-free infrared spectroscopy method. Our approach applied a novel setup for label-free mid-infrared range classification method. Transflection spectroscopy was used on aluminium coated glass slides. Both whole range spectra (4000-648 cm −1) and hypersensitive fingerprint regions (1800-648 cm −1) were tested on the imaged areas of cell lines fixed in ethanol. Non-cell spectra were possible to be excluded based on mean transmission values being above 90%. Feasibility of a mean transmission based spectra filtering method with principal component analysis and linear discriminant analysis was shown to separate cell lines representing different tissue types. Fingerprint region resulted the best separation of cell lines spectra with accuracy of 99.84% at 70-75 mean transmittance range. Our approach in vitro was able to separate unique cell lines representing different tissues of origin. Proper data handling and spectra processing are key steps to achieve the adaptation of this dye-free technique for intraoperative surgery. Further studies are urgently needed to test this novel, marker-free approach.
The unique character of selenium compounds, including sodium selenite and Se-methylselenocysteine (MSC), is that they exert cytotoxic effects on neoplastic cells, providing a great potential for treating cancer cells being highly resistant to cytostatic drugs. However, selenium treatment may affect microRNA (miRNA) expression as the pattern of circulating miRNAs changed in a placebo-controlled selenium supplement study. This necessitates exploring possible changes in the expression profiles of miRNAs. For this, miRNAs being critical for liver function were selected and their expression was measured in hepatocellular carcinoma (HLE and HLF) and cholangiocarcinoma cell lines (TFK-1 and HuH-28) using individual TaqMan MicroRNA Assays following selenite or MSC treatments. For establishing tolerable concentrations, IC 50 values were determined by performing SRB proliferation assays. The results revealed much lower IC 50 values for selenite (from 2.7 to 11.3 μM) compared to MSC (from 79.5 to 322.6 μM). The treatments resulted in cell line-dependent miRNA expression patterns, with all miRNAs found to show fold change differences; however, only a few of these changes were statistically different in treated cells compared to untreated cells below IC 50. Namely, miR-199a in HLF, miR-143 in TFK-1 upon MSC treatment, miR-210 in HLF and TFK-1, miR-22,-24,-122, −143 in HLF upon selenite treatment. Fold change differences revealed that miR-122 with both selenium compounds, miR-199a with MSC and miR-22 with selenite were affected. The miRNAs showing minimal alterations included miR-125b and miR-194. In conclusion, our results revealed moderately altered miRNA expression in the cell lines (less alterations following MSC treatment), being miR-122, −199a the most affected and miR-125b,-194 the least altered miRNAs upon selenium treatment.
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam’s decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 μm2 tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research.
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