Background and aim: Early detection of hepatocellular carcinoma (HCC) enhances eective and curative
management. New genetic markers with distinct diagnostic ability are required. Aim: determine the expression
of GPC3, PEG10, SERPINI1, MK and QP-C in the peripheral blood of HCC patients.
Methods: 74 HCV patients were recruited and divided into three groups; chronic hepatitis (I), liver cirrhosis
(II) and HCC (III). Demographics, laboratory and imaging data were collected. Child score and metastatic
work up were completed. e expression of the ve candidate genes in the peripheral blood was performed
by qRT-PCR assay.
Results: Groups were gender matched, age in group I was signicantly lower than in groups II and III (37.7
vs 50.4 and 55.6, p value <0.005). CHILD score; group II and III A/B/C = (7/5/6) and (20/6/3). AFP was
signicantly higher in group III than I and II (204 vs 3.9 and 6.9, p < 0.01). In HCC group 69% of the lesions
were < 5 cm, and had 1-2 nodules; 14% had metastases. GPC3, PEG10, SERPINI1 and MK mRNA were
signicantly higher in the HCC group compared to the other groups while QP-C mRNA was higher in chronic
hepatitis C group compared to other groups. e gene expression values in HCC patients were independent
of the tumor size, AFP levels or extrahepatic metastasis. Combined measurement of the ve gene markers
showed 100% sensitivity and 33% specicity, 48% PPV and 100% NPV.
Conclusion: GPC3, PEG10, SERPINI1 and MK are genetic markers that can represent a useful tool for
detection of HCC.

The university course timetabling problem (UCTP) is a combinatorial NP-complete problem that has been subject to research since the early 1960’s. Numerous solution techniques have been applied to the timetabling problem ever since. This paper aims at formulating an immune-inspired algorithm, namely the Clonal Selection Algorithm1 (CSA1) and testing its ability in solving the UCTP against the Genetic algorithm (GA). An Immune-Genetic algorithm (IGA) was also created, which combines the crossover operator borrowed from the genetic algorithm with immune-inspired concepts. Also, experimenting with the effects of changing the selection and re-selection schemes of the algorithms motivated the creation of a second
version of CSA1, that is CSA2 and three more versions of IGA: IGA1, IGA2 and IGA3. All the devised algorithms were contrasted in their performances against the GA. Enhancements were applied to the mutation operator of the formulated algorithms by introducing a ‘move factor’. As a means of improving the results attained by the algorithms, local search consisting of three variable neighborhoods was incorporated into each of them. The algorithms were tested over two problem instances, with varying complexities and the results demonstrate the effectiveness of the algorithms in solving the UCTP.

Proteins are vital components of living cells. A number of diseases such as Alzheimer's, Cystic fibrosis and Mad Cow diseases are shown to result from malfunctioning of proteins. Protein folding problem is the process of predicting the optimal 3D molecular structure of a protein, or tertiary structure, which is an indication of its proper function. An enhancement over Cellular Genetic Algorithm (CGA) and Cellular Estimation of Distribution Algorithm (CEDA) was made to minimize the energy of proteins indicating how far it is from its optimal 3D structure. Energy was calculated using the Empirical Conformational Energy Program for Peptides (ECEPP) package. Experiments were performed on the Met-enkephalin protein. The enhanced algorithms reached energy of -10.1 and -10.78 for CGA and CEcGA surpassing the Elitism-based compact Genetic Algorithm and Breeder Genetic Algorithm which didn't reach this energy but reached-7.378, and Breeder Genetic Algorithm (BGA) which reached -3. Results show that the proposed two algorithms "CGA and CEDA" are better than DGA, BGA, and EcGA and a computational alternative to costly laboratory methods and an efficient means for solving organic docking problems.