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Background and Aim: Lung Cancer (LC) is a major cancer killer worldwide, and 5-yr survival is extremely poor (≤15%), accentuating the need for more effective diagnostic and therapeutic strategies. Studies have shown cell-free microRNAs (miRNAs) circulating in the serum and plasma with specific expression in cancer, indicating the potential of using miRNAs as biomarkers for cancer diagnosis and therapy. This study aimed to identify differentially-expressed two miRNAs in the plasma of Non-Small Cell Lung Cancer (NSCLC) patients that might be a clinically useful tool for lung cancer early detection. miRNA-21 is one of the most abundant oncomirs. miRNA-23a functions as an oncogene in several human cancers, however, its clinical value has not been investigated in NSCLC. Materials and Methods: A case-control study was conducted in Assiut University Hospital, Egypt, from 2017 to 2018. Plasma samples were obtained from 45 NSCLC patients. The expression level of miR-21 and miRNA-23a was detected by qRT-PCR and compared to 40 healthy control subjects. The relation between both miRNAs and clinicopathological parameters was evaluated. Results: The expression level of miR-21 and miRNA-23a was significantly up-regulated (36.9 ± 18.7 vs. 1.12 ± 0.84 and 24.7 ± 19.09 vs. 1.16 ± 0.45) in NSCLC compared to matched controls (P<0.0001each). There was a significant difference in the level of plasma miRNA-21 and miRNA- 23a expression between the different grades of the disease (P = 0.032 and P = 0.001, respectively). The plasma miRNA-21 and miRNA-23a levels in the lung cancer patients with distant metastasis (n = 20) were significantly higher than those in the patients without metastasis (n = 25) (P<0.0001 each), the expression of miR-21 and miRNA-23a was significantly associated with tumor size (P = 0.001, P = 0.0001, respectively), but not significantly related to lymph node metastasis (P = 0.687 and 0.696, respectively). A positive correlation was observed between miRNA-21 and miRNA-23a (r = 0.784, P<0.01), There was no significant difference in the plasma miRNA-21 and miRNA-23a levels in the lung cancer patients with different histopathological types. Conclusion: miR-21 and miR-23a might play an oncogenic role in LC and is a poor prognostic factor. Switching off miRNA-21 and miRNA-23a may improve the treatment of LC. Our results must be verified by large-scale prospective studies with standardized methodology.

Background: Lung cancer is one of the main human health threats. Survival of lung cancer patients depends on the timely detection and diagnosis. Among the genetic irregularities that control cancer development and progression, there are microRNAs (miRNAs). This study aimed to assess the plasma level of circulating miRNA-17 and miRNA-222 as non-invasive markers in non-small-cell lung cancer (NSCLC) patients. Patients and methods: A total of 40 patients with NSCLC and 20 healthy controls who were matched in terms of age and sex with the patient group were included in this case-control study.. Estimation of miRNA-17 and miRNA-222 expression profiles in the plasma was done using quantitative real-time PCR (qRT-PCR). The relationship between both markers and their clinicopathological features were also determined. Receiver operating characteristic (ROC) curve analysis was done to evaluate the role of these microRNAs in NSCLC diagnosis and follow-up. Results: MiRNA-17 and miRNA-222 levels were significantly upregulated in NSCLC patients compared with controls (48.32±12.35 vs 1.16±0.19 and 34.53±3.1 vs 1.22±0.14) (P=0.000). Plasma miRNA-17 level was increased, and the miRNA-222 level was decreased across different stages of the disease; however, these differences d were not statistically significant (P=0.4, P=0.5, respectively). The miRNA-17 levels were higher in the lung cancer patients with metastasis , but miRNA-222 levels were lower patients without metastasis. We found no statistically significant difference in this regard(P=0.4 vs P=0.3, respectively). ROC curve analysis showed that the sensi¬tivity and specificity of miRNA-17 were 77.78% and 87.50% , and of miRNA-222 were 50% and 88.89%. Conclusion: MiRNA-17 and miRNA-222 can be considered as non-invasive biomarkers for detection of early lung carcinogenesis and metastasis in patients with NSCLC, hence providing a basis for the development of novel therapeutic approaches.

