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Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. In this paper, a novel diagnostics approach based on machine learning techniques is presented. Logistic regression is one of the most classic machine learning models used to solve the problem of binary classification. In typical implementations, logistic regression coefficients are optimized using iterative methods. Additionally, parameters such as solver, C - a regularization parameter or the number of iterations of the algorithm operation should be selected. In our research, we propose a combination of logistic regression with genetic algorithms. We present three experiments showing the fusion of those methods. In the first experiment, we genetically select the logistic regression parameters, while the second experiment extends this approach by including a genetic selection of features. The third experiment presents a novel approach to train the logistic regression model - the genetic selection of coefficients (weights). Our models are tested for the survival prediction of hepatocellular carcinoma based on patient data collected at Coimbra's Hospital and Universitary Center (CHUC), Portugal. The model we proposed achieved a classification accuracy of 94.55% and an f1-score of 93.56%. Our algorithm shows that machine learning techniques optimized by the proposed concept can bring a new and accurate approach in HCC diagnosis with high accuracy., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

SSF3 is a CHO cell line adapted for growth in protein-free medium. It grows in suspension unless serum-derived attachment factors such as vitronectin are added to the medium. Serum-independent cell lines, which adhere to the substrate after induction with dexamethasone or constitutively, were created by transfection with a human vitronectin gene under control of the mouse mammary tumor-virus promoter. Substrate attachment and SSF3VN-cell spreading could be prevented with an RGD peptide (arginine-glycine-aspartic acid) confirming that attachment is mediated by an intregrin receptor. Hormone-inducible attachment could be blocked by glucocorticoid antagonist promegestone. All steps in the isolation of stable transfected SSF3VN cell lines could be done in a chemically defined medium avoiding the risk of introduction of serum-derived infectious agents. SSF3VN cells could be grown in protein-free medium in solid-phase large-scale bioreactors. Application in microplates as used in high-throughput screening was demonstrated in an assay of Ca(2+) release from internal stores induced by agonist-binding to recombinant human metabotropic glutamate receptor hmGluR1b., (Copyright 1999 John Wiley & Sons, Inc.)