Current Topics in Medicinal Chemistry - Volume 25, Issue 6, 2025

This systematic review was conducted to evaluate the applicability of the envelope (E) protein in the diagnosis of arboviruses.

This review was performed in accordance with the PRISMA statement. Five databases were explored (PubMed, Web of Science, Scopus, EMBASE, and IEDB). The inclusion and exclusion criteria were applied to study eligibility. After data extraction, the risk of bias and evidence certainty were evaluated according to QUADAS and GRADE assessments, respectively.

A total of 11 studies were included in the review. ELISA was the most frequently utilized technique, with two studies employing it for antigen detection and nine for antibodies. The E protein was used as a whole protein, heterologous protein, and peptides. The diagnostic metrics were enhanced by optimizations on techniques, such as antibody capture, competitors, and nanosensors. Monoclonal antibodies showed improved specificity, including in co-infected samples. Seven studies demonstrated a minimal risk of bias, and the evidence certainty was considered moderate for dengue diagnosis.

The E protein was successfully employed in different immunological assays with large-scale strategies, enhancing the applicability potential for differential arboviruses’ diagnosis. Furthermore, both the antigen design and the implementation of innovative methodologies will have a substantial impact on the quality of the new tests. The PROSPERO protocol related to this work: CRD42021265243.

DNA repeats and motifs are specific nucleotide patterns/DNA sequences frequently present in the genomes of prokaryotes and eukaryotes. Computational identification of these discrete patterns is of considerable importance since they are associated with gene regulation, genomic instability, and genetic diversity and result in a variety of diseases/disorders.

In this article, the myriad of computational tools/algorithms and databases (~200 distinct resources) implicated in the detection of DNA repeats and motifs have been enlisted. This article will not only provide guidance to the users regarding the accuracy, reliability, and popularity (reflected by the citation index) of currently available tools but also enable them to select the best tool(s) to carry out a desired task.

The structured literature review, with its dependable and reproducible research process, allowed us to acquire 200 peer-reviewed publications from indexing databases, such as Scopus, ScienceDirect, Web of Science (WoS), PubMed, and EMBASE, by utilizing PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations. Numerous keyword combinations regarding DNA repeats and motifs were used to create the query syntax.

Initially, 3,233 research publications were retrieved, and 200 of them that satisfied the eligibility criteria for the detection and identification of DNA repeats and motifs by computational tools were chosen. A total of 200 research publications were recovered, of which 99 dealt with repeat prediction tools, 12 with repetitive sequence databases, 19 with specialized regulatory element databases, and 69 with motif prediction tools.

This article lists numerous databases and computational tools/algorithms (~ 200 different resources) that are involved in the identification of DNA repeats and motifs. It will help users choose the appropriate tool(s) for carrying out a particular task in addition to offering guidance on the reliability, dependability, and popularity (as indicated by the citation index) of currently available tools.

Single nucleotide polymorphisms (SNPs) are pivotal in clinical genetics, serving to link genotypes with disease susceptibility and response to environmental factors, including pharmacogenetics. They also play a crucial role in population genetics for mapping the human genome and localizing genes. Despite their utility, challenges arise when molecular genetic studies yield insufficient or uninformative data, particularly for socially significant diseases. This study aims to address these gaps by proposing a method to predict allelic variants of SNPs.

Using quantitative PCR and analyzing body composition data from 150 patients with their voluntary informed consent, we employed IBM SPSS Statistics 29.0 for data analysis. Our prototype formula, exemplified by allelic variant ADRB2 (rs1042713) = 0.257 + 0.639 * allelic variant ADRB2 (rs1042714) - 0.314 * allelic variant ADRB3 (rs4994) + 0.191 * allelic variant PPARA (rs4253778) - 0.218 * allelic variant PPARD (rs2016520) + 0.027 * body weight + 0.00001 * body weight², demonstrates the feasibility of predicting SNP allelic variants.

This method holds promise for diverse diseases, including those of significant social impact, due to its potential to streamline and economize molecular genetic research. Its ability to stratify disease risk in the absence of complete SNP data makes it particularly compelling for clinical and laboratory geneticists.

However, its translation into clinical practice necessitates the establishment of a comprehensive SNP database, especially for frequently analyzed SNPs within the implementing institution.