Blueprint of differentially expressed genes reveals the dynamic gene expression landscape and the gender biases in long COVID

BACKGROUND

Long COVID has appeared as a significant global health issue and is an extra burden to the healthcare system. It affects a considerable number of people throughout the globe. However, substantial research gaps have been noted in understanding the mechanism and genomic landscape during the long COVID infection. A study has aimed to identify the differentially expressed genes (DEGs) in long COVID patients to fill the gap.

METHODS

We used the RNA-seq GEO dataset acquired through the GPL20301 Illumina HiSeq 4000 platform. The dataset contains 36 human samples derived from PBMC (Peripheral blood mononuclear cells). Thirty-six human samples contain 13 non-long COVID individuals' samples and 23 long COVID individuals' samples, considered the first direction analysis. Here, we performed two-direction analyses. In the second direction analysis, we divided the dataset gender-wise into four groups: the non-long COVID male group, the long COVID male group, the non-long COVID female group, and the long COVID female group.

RESULTS

In the first analysis, we found no gene expression. In the second analysis, we identified 250 DEGs. During the DEG profile analysis of the non-long COVID male group and the long COVID male group, we found three upregulated genes: IGHG2, IGHG4, and MIR8071-2. Similarly, the analysis of the non-long COVID female group and the long COVID female group reveals eight top-ranking genes. It also indicates the gender biases of differentially expressed genes among long COVID individuals. We found several DEGs involved in PPI and co-expression network formation. Similarly, cluster enrichment and gene list enrichment analysis were performed, suggesting several genes are involved in different biological pathways or processes.

CONCLUSIONS

This study will help better understand the gene expression landscape in long COVID. However, it might help the discovery and development of therapeutics for long COVID.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.

METHODS

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.

FINDINGS

The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.

INTERPRETATION

Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.

FUNDING

Bill & Melinda Gates Foundation.

Structural Landscape of nsp Coding Genomic Regions of SARS-CoV-2-ssRNA Genome: A Structural Genomics Approach Toward Identification of Druggable Genome, Ligand-Binding Pockets, and Structure-Based Druggability

SARS-CoV-2 has a single-stranded RNA genome (+ssRNA), and synthesizes structural and non-structural proteins (nsps). All 16 nsp are synthesized from the ORF1a, and ORF1b regions associated with different life cycle preprocesses, including replication. The regions of ORF1a synthesizes nsp1 to 11, and ORF1b synthesizes nsp12 to 16. In this paper, we have predicted the secondary structure conformations, entropy & mountain plots, RNA secondary structure in a linear fashion, and 3D structure of nsp coding genes of the SARS-CoV-2 genome. We have also analyzed the A, T, G, C, A+T, and G+C contents, GC-profiling of these genes, showing the range of the GC content from 34.23 to 48.52%. We have observed that the GC-profile value of the nsp coding genomic regions was less (about 0.375) compared to the whole genome (about 0.38). Additionally, druggable pockets were identified from the secondary structure-guided 3D structural conformations. For secondary structure generation of all the nsp coding genes (nsp 1-16), we used a recent algorithm-based tool (deep learning-based) along with the conventional algorithms (centroid and MFE-based) to develop secondary structural conformations, and we found stem-loop, multi-branch loop, pseudoknot, and the bulge structural components, etc. The 3D model shows bound and unbound forms, branched structures, duplex structures, three-way junctions, four-way junctions, etc. Finally, we identified binding pockets of nsp coding genes which will help as a fundamental resource for future researchers to develop RNA-targeted therapeutics using the druggable genome.

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period.

METHODS

22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.

FINDINGS

Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.

INTERPRETATION

Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.

FUNDING

Bill & Melinda Gates Foundation.

