Diltiazem for COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has infected over 100 million people globally due to its high infectivity. After decades of efforts on the studies of nanomaterials, researchers have applied nanomaterials‐based strategies to combat the pandemic of the coronavirus disease 2019 (COVID‐19). First, nanomaterials facilitate the development of easy, fast, and low‐cost diagnostic assays to detect SARS‐CoV‐2 and related biomarkers. Second, nanomaterials enable the efficient delivery of viral antigens to antigen‐presenting cells or serve as adjuvants in the host, leading to vaccine development at an unprecedented pace. Lastly, nanomaterials‐based treatments may inhibit SARS‐CoV‐2 replication and reduce inflammation. Overall, nanomaterials have played important roles in controlling this COVID‐19 pandemic. Here, we provide a brief overview of the representative examples of nanomaterials‐based diagnostics, vaccines, and therapeutics in the fight against COVID‐19. The use and effectiveness of state responses is constantly evolving, particularly in relation to physical distancing policies during the COVID-19 pandemic. Testing is a measure of response performance and will be a central point during the infectious disease pandemic as all countries face similar situations. COVID-19 is a unique opportunity to assess and measure the success of a country, control its spread, and combat the social and economic impacts of interventions. By fighting the factors associated with testing and reporting, understanding the limits on COVID-19 case numbers will strengthen the country’s response to these and future pandemics, and improve the reliability of the knowledge gained by cross-country comparisons. With amazing and amazing COVID-19, a lack of testing may not trust the efforts of the entire community, rather than the entire population. The emergence of novel strains of SARS-CoV-2 highlights the pressing need to investigate various strategies for enhancing pandemic resilience. Even though tried-and-true methods like social separation, masks, and vaccinations have proven effective, issues with immunizations make finding a global answer more complex. This paper underscores the pivotal connection between immunological resilience and vitamins, shedding light on the compromised immune response resulting from undernourishment. Vitamins become essential for protecting the body from viral invasion, particularly from SARS-CoV-2. Crucial roles in cellular activities are played by vitamin A, which is necessary for vision, and the B-vitamin complex, which supports energy synthesis and nerve function. In the context of viral infections, the significance of vitamin D, crucial for both immune system function and bone health, along with vitamin C and its ability to combat free radicals, becomes paramount.This research aims to to elucidate the specific effects and mechanisms by which essential vitamins (A, B, C, D, and E) contribute to the mitigation of..

Abstract Background Viruses such as Ebola, Marburg, influenza, mpox, MERS-CoV, SARS-CoV, and SARS-CoV-2 pose a significant risk for future pandemics. Developing novel antiviral medicines can be time-consuming and resource intensive. Repurposing existing medicines with antiviral activity offers a faster, cost-effective strategy to expand treatment options during public health emergencies. This scan aimed to identify and synthesise recent evidence on repurposed antiviral medicines under investigation for these viruses. Method A horizon scanning approach was employed, starting with a targeted search in Embase, followed by a systematic search of ClinicalTrials.gov to capture the developmental stages of the technologies. Eligible technologies included UK- or EU-licensed medicines repurposed as antiviral therapies for the viruses of interest. Vaccines, unlicensed medicines, and already approved treatments for the targeted viruses were excluded. Results A total of 196 repurposed technologies targeting the viruses were identified from published literature, and the expanded search on the clinical trials registry yielded 58 technologies in active clinical development. Interventional clinical trial activity was limited to influenza and COVID-19, with 29 technologies for COVID-19 and two for influenza advancing to phase III evaluation. For other viruses, proposed antiviral candidates were identified in the literature but had not progressed into clinical development. Commonly investigated pharmacological classes included direct-acting antivirals, tyrosine kinase inhibitors, immunomodulators, and anti-inflammatory agents. Conclusion Repurposing antiviral medicines represents a pragmatic strategy for rapid therapeutic deployment against emerging viral threats. Collaboration among researchers, policymakers, research funders, and regulatory bodies will be essential to improve pandemic preparedness and support repurposing efforts in emergency situations.

Abstract Motivation The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. Results In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. Availability and implementation https://github.com/Hhhzj-7/DeepCoVDR.