Predominance of CCR5 tropism in non-b HIV-1 subtypes circulating in Kisii County, Kenya

Introduction: The chemokine receptors CCR5 and CXCR4 are considered as the main receptors during HIV infection, replication, transmission and subsequent AIDS progression. CCR5 antagonists are drugs designed to inhibit viral entry by binding to these chemokine receptors. However, characterisation of HIV-1 co-receptor usage before rolling out of CCR5/CXCR4 antagonists has not yet been done in the country.Objective: To determine the HIV-1 co receptor usage among HIV-1 infected individuals and predict possible use CCR5 antagonistic drugs.Design: A cross sectional study Setting: Comprehensive HIV care clinics of Kisii Teaching & Referral Hospital, Kenya.Methods: A total of seventy-two (72) blood samples were obtained from both drug naïve (32) and experienced (40) study participants. Viral DNA was extracted using QIAamp MinElute Virus kit and partial HIV-1 V3 region was amplified and directly sequenced. Coreceptor usage predicted using insilico Geno2pheno (coreceptor) with a false positive rate of 15%.Results: Sixty-one individuals (77.8%) were infected with HIV-1 subtype A1, twelve (18.1%) HIV-1 subtype D and four (4.1%) were HIV-1 subtype C. CCR5-using variants were found in 52 (72.2%) while 20(27.8%) participants were infected with CXCR4–using variants. There was no significant difference in co-receptor usage a cross gender, HIV subtypes, disease staging or impact of treatment or CD 4 counts that was observed.Conclusions and recommendation: The detected high level of circulating R5 strains suggests the likelihood of a successful implementation and use of CCR5 antagonists in Kenya where HIV-1 A1 is the most predominant

Prediction of aromatase inhibitory activity using the efficient linear method (ELM)

Aromatase inhibition is an effective treatment strategy for breast cancer. Currently, several in silico methods have been developed for the prediction of aromatase inhibitors (AIs) using artificial neural network (ANN) or support vector machine (SVM). In spite of this, there are ample opportunities for further improvements by developing a simple and interpretable quantitative structure-activity relationship (QSAR) method. Herein, an efficient linear method (ELM) is proposed for constructing a highly predictive QSAR model containing a spontaneous feature importance estimator. Briefly, ELM is a linear-based model with optimal parameters derived from genetic algorithm. Results showed that the simple ELM method displayed robust performance with 10-fold cross-validation MCC values of 0.64 and 0.56 for steroidal and non-steroidal AIs, respectively. Comparative analyses with other machine learning methods (i.e. ANN, SVM and decision tree) were also performed. A thorough analysis of informative molecular descriptors for both steroidal and non-steroidal AIs provided insights into the mechanism of action of compounds. Our findings suggest that the shape and polarizability of compounds may govern the inhibitory activity of both steroidal and non-steroidal types whereas the terminal primary C(sp3) functional group and electronegativity may be required for non-steroidal AIs. The R code of the ELM method is available at http://dx.doi.org/10.6084/m9.figshare.1274030

Network intrusions classification using data mining approaches

Intrusion Detection System has an important task in detecting threats or attacks in the computer networks. Intrusion Detection System (IDS) is a network protection device used to identify and check data packets in network traffic. Snort is free software used to detect attacks and protect computer networks. Snort can only detect misuse attacks, whereas to detect anomaly attacks using Bayes Network, Naive Bayes, Random Tree, LMT and J-48 Classification Method. In this paper, the experimental study uses the KDDCUP 99 dataset and the dataset taken from Campus Network. The main objective of this research is to detect deceptive packets that pass computer network traffic. The steps taken in this study are data preparation, data cleaning, dataset classification, feature extraction, rules snort for detecting, and detecting packet as an attack or normal. The result of the proposed system is an accurate detection rate

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review