1,720,999 research outputs found
Overview of bioinformatic tools to study viral infections
No full textmicroRNAs play an important role in post-transcriptional gene regulation. Recently, viral microRNAs have been discovered in several viruses, including Hepatitis B virus. This brief work explores bioinformatics tools for viral/host miRNA research and provides insights into the roles of miRNAs in HBV infection, offering an overview of this field, in order to facilitate the selection of the most suitable bioinformatics tools according to individual needs and research goals
Hepatitis B Virus and microRNAs: A Bioinformatics Approach
Full text linkIn recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral–host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals
Spillover: Mechanisms, Genetic Barriers, and the Role of Reservoirs in Emerging Pathogens
Full text link: Viral spillover represents the transmission of pathogen viruses from one species to another that can give rise to an outbreak. It is a critical concept that has gained increasing attention, particularly after the SARS-CoV-2 pandemic. However, the term is often used inaccurately to describe events that do not meet the true definition of spillover. This review aims to clarify the proper use of the term and provides a detailed analysis of the mechanisms driving zoonotic spillover, with a focus on the genetic and environmental factors that enable viruses to adapt to new hosts. Key topics include viral genetic variability in reservoir species, biological barriers to cross-species transmission, and the factors that influence viral adaptation and spread in novel hosts. The review also examines the role of evolutionary processes such as mutation and epistasis, alongside ecological conditions that facilitate the emergence of new pathogens. Ultimately, it underscores the need for more accurate predictive models and improved surveillance to better anticipate and mitigate future spillover events
Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19
Full text linkThe availability of the epidemiological data strongly affects the reliability of several mathematical models in tracing and forecasting COVID-19 pandemic, hampering a fair assessment of their relative performance. The marked difference between the lethality of the virus when comparing the first and second waves is an evident sign of the poor reliability of the data, also related to the variability over time in the number of performed swabs. During the early epidemic stage, swabs were made only to patients with severe symptoms taken to hospital or intensive care unit. Thus, asymptomatic people, not seeking medical assistance, remained undetected. Conversely, during the second wave of infection, total infectives included also a percentage of detected asymptomatic infectives, being tested due to close contacts with swab positives and thus registered by the health system. Here, we compared the outcomes of two SIR-type models (the standard SIR model and the A-SIR model that explicitly considers asymptomatic infectives) in reproducing the COVID-19 epidemic dynamic in Italy, Spain, Germany, and France during the first two infection waves, simulated separately. We found that the A-SIR model overcame the SIR model in simulating the first wave, whereas these discrepancies are reduced in simulating the second wave, when the accuracy of the epidemiological data is considerably higher. These results indicate that increasing the complexity of the model is useless and unnecessarily wasteful if not supported by an increased quality of the available data
In HIV/HCV coinfected patients dendritic cell activation state is not associated with IL28B genotype
No full textThe Use of Both Therapeutic and Prophylactic Vaccines in the Therapy of Papillomavirus Disease
Full text linkHuman papillomavirus (HPV) is the most common sexually transmitted virus. The high-risk HPV types (i.e., HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59) are considered to be the main etiological agents of genital tract cancers, such as cervical, vulvar, vaginal, penile, and anal cancers, and of a subset of head and neck cancers. Three prophylactic HPV vaccines are available that are bivalent (vs. HPV16, 18), tetravalent (vs. HPV6, 11, 16, 18), and non-avalent (vs. HPV6, 11, 16, 18, 31, 33,45, 52, 58). All of these vaccines are based on recombinant DNA technology, and they are prepared from the purified L1 protein that self-assembles to form the HPV type-specific empty shells (i.e., virus-like particles). These vaccines are highly immunogenic and induce specific antibodies. Therapeutic vaccines differ from prophylactic vaccines, as they are designed to generate cell-mediated immunity against transformed cells, rather than neutralizing antibodies. Among the HPV proteins, the E6 and E7 oncoproteins are considered almost ideal as targets for immunotherapy of cervical cancer, as they are essential for the onset and evolution of malignancy and are constitutively expressed in both premalignant and invasive lesions. Several strategies have been investigated for HPV therapeutic vaccines designed to enhance CD4+ and CD8+ T-cell responses, including genetic vaccines (i.e., DNA/ RNA/virus/ bacterial), and protein-based, peptide-based or dendritic-cell-based vaccines. However, no vaccine has yet been licensed for therapeutic use. Several studies have suggested that administration of prophylactic vaccines immediately after surgical treatment of CIN2 cervical lesions can be considered as an adjuvant to prevent reactivation or reinfection, and other studies have described the relevance of prophylactic vaccines in the management of genital warts. This review summarizes the leading features of therapeutic vaccines, which mainly target the early oncoproteins E6 and E7, and prophylactic vaccines, which are based on the L1 capsid protein. Through an analysis of the specific immunogenic properties of these two types of vaccines, we discuss why and how prophylactic vaccines can be effective in the treatment of HPV-related lesions and relapse
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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