Regulatory role of miRNAs in the human immune and inflammatory response during the infection of SARS-CoV-2 and other respiratory viruses: A comprehensive review

miRNAs are single-stranded ncRNAs that act as regulators of different human body processes. Several miRNAs have been noted to control the human immune and inflammatory response during severe acute respiratory infection syndrome (SARS-CoV-2) infection. Similarly, many miRNAs were upregulated and downregulated during different respiratory virus infections. Here, an attempt has been made to capture the regulatory role of miRNAs in the human immune and inflammatory response during the infection of SARS-CoV-2 and other respiratory viruses. Firstly, the role of miRNAs has been depicted in the human immune and inflammatory response during the infection of SARS-CoV-2. In this direction, several significant points have been discussed about SARS-CoV-2 infection, such as the role of miRNAs in human innate immune response; miRNAs and its regulation of granulocytes; the role of miRNAs in macrophage activation and polarisation; miRNAs and neutrophil extracellular trap formation; miRNA-related inflammatory response; and miRNAs association in adaptive immunity. Secondly, the miRNAs landscape has been depicted during human respiratory virus infections such as human coronavirus, respiratory syncytial virus, influenza virus, rhinovirus, and human metapneumovirus. The article will provide more understanding of the miRNA-controlled mechanism of the immune and inflammatory response during COVID-19, which will help more therapeutics discoveries to fight against the future pandemic.

SARS-CoV-2 Omicron Spike shows strong binding affinity and favourable interaction landscape with the TLR4/MD2 compared to other variants

Emergences of SARS-CoV-2 variants have made the pandemic more critical. Toll-like receptor 4 (TLR4) recognizes the molecular patterns of pathogens and activates the production of proinflammatory cytokines to restrain the infection. We have identified a molecular basis of interaction between the Spike and TLR4 of SARS-CoV-2 and its present and past VOCs (variant- of concern) through in silico analysis. The interaction of wild type Spike with TLR4 showed 15 number hydrogen bonds formation. Similarly, the Alpha variants' Spike with the TLR4 has illustrated that 14 hydrogen bonds participated in the interaction. However, the Delta Spike and TLR4 interaction interface showed that 17 hydrogen bonds were formed during the interaction. Furthermore, Omicron S-glycoprotein and TLR4 interaction interface was depicted (interaction score: -170.3), and 16 hydrogen bonds were found to have been formed in the interaction. Omicron S-glycoprotein shows stronger binding affinity with the TLR4 than wild type, Alpha, and Delta variants. Similarly, the Alpha Spike shows higher binding affinity with TLR4 than the wild type and Delta variant. Now, it is an open question of the molecular basis of the interaction of Spike and TLR4 and the activated downstream signaling events of TLR4 for SARS-CoV-2 and its variants.

A Domain-Specific Next-Generation Large Language Model (LLM) or ChatGPT is Required for Biomedical Engineering and Research

Large language models or ChatGPT have recently gained extensive media coverage. At the same time, the use of ChatGPT has increased deistically. Biomedical researchers, engineers, and clinicians have shown significant interest and started using it due to its diverse applications, especially in the biomedical field. However, it has been found that ChatGPT sometimes provided incorrect or partly correct information. It is unable to give the most recent information. Therefore, we urgently advocate a domain-specific next-generation, ChatBot for biomedical engineering and research, providing error-free, more accurate, and updated information. The domain-specific ChatBot can perform diversified functions in biomedical engineering, such as performing innovation in biomedical engineering, designing a medical device, etc. The domain-specific artificial intelligence enabled device will revolutionize biomedical engineering and research if a biomedical domain-specific ChatBot is produced.

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.

Need an AI-Enabled, Next-Generation, Advanced ChatGPT or Large Language Models (LLMs) for Error-Free and Accurate Medical Information

Recently, the interest in AI-guided ChatGPT has increased day-to-day, and different applications have been explored, including the medical field. The publication number is also increasing. At the same time, people are trying to get medical information from this Chartbot. However, researchers found that ChatGPT also provides partly correct or false information. Therefore, in this article, we urge the researchers to develop an AI-enabled, next-generation, advanced ChatGPT or large language models (LLMs) so that people can get accurate and error-free medical information.

ChatGPT and large language models in orthopedics: from education and surgery to research

ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.

Exploring the structural and molecular interaction landscape of nirmatrelvir and Mpro complex: The study might assist in designing more potent antivirals targeting SARS-CoV-2 and other viruses

BACKGROUND

Several therapeutics have been developed and approved against SARS-CoV-2 occasionally; nirmatrelvir is one of them. The drug target of nirmatrelvir is Mpro, and therefore, it is necessary to comprehend the structural and molecular interaction of the Mpro-nirmatrelvir complex.

METHODS

Integrative bioinformatics, system biology, and statistical models were used to analyze the macromolecular complex.

