ArticleFuture virology2014
Novel drugs targeting Toll-like receptors for antiviral therapy.
Article in Future virology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed, 98 citations in OpenAlex.
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- An Immunoinformatic Approach for Identifying and Designing Conserved Multi-Epitope Vaccines for Coronaviruses.Biomedicines · 2024Article
- Evaluation of intranasal TLR2/6 agonist INNA-051: safety, tolerability and proof of pharmacology.ERJ open research · 2024Article
- Updates in Dermatomyositis: Newer Treatment Options and Outcome Measures From Dermatologic Perspectives.Annals of dermatology · 2024Review
- Divergent TLR2 and TLR4 Activation by Fungal Spores and Species Diversity in Dust from Waste Sorting Plants.Applied and environmental microbiology · 2023Article
- Evaluation of Immune Modulation by β-1,3; 1,6 D-Glucan Derived fromFoods (Basel, Switzerland) · 2023Article
- Targeting toll-like receptor 7/8 for immunotherapy: recent advances and prospectives.Biomarker research · 2022Review
- Nanocarriers for effective delivery: modulation of innate immunity for the management of infections and the associated complications.Journal of nanobiotechnology · 2022Review
- Toll-like Receptor Response to Hepatitis C Virus Infection: A Recent Overview.International journal of molecular sciences · 2022Review
- Design of a multi-epitope vaccine against SARS-CoV-2: immunoinformatic and computational methods.RSC advances · 2022Article
- Nafamostat reduces systemic inflammation in TLR7-mediated virus-like illness.Journal of neuroinflammation · 2022Article
- Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8.PloS one · 2022Article
- SARS-CoV-2 subunit vaccine adjuvants and their signaling pathways.Expert review of vaccines · 2022Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Toll-like receptors (TLRs) are sentinel receptors of the host innate immune system that recognize conserved 'pathogen-associated molecular patterns' of invading microbes, including viruses. The activation of TLRs establishes antiviral innate immune responses and coordinates the development of long-lasting adaptive immunity in order to control viral pathogenesis. However, microbe-induced damage to host tissues may release 'danger-associated molecular patterns' that also activate TLRs, leading to an overexuberant inflammatory response and, ultimately, to tissue damage. Thus, TLRs have proven to be promising targets as therapeutics for the treatment of viral infections that result in inflammatory damage or as adjuvants in order to enhance the efficacy of vaccines. Here, we explore recent advances in TLR biology with a focus on novel drugs that target TLRs (agonists and antagonists) for antiviral therapy.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.