ABSTRACT
Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63-65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.
ABSTRACT
Comprehensive and systematic examination of Dengue virus (DENV) evolution is essential in the context of Pakistan as the virus presents a significant public health challenge with the ability to adapt and evolve. To shed light on intricate evolutionary patterns of all four DENV serotypes, we analyzed complete genome sequences (n=43) and envelope (E) gene sequences (n=44) of all four DENV serotypes collected in Pakistan from 1994 to 2023 providing a holistic view of their genetic evolution. Our findings revealed that all four serotypes of DENV co-circulate in Pakistan with a close evolutionary relationship between DENV-1 and DENV-3. Genetically distinct serotypes DENV-2 and DENV-4 indicate that DENV-4 stands out as the most genetically different, while DENV-2 exhibits greater complexity due to the presence of multiple genotypes and the possibility of temporal fluctuations in genotype prevalence. Selective pressure analysis in Envelope (E) gene revealed heterogeneity among sequences (n=44) highlighting 46 codons in the genome experiencing selective pressure, characterized by a bias towards balancing selection indicating genetic stability of the virus. Furthermore, our study suggested an intriguing evolutionary shift of DENV-4 towards the DENV-2 clade, potentially influenced by antibodies with cross-reactivity to multiple serotypes providing a critical insight into the complex factors shaping DENV evolution and contributing to the emergence of new serotype.
ABSTRACT
Conventional diagnostic techniques are based on the utilization of analyte sampling, sensing and signaling on separate platforms for detection purposes, which must be integrated to a single step procedure in point of care (POC) testing devices. Due to the expeditious nature of microfluidic platforms, the trend has been shifted toward the implementation of these systems for the detection of analytes in biochemical, clinical and food technology. Microfluidic systems molded with substances such as polymers or glass offer the specific and sensitive detection of infectious and noninfectious diseases by providing innumerable benefits, including less cost, good biological affinity, strong capillary action and simple process of fabrication. In the case of nanosensors for nucleic acid detection, some challenges need to be addressed, such as cellular lysis, isolation and amplification of nucleic acid before its detection. To avoid the utilization of laborious steps for executing these processes, advances have been deployed in this perspective for on-chip sample preparation, amplification and detection by the introduction of an emerging field of modular microfluidics that has multiple advantages over integrated microfluidics. This review emphasizes the significance of microfluidic technology for the nucleic acid detection of infectious and non-infectious diseases. The implementation of isothermal amplification in conjunction with the lateral flow assay greatly increases the binding efficiency of nanoparticles and biomolecules and improves the limit of detection and sensitivity. Most importantly, the deployment of paper-based material made of cellulose reduces the overall cost. Microfluidic technology in nucleic acid testing has been discussed by explicating its applications in different fields. Next-generation diagnostic methods can be improved by using CRISPR/Cas technology in microfluidic systems. This review concludes with the comparison and future prospects of various microfluidic systems, detection methods and plasma separation techniques used in microfluidic devices.