RESULTS

Using two macromolecular complexes, the study illustrated the interactive residues, H-bonds, and interactive interfaces. It informed of six and nine H-bond formations for the first and second complex, respectively. The maximum bond length was observed as 3.33 Å. The ligand binding pocket's surface area and volume were noted as 303.485 Å2 and 295.456 Å3 for the first complex and 308.397 Å2 and 304.865 Å3 for the second complex. The structural proteome dynamics were evaluated by analyzing the complex's NMA mobility, eigenvalues, deformability, and B-factor. Conversely, a model was created to assess the therapeutic status of nirmatrelvir.

CONCLUSIONS

Our study reveals the structural and molecular interaction landscape of Mpro-nirmatrelvir complex. The study will guide researchers in designing more broad-spectrum antiviral molecules mimicking nirmatrelvir, which assist in fighting against SARS-CoV-2 and other infectious viruses. It will also help to prepare for future epidemics or pandemics.

A next-generation dynamic programming language Julia: Its features and applications in biological science

BACKGROUND

The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences.

THE AIM OF REVIEW

This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.

Current Status of Microneedle Array Technology for Therapeutic Delivery: From Bench to Clinic

In recent years, microneedle (MN) patches have emerged as an alternative technology for transdermal delivery of various drugs, therapeutics proteins, and vaccines. Therefore, there is an urgent need to understand the status of MN-based therapeutics. The article aims to illustrate the current status of microneedle array technology for therapeutic delivery through a comprehensive review. However, the PubMed search was performed to understand the MN's therapeutics delivery status. At the same time, the search shows the number no of publications on MN is increasing (63). The search was performed with the keywords "Coated microneedle," "Hollow microneedle," "Dissolvable microneedle," and "Hydrogel microneedle," which also shows increasing trend. Similarly, the article highlighted the application of different microneedle arrays for treating different diseases. The article also illustrated the current status of different phases of MN-based therapeutics clinical trials. It discusses the delivery of different therapeutic molecules, such as drug molecule delivery, using microneedle array technology. The approach mainly discusses the delivery of different therapeutic molecules. The leading pharmaceutical companies that produce the microneedle array for therapeutic purposes have also been discussed. Finally, we discussed the limitations and future prospects of this technology.

Antibody evasion associated with the RBD significant mutations in several emerging SARS-CoV-2 variants and its subvariants

Since the origin of the wild strain of SARS-CoV-2, several variants have emerged, which were designated as VOC, VOI, and VUM from time to time. The Omicron variant is noted as the recent VOC. After the origin of the Omicron variant on November 2021, several subvariants of Omicron have originated subsequently, like BA.1/2, BA.2.75/2.75.2, BA.4/5, BF.7, BQ.1/1.1, XBB.1/1.5, etc. which are circulated throughout the globe. Scientists reported that antibody escape is a common phenomenon observed in all the previous VOCs, VOIs, including Omicron and its subvariants. The mutations in the NTD (N-terminal domain) and RBD (Receptor-binding domain) of the spike of these variants and subvariants are responsible for antibody escape. At the same time, it has been noted that spike RBD mutations have been increasing in the last few months. This review illustrates significant RBD mutations namely R346T, K417N/T, L452R, N460K E484A/K/Q, and N501Y found in the previous emerging SARS-CoV-2 variants, including Omicron and its subvariants in high frequency and their role in antibody evasion and immune evasion. The review also describes the different classes of nAb responsible for antibody escape in SARS-CoV-2 variants and the molecular perspective of the mutation in nAb escape. It will help the future researchers to develop efficient vaccines which can finally prevent the pandemic.

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

Entropy-Driven, Integrative Bioinformatics Approaches Reveal the Recent Transmission of the Monkeypox Virus from Nigeria to Multiple Non-African Countries

Monkeypox virus (mpox) has currently affected multiple countries around the globe. This study aims to analyze how the virus spread globally. The study uses entropy-driven bioinformatics in five directions to analyze the 60 full-length complete genomes of mpox. We analyzed the topological entropy distribution of the genomes, principal component analysis (PCA), the dissimilarity matrix, entropy-driven phylogenetics, and genome clustering. The topological entropy distribution showed genome positional entropy. We found five clusters of the mpox genomes through the two PCA, while the three PCA elucidated the clustering events in 3D space. The clustering of genomes was further confirmed through the dissimilarity matrix and phylogenetic analysis which showed the bigger size of Cluster 1 and size similarity between Clusters 2 and 4 as well as Clusters 3 and 5. It corroborated with the phylogenetics of the genomes, where Cluster 1 showed clear segregation from the other four clusters. Finally, the study concluded that the spreading of the mpox is likely to have originated from African countries to the rest of the non-African countries. Overall, the spreading and distribution of the mpox will shed light on its evolution and pathogenicity of the mpox and help to adopt preventive measures to stop the spreading of the virus.

