scholarly journals Real-time predictions of reservoir size and rebound time during antiretroviral therapy interruption trials for HIV

2016 ◽  
Author(s):  
Alison L Hill ◽  
Daniel Scholes Rosenbloom ◽  
Edward Goldstein ◽  
Emily Hanhauser ◽  
Daniel R Kuritzkes ◽  
...  
Keyword(s):  
Viral Load ◽  
Antiretroviral Therapy ◽  
Real Time ◽  
Latent Infection ◽  
Optimal Schedule ◽  
Simulated Data ◽  
Latent Reservoir ◽  
Modeling Tools ◽  
Hematopoietic Stem ◽  
Relative Contribution

Monitoring the efficacy of novel reservoir-reducing treatments for HIV is challenging. The limited ability to sample and quantify latent infection means that supervised antiretroviral therapy (ART) interruption studies are generally required. Here we introduce a set of mathematical and statistical modeling tools to aid in the design and interpretation of ART-interruption trials. We show how the likely size of the remaining reservoir can be updated in real-time as patients continue off treatment, by combining the output of laboratory assays with insights from models of reservoir dynamics and rebound. We design an optimal schedule for viral load sampling during interruption, whereby the frequency of follow-up can be decreased as patients continue off ART without rebound. While this scheme can minimize costs when the chance of rebound between visits is low, we find that the reservoir will be almost completely reseeded before rebound is detected unless sampling occurs at least every two weeks and the most sensitive viral load assays are used. We use simulated data to predict the clinical trial size needed to estimate treatment effects in the face of highly variable patient outcomes and imperfect reservoir assays. Our findings suggest that large numbers of patients - between 40 and 150 - will be necessary to reliably estimate the reservoir-reducing potential of a new therapy and to compare this across interventions. As an example, we apply these methods to the two "Boston patients", recipients of allogeneic hematopoietic stem cell transplants who experienced large reductions in latent infection and underwent ART-interruption. We argue that the timing of viral rebound was not particularly surprising given the information available before treatment cessation. Additionally, we show how other clinical data can be used to estimate the relative contribution that remaining HIV+ cells in the recipient versus newly infected cells from the donor made to the residual reservoir that eventually caused rebound. Together, these tools will aid HIV researchers in the evaluating new potentially-curative strategies that target the latent reservoir.

scholarly journals Models of SIV rebound after treatment interruption that involve multiple reactivation events

2020 ◽  
Author(s):  
Christiaan H. van Dorp ◽  
Jessica M. Conway ◽  
James B. Whitney ◽  
Dan H. Barouch ◽  
Alan S. Perelson
Keyword(s):  
Growth Rate ◽  
Viral Load ◽  
Antiretroviral Therapy ◽  
Treatment Interruption ◽  
Latent Reservoir ◽  
Viral Rebound ◽  
Viral Growth ◽  
Art Initiation ◽  
Reactivation Event ◽  
Hiv 1

AbstractIn order to assess the efficacy of novel HIV-1 treatments leading to a functional cure, the time to viral rebound is frequently used as a surrogate endpoint. The longer the time to viral rebound, the more efficacious the therapy. In support of such an approach, mathematical models serve as a connection between the size of the latent reservoir and the time to HIV-1 rebound after treatment interruption. The simplest of such models assumes that a single successful latent cell reactivation event leads to observable viremia after a period of exponential viral growth. Here we consider a generalization developed by Pinkevych et al. and Hill et al. of this simple model in which multiple reactivation events can occur, each contributing to the exponential growth of the viral load. We formalize and improve the previous derivation of the dynamics predicted by this model, and use the model to estimate relevant biological parameters from SIV rebound data. We confirm a previously described effect of very early antiretroviral therapy (ART) initiation on the rate of recrudescence and the viral load growth rate after treatment interruption. We find that every day ART initiation is delayed results in a 39% increase in the recrudescence rate, and a 11% decrease of the viral growth rate. We show that when viral rebound occurs early relative to the viral load doubling time, a model with multiple successful reactivation events fits the data better than a model with only a single successful reactivation event.Author SummaryHIV-1 persists during suppressive antiretroviral therapy (ART) due to a reservoir of latently infected cells. When ART is stopped, HIV generally rebounds within a few weeks. However, there is a small fraction of patients that do not rebound over a period of months or years. A variety of treatments are being tested for their ability to reduce the size of the latent reservoir, to induce effective immune responses against the virus, or to prevent or prolong the time to viral rebound after ART interruption. These novel treatments are typically first tested in SIV infected macaques, and the efficacy of the treatment assessed by interrupting ART and measuring the time to viral rebound. Here, we develop and test a mathematical and statistical model that describes the process of viral rebound. The model can be