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We investigated the association between six common and novel interleukin-6 (IL-6) polymorphisms with the risk of cervical cancer (CC) among Tunisians. Study subjects comprised 112 CC cases and 164 control women. Genotyping of IL-6 rs2069845, rs2069840, rs1474348, rs1800795, rs1800797, rs2069827 variants was done by real-time PCR, with defined clusters. The allelic and genotypic distributions of the tested IL-6 SNPs were comparable between CC patients and control women. Stratification according to FIGO staging revealed that rs1800795 homozygous major allele genotype (P = 0.033; OR =0.49(0.25-0.95)) and major allele (P = 0.037; OR = 0.57 (0.33-0.97)) were protective of CC. Moreover, carriage of rs1474348 major allele was also protective of CC (P = 0.014; OR = 0.53(0.32-0.88)), while higher rs1474348 minor allele frequency was seen in CC patients with early FIGO stage (P = 0.044; OR = 0.39 (0.15-1.00)), thus implicating rs1474348 in CC evolution and progression of angiogenesis. Haploview analysis demonstrated high linkage disequilibrium (LD) between rs2069845, rs2069840, rs1474348 and rs1800795, and 6-locus haplotype analysis identified GACCCA haplotype to be positively associated with increased CC, while GAGGGG haplotype was negatively associated with CC, thus suggesting a protective role for this haplotype in CC. Furthermore, there was a significant association between the incidence of CC and the use hormonal contraception (P = 0.047; OR = 1.97 (0.94-4.13)) and smoking (P < 0.001; OR = 7.12 (2.97-17.04)). The IL-6 variants rs1800795 and rs1474348, and haplotypes GACCCA and GAGGGG, along with use of hormonal contraceptives and smoking, are major risk factors of CC susceptibility and evolution among Tunisian women.

Communication between the nodes in a vehicle is performed using many protocols. The most common of these is known as the Controller Area Network (CAN). The functionality of the CAN protocol is based on sending messages from one node to all others throughout a bus. Messages are sent without either source or destination addresses. Consequently, it is simple for an attacker to inject malicious messages. This may lead to some nodes malfunctioning or to total system failure, which can affect the safety of the driver as well as the vehicle. Detecting intrusions is a challenging problem when using a CAN bus protocol for in-vehicle communication. Most existing work focuses on the physical aspects without taking into consideration the data itself. Machine Learning (ML) tools, especially classification techniques, have been widely used to address similar problems. In this paper, we use and compare several ML techniques to deal with the problem of detecting intrusions in in-vehicle communication. An experimental study is performed using a real dataset extracted from a KIA Soul car. Compared to previous work, which focuses on detecting intrusions based on the physical aspect, in this paper, data analysis and statistical learning techniques are applied. Furthermore, the paper provides a comparative study of the most common ML techniques. The results show that the techniques proposed in this paper outperform other techniques that have been used previously.