Latcripin-7A from Lentinula edodes C91-3 induces apoptosis, autophagy, and cell cycle arrest at G1 phase in human gastric cancer cells via inhibiting PI3K/Akt/mTOR signaling.

Gastric cancer (G.C) is one of the most lethal cancer types worldwide. Current treatment requires surgery along with chemotherapy, which causes obstacles for speedy recovery. The discovery of novel drugs is needed for better treatment of G.C with minimum side effects. Latcripin-7A (LP-7A) is a newly discovered peptide extracted from Lentinula edodes. It is recently studied for its anti-cancer activity. In this study, LP-7A was modeled using a phyre2 server. Anti-proliferation effects of LP-7A on G.C cells were examined via CCK-8, colony formation, and morphology assay. Apoptosis of LP-7A treated G.C cells was evaluated via Hoechst Stain, western blot and flow cytometry. Autophagy was assessed via acridine orange staining and western blot. The cell cycle was assessed via flow cytometry assay and western blot. Pathway was studied via western blot and STRING database. Anti-migratory effects of LP-7A treated G.C cells were analyzed via wound healing, western blot, and migration and invasion assay. LP-7A effectively inhibited the growth of G.C cells by inhibiting the PI3K/Akt/mTOR pathway. G.C cells treated with LP-7A arrested the cell cycle at the G1 phase, contributing to the inhibition of migration and invasion. Furthermore, LP-7A induced apoptosis and autophagy in gastric cancer cells. These results indicated that LP-7A is a promising anti-cancer agent. It affected the proliferation and growth of G.C cells (SGC-7901 and BGC-823) by inducing apoptosis, autophagy, and inhibiting cell cycle at the G1 phase in G.C cells.

A comprehensive survey of AI-enabled phishing attacks detection techniques.

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client's sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images.

BACKGROUND AND OBJECTIVE: In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images. METHODS: In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal). RESULTS: The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3-9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively. CONCLUSIONS: The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals.

Survey on Revocation in Ciphertext-Policy Attribute-Based Encryption.

Recently, using advanced cryptographic techniques to process, store, and share datasecurely in an untrusted cloud environment has drawn widespread attention from academicresearchers. In particular, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising,advanced type of encryption technique that resolves an open challenge to regulate fine-grainedaccess control of sensitive data according to attributes, particularly for Internet of Things (IoT)applications. However, although this technique provides several critical functions such as dataconfidentiality and expressiveness, it faces some hurdles including revocation issues and lack ofmanaging a wide range of attributes. These two issues have been highlighted by many existingstudies due to their complexity which is hard to address without high computational cost affectingthe resource-limited IoT devices. In this paper, unlike other survey papers, existing single andmultiauthority CP-ABE schemes are reviewed with the main focus on their ability to address therevocation issues, the techniques used to manage the revocation, and comparisons among themaccording to a number of secure cloud storage criteria. Therefore, this is the first review paperanalysing the major issues of CP-ABE in the IoT paradigm and explaining the existing approachesto addressing these issues.