Current landscape of long COVID clinical trials.

Long COVID was reported as a multi-systemic condition after the infection of SARS-CoV-2, and more than 65 million people are suffering from this disease. It has been noted that around 10% of severe SARS-CoV-2 infected individuals are suffering from the enduring effects of long COVID. The symptoms of long COVID have also been noted in several mild or asymptomatic SARS-CoV-2 infected individuals. While limited reports on clinical trials investigating new therapeutics for long COVID exist, there is an abundance of scattered information available regarding these trials. This review explores the extensive literature search, and complete clinical trial database search to map the current status of long COVID clinical trials worldwide. The study listed about 110 long COVID clinical trials. In addition to conducting extensive long COVID clinical trials, we have comprehensively presented an overview of the condition, its symptoms, notable manifestations, associated clinical trials, the unique challenges it poses, and our recommendations for addressing long COVID.

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.

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.

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.

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.

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.

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.

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.

India's Quantum Move: From Budget Allocation, Action and Future Challenges.

Major countries like the USA, European Union, UK, Japan, Canada, Australia, Singapore, and China have taken significant initiatives to develop quantum computation infrastructure. India has also taken several steps to join the quantum computation family. The Indian government has taken several initiatives to build the nation's infrastructure on quantum computation and participate in the global quantum landscape. The Indian government has created a roadmap in this direction. The significant steps are: firstly, noteworthy budget allocation (1.12 billion USD in 2020 and 734 million USD for the National Quantum Mission in 2023); secondly, 21 quantum hubs are being developed throughout the country; thirdly, 4 quantum research parks have been created and finally, Department of Science and Technology (DST) has initiated QuEST (Quantum Enabled Science and Technology) programme during 2017-18. The article also discusses other effective strategies and moves by the Indian government, like different ambitious national missions on quantum science and technology to create the country's ecosystem. In that direction, the article addresses the opportunities and challenges of quantum science and technology for India. However, the Indian government should encourage quantum computation research more for the country's development. Finally, the information provided here depicts an overall view of India's quantum computation landscape.

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.

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.

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.

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.

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.