Future Potential of Quantum Computing and Simulations in Biological Science

The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference, quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.

A novel mutation-proof, next-generation vaccine to fight against upcoming SARS-CoV-2 variants and subvariants, designed through AI enabled approaches and tools, along with the machine learning based immune simulation: A vaccine breakthrough

Emerging SARS-CoV-2 variants and subvariants are great concerns for their significant mutations, which are also responsible for vaccine escape. Therefore, the study was undertaken to develop a mutation-proof, next-generation vaccine to protect against all upcoming SARS-CoV-2 variants. We used advanced computational and bioinformatics approaches to develop a multi-epitopic vaccine, especially the AI model for mutation selection and machine learning (ML) strategies for immune simulation. AI-enabled and the top-ranked antigenic selection approaches were used to select nine mutations from 835 RBD mutations. We selected twelve common antigenic B cell and T cell epitopes (CTL and HTL) containing the nine RBD mutations and joined them with the adjuvants, PADRE sequence, and suitable linkers. The constructs' binding affinity was confirmed through docking with TLR4/MD2 complex and showed significant binding free energy (-96.67 kcal mol^-1) with positive binding affinity. Similarly, the calculated eigenvalue (2.428517e-05) from the NMA of the complex reveals proper molecular motion and superior residues' flexibility. Immune simulation shows that the candidate can induce a robust immune response. The designed mutation-proof, multi-epitopic vaccine could be a remarkable candidate for upcoming SARS-CoV-2 variants and subvariants. The study method might guide researchers in developing AI-ML and immunoinformatics-based vaccines for infectious disease.

Revealing the structural and molecular interaction landscape of the favipiravir-RTP and SARS-CoV-2 RdRp complex through integrative bioinformatics: Insights for developing potent drugs targeting SARS-CoV-2 and other viruses

BACKGROUND

The global research community has made considerable progress in therapeutic and vaccine research during the COVID-19 pandemic. Several therapeutics have been repurposed for the treatment of COVID-19. One such compound is, favipiravir, which was approved for the treatment of influenza viruses, including drug-resistant influenza. Despite the limited information on its molecular activity, clinical trials have attempted to determine the effectiveness of favipiravir in patients with mild to moderate COVID-19. Here, we report the structural and molecular interaction landscape of the macromolecular complex of favipiravir-RTP and SARS-CoV-2 RdRp with the RNA chain.

METHODS

Integrative bioinformatics was used to reveal the structural and molecular interaction landscapes of two macromolecular complexes retrieved from RCSB PDB.

RESULTS

We analyzed the interactive residues, H-bonds, and interaction interfaces to evaluate the structural and molecular interaction landscapes of the two macromolecular complexes. We found seven and six H-bonds in the first and second interaction landscapes, respectively. The maximum bond length is 3.79 Å. In the hydrophobic interactions, five residues (Asp618, Asp760, Thr687, Asp623, and Val557) were associated with the first complex and two residues (Lys73 and Tyr217) were associated with the second complex. The mobilities, collective motion, and B-factor of the two macromolecular complexes were analyzed. Finally, we developed different models, including trees, clusters, and heat maps of antiviral molecules, to evaluate the therapeutic status of favipiravir as an antiviral drug.

CONCLUSIONS

The results revealed the structural and molecular interaction landscape of the binding mode of favipiravir with the nsp7-nsp8-nsp12-RNA SARS-CoV-2 RdRp complex. Our findings can help future researchers in understanding the mechanism underlying viral action and guide the design of nucleotide analogs that mimic favipiravir and exhibit greater potency as antiviral drugs against SARS-CoV-2 and other infectious viruses. Thus, our work can help in preparing for future epidemics and pandemics.

