Improvement of SARS-COV-2 screening using pooled sampling testing in limited RT-qPCR resources.

BACKGROUND: COVID-19 is a worldwide pandemic representing the most challenging global health crisis currently. Screening tests availability are a problematic task due to resource-limited abilities of some countries using RT-qPCR technique for SARS-COV-2 detection. OBJECTIVE: To cope with these health emergencies, in particular with this COVID-19 pandemic, states with low molecular diagnostic resources must optimize their capacity in molecular tests. We aimed to design a simple and effective strategy to improve inputs in the RT-qPCR tests as we attempted to check the financial advisability of using such an approach by calculating reduction rate of the test unit cost. METHODS: The used RNA was taken from suspected Covid-19 positive people. Nasopharyngeal swabs were collected at Pasteur Institute Diagnostic Center, Constantine, Algeria, 2020. We have optimized a screening strategy by grouping 16 individuals per pool, without reducing the sensitivity of RT-qPCR. RESULTS: A 1/16 dilution of a positive sample was a practical limit that does not require the use of robotic systems or mathematical modeling to construct the pools. The financial analysis of our strategy has shown that the costs can be reduced to 90 %. The pooled testing strategy that was proven in this study could be recommended to help COVID-19 containment in countries with low potential screening infrastructures using RT-qPCR technique by reducing the number of tests required to identify all positive subjects.

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analytical methods, especially deep learning models. Recently, there is an increasing interest in deep learning utilization in computer-aided drug discovery due to its exceptional successful application in many fields. The present work proposed a natural language processing approach, based on embedding deep neural networks. Our method aims to transform the Simplified Molecular Input Line Entry System format into word embedding vectors to represent the semantics of compounds. These vectors are fed into supervised machine learning algorithms such as convolutional long short-term memory neural network, support vector machine, and random forest to build up quantitative structure-activity relationship models on toxicity data sets. The obtained results on toxicity data to the ciliate Tetrahymena pyriformis (IGC50 ), and acute toxicity rat data expressed as median lethal dose of treated rats (LD50 ) show that our approach can eventually be used to predict the activities of chemical compounds efficiently. All material used in this study is available online through the GitHub portal (https://github.com/BoukeliaAbdelbasset/NLPDeepQSAR.git).

Association of TERT, OGG1, and CHRNA5 Polymorphisms and the Predisposition to Lung Cancer in Eastern Algeria.

Lung cancer remains the most common cancer in the world. The genetic polymorphisms (rs2853669 in TERT, rs1052133 in OGG1, and rs16969968 in CHRNA5 genes) were shown to be strongly associated with the risk of lung cancer. Our study's aim is to elucidate whether these polymorphisms predispose Eastern Algerian population to non-small-cell lung cancer (NSCLC). To date, no study has considered this association in the Algerian population. This study included 211 healthy individuals and 144 NSCLC cases. Genotyping was performed using TaqMan probes and Sanger sequencing, and the data were analyzed using multivariate logistic regression adjusted for covariates. The minor allele frequencies (MAFs) of TERT rs2853669, CHRNA5 rs16969968, and OGG1 rs1052133 polymorphisms in controls were C: 20%, A: 31%, and G: 29%, respectively. Of the three polymorphisms, none shows a significant association, but stratified analysis rs16969968 showed that persons carrying the AA genotype are significantly associated with adenocarcinoma risk (pAdj = 0.03, ORAdj = 2.55). Smokers with an AA allele have a larger risk of lung cancer than smokers with GG or GA genotype (pAdj = 0.03, ORAdj = 3.91), which is not the case of nonsmokers. Our study suggests that CHRNA5 rs16969968 polymorphism is associated with a significant increase of lung adenocarcinoma risk and with a nicotinic addiction.

Optimization of the chromone scaffold through QSAR and docking studies: Identification of potent inhibitors of ABCG2.

The membrane transporter BCRP/ABCG2 has emerged as a privileged biological target for the development of small compounds capable of abolishing multidrug resistance. In this context, the chromone skeleton was found as an excellent scaffold for the design of ABCG2 inhibitors. With the aims of optimizing and developing more potent modulators of the transporter, we herewith propose a multidisciplinary medicinal chemistry approach performed on this promising scaffold. A quantitative structure-activity relationship (QSAR) study on a series of chromone derivatives was first carried out, giving a robust model that was next applied to the design of 13 novel compounds derived from this nucleus. Two of the most active according to the model's prediction, namely compounds 22 (5-((3,5-dibromobenzyl)oxy)-N-(2-(5-methoxy-1H-indol-3-yl)ethyl)-4-oxo-4H-chromene-2-carboxamide) and 31 (5-((2,4-dibromobenzyl)oxy)-N-(2-(5-methoxy-1H-indol-3-yl)ethyl)-4-oxo-4H-chromene-2-carboxamide), were synthesized and had their biological potency evaluated by experimental assays, confirming their high inhibitory activity against ABCG2 (experimental EC50 below 0.10 µM). A supplementary docking study was then conducted on the newly designed derivatives, proposing possible binding modes of these novel molecules in the putative ligand-binding site of the transporter and explaining why the two aforementioned compounds exerted the best activity according to biological data. Results from this study are recommended as references for further research in hopes of discovering new potent inhibitors of ABCG2.