These days, the classification between normal and cancerous tissues and between different types of cancers represents a very important issue. Selecting the little informative number of genes is considered the main challenge in the cancer diagnosis issue. Therefore, Gene selection is usually the preliminary step for solving the cancer classification problems. Bio-inspired metaheuristic optimization algorithms, when used to solve gene selection and classification problems, they demonstrate their effectiveness. Barnacles Mating Optimizer (BMO) algorithm, which imitates the behavior of mating barnacles in nature for solving optimization problems, is considered one of these algorithms. In this paper, Barnacles Mating Optimizer (BMO) algorithm augmented with Support Vector Machines (SVM) called BMO-SVM is proposed for a microarray gene expression profiling in order to select the most predictive and informative genes for cancer classification. Conducting a comparative experimental study among a set of the most common bio-inspired optimization techniques to specify the most effective. A binary microarray dataset (i.e., leukemia1) and a multi-class microarray dataset (i.e., SRBCT, lymphoma, and leukemia2) are used for testing the performance of the proposed model. The experimental results revealed the superiority of the proposed BMO-SVM approach against several well-known meta-heuristic optimization algorithms, such as the Tunicate Swarm Algorithm (TSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). It is worth mentioning that our proposed algorithm achieves a high informational superiority percentage compared to other algorithms.

Rumors and misleading information detection and prevention still represent a big challenge against social network developers and researchers. Since newsworthy information propagation is a traditional behavior of most of the users in social media, then verifying information credibility and reliability is indeed a vital security requirement for social network platforms. Due to its immutability, security, tamperproof and P2P design, Blockchain as a powerful technology can provide a magical solution to overcome this challenge. This Paper introduces a novel blockchain approach called Proof of Credibility (PoC) for detecting fake news and blocking its propagation in social networks. The functionality of the PoC protocol has been simulated on two datasets of newsworthy tweets collected from different news sources on Twitter. The results clarified a satisfying performance and efficiency of the proposed approach in detecting rumors and blocking its propagation.

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Recently, Internet of Things (IoT)-based systems, especially automation systems, have become an indispensable part of modern-day lives to support the controlling of the networked devices and providing context-aware and intelligent environments. IoT-based services/apps developed by the end-users interact with each other and share concurrent access to devices according to their preferences, which increases safety, security, and correctness issues in IoT systems. Due to the critical impacts resulting from these issues, IoT-based apps require a customized type of compilers or checking tools that capable of analyzing the structures of these apps and detecting different types of errors and conflicts either in intra-IoT app instructions or in inter-IoT apps interactions. A plethora of approaches and frameworks have been proposed to assist the best practices for end-users in developing their IoT-based apps and mitigate these errors and conflicts. This paper focuses on conflict classification and detection approaches in the context of IoT systems by investigating the current research techniques that provided conflicts’ classification or detection in IoT systems (published between 2014 and 2020). A classification of IoT-based apps interaction conflicts is proposed. The proposed conflicts’ classification provides a priori conflicts detection method based on the analysis of IoT app instructions’ relationships with utilizing the state-of-the-art Satisfiability Modulo Theories (SMT) model checking and formal notations. The current detection approaches are compared with each other according to the proposed conflicts’ classification to determine to which extend they cover different conflicts. Based on this comparison, we provide evidence that the existing approaches have a gap in covering different conflicts’ levels and types which yields to minimize the correctness and safety of IoT systems. We point out the need to develop a safety and security compiler or tool for IoT systems. Also, we recommend using a hybrid approach that combines model checking with a variety of languages and semantic technologies in developing future IoT-based apps verification frameworks to cover all levels and types of conflicts to guarantee and increase the safety, security, and correctness of IoT systems.