Objectives This study aims to analyze human leukocyte antigen A (HLA-A), human leukocyte antigen B (HLA-B), human leukocyte antigen C (HLA-C), HLA-DRB1*, HLA-DRB3*, HLA-DRB4*, HLA-DRB5*, HLA-DQB1* loci expression in patients with rheumatoid arthritis (RA) in the Federation of Bosnia and Herzegovina. Patients and methods Deoxyribonucleic acid was isolated from peripheral blood of 48 RA patients (22 males, 26 females; mean age 36 years; range 2 to 63 years) and 104 healthy control individuals (52 males, 52 females; mean age 43 years; range 2 to 76 years). Deoxyribonucleic acid samples were analyzed using polymerase chain reaction-sequence-specific primers and sequence specific oligonucleotides methods. Results The most frequent allelic groups in RA patients were HLA-DRB1*01 (odds ratio=2.795; 95% confidence interval: 1.441-5.421; p=0.004) and HLA-DRB1*04 (odds ratio=2.573; 95% confidence interval: 1.214-5.453; p=0.023). Among RA patients, the most frequent genotype for the allelic group HLA-DRB1*, in the light of the common epitopes theory, was observed for DRB1*01/DRB1*13. This genotype indicates an increased incidence and relative risk (odds ratio=11.09). Conclusion The most common genotype in our RA patients was DRB1*01/DRB1*13, which showed increased frequency and a high relative risk. This genotype variant may be considered a predisposing factor for the development of RA.

Abstract This paper investigates the empirical relationship between corruption, economic growth, and government spending in fourteen transitioning economies from 1995-2013. We find strong evidence of bilateral Granger causation between economic growth and corruption for the full sample but weaker evidence of such a relationship for four former Yugoslav republics. We also find bilateral Granger causality between government spending and corruption but a weaker unidirectional Granger causality from government spending to corruption in four former Yugoslav republics. Our results recommend caution when assuming that corruption is purely exogenous in empirical models.

Chou, and Fasman developed the first empirical prediction method to predict secondary structure of proteins from their amino acid sequences. Subsequently, a more sophisticated GOR method has been developed. Although it became very popular among biologists, their accuracy was only slightly better than random. A significant improvement in prediction accuracy >70% has been achieved by ‘second generation’ methods such as PHD, SAM-T98, and PSIPRED, which utilized information concerning sequence conservation. Only recently F. B. Akcesme developed a local similarity based method to obtain an accuracy >90%in secondary structure prediction of any new protein. In this article we examined the possibility of sequence similarity based secondary structure prediction of proteins. To deal with this issue, all proteins of PDB dataset are searched for identical subsequences in the other larger proteins of PDB dataset. It is seen that around 17% of proteins in the PDB dataset have identical subsequences in other larger proteins of PDB dataset. When the secondary structures of proteins are assigned as the corresponding secondary structures of identical parts in other larger proteins, the average prediction accuracy is found to be 90.39 %. Therefore, we concluded that an unknown protein has a chance of 17 % to have an identical subsequence in a larger protein in Protein Data Bank (PDB), and there is a possibility that its secondary structure be predicted with around 90% accuracy with this method.