A European perspective of phylogenomics, sublineages, geographical distribution, epidemiology, and mutational landscape of mpox virus: Emergence pattern may help to fight the next public health emergency in Europe

BACKGROUND

The 2022 outbreak of the mpox virus (previously monkeypox virus, MPXV) in non-epidemic regions has created a global issue. The emergence of MPXV was first reported in Europe, which was described as the MPXV epicenter, however, no reports are available to illustrate its outbreak patterns in Europe.

METHODS

The study used numerous in silico and statistical methods to analyze hMPXV1 in European countries. Here, we used different bioinformatics servers and software to evaluate the spread of hMPXV1 in European countries. For analysis, we use various advanced servers like Nextstrain, Taxonium, MpoxSpectrum, etc. Similarly, for the statistical model, we used PAST software.

RESULTS

The phylogenetic tree was depicted to illustrate the origin and evolution of hMPXV1 using vas number of genome sequences (n = 675). We found several sublineages in Europe, indicating microevolution. The scatter plot reveals the clustering patterns of the newly developed lineages in Europe. We developed statistical models for the monthly total relative frequency counts of these sublineages. The epidemiology of MPX in Europe was examined in an attempt to capture the epidemiological pattern, total cases, and deaths. Our Study noted the highest number of cases was in Spain (7500 cases) and the second-highest in France (4114 cases). The third highest number of cases was in the UK (3730 cases), which was very similar to Germany (3677 cases). Finally, we noted the mutational landscape throughout European genomes. Significant mutations were observed at the nucleotide and protein levels. We identified several unique homoplastic mutations in Europe.

CONCLUSION

This study reveals several essential aspects of the European outbreak. It might help to eradicate the virus in Europe, assist in strategy formation to fight against the virus, and support working against the next public health emergency in Europe.

Evaluation of differentially expressed genes during replication using gene expression landscape of monkeypox-infected MK2 cells: A bioinformatics and systems biology approach to understanding the genomic pattern of viral replication

PURPOSE

The current outbreak of monkeypox (MPX) has created colossal concerns. However, immense research gaps have been noted in our understanding of the replication process, machinery, and genomic landscape during host cell infection. To fill this gap, differentially expressed genes (DEGs) were comprehensively analyzed during viral replication in host (MK2) cells.

METHODS

We used a microarray GEO dataset which was divided into three groups: control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h. Using the dataset, DEG analysis, PPI network analysis, co-expression, and pathway analysis were conducted using bioinformatics, systems biology, and statistical approaches.

RESULTS

We identified 250 DEGs and 24 top-ranked genes. During the DEG analysis, we identified eight up-regulated genes (LOC695323, TMEM107, LOC695427, HIST1H2AD, LOC705469, PMAIP1, HIST1H2BJ, and HIST1H3D) and 16 down-regulated genes (HOXA9, BAMBI, LMO4, PAX6, AJUBA, CREBRF, CD24, JADE1, SLC7A11, EID2, SOX4, B4GALT5, PPARGC1A, BUB3, SOS2, and CDK19). We also developed PPI networks and performed co-expression analyses using the top-ranked genes. Furthermore, five genes were listed for co-expression pattern analysis.

CONCLUSIONS

This study will help in better understanding the replication process, machinery, and genomic landscape of the virus. This will further aid the discovery and development of therapeutics against viruses.

Therapeutic applications of nanobodies against SARS-CoV-2 and other viral infections: Current update

In the last two years, the world encountered the SARS-CoV-2 virus, which is still dominating the population due to the absence of a viable treatment. To eradicate the global pandemic, scientists, doctors, and researchers took an exceptionally significant initiative towards the development of effective therapeutics to save many lifes. This review discusses about the single-domain antibodies (sdAbs), also called nanobodies, their structure, and their types against the infections of dreadful SARS-CoV-2 virus. A precise description highlights the nanobodies and their therapeutic application against the other selected viruses. It aims to focus on the extraordinary features of these antibodies compared to the conventional therapeutics like mAbs, convalescent plasma therapy, and vaccines. The stable structure of these nanobodies along with the suitable mechanism of action also confers greater resistance to the evolving variants with numerous mutations. The nanobodies developed against SARS-CoV-2 and its mutant variants have shown the greater neutralization potential than the primitive ones. Engineering of these specialized antibodies by modern biotechnological approaches will surely be more beneficial in treating this COVID-19 pandemic along with certain other viral infections.