Publications by authors named "G Sakellaropoulos"

Minimally invasive thoracic techniques often need to be converted to open thoracotomy. Thoracotomy is associated with severe postoperative pain in 50% of the patients, and this situation can be maintained for a prolonged period. Many efforts have been made to avoid this complication.

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Background: Immunoglobulin A nephropathy (IgAN) is among the commonest glomerulonephritides in Greece and an important cause of end-stage kidney disease (ESKD) with an insidious chronic course. Thus, the recently published International IgAN prediction tool could potentially provide valuable risk stratification and guide the appropriate treatment module. This study aimed to externally validate this prediction tool using a patient cohort from the IgAN registry of the Greek Society of Nephrology.

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The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM.

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Background:   Worldwide, the incidence of melanoma is increasing, while late diagnosis is related to poor prognosis. A significant risk marker for melanoma is the presence of atypical nevi; therefore, it is of outstanding importance to make accurate clinical classification of common benign nevi, atypical nevi, and melanomas. The non-invasive method of dermoscopy allowed for the visualization of structures invisible to the naked eye and undoubtedly advanced the assessment of melanocytic lesions to a new dimension.

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Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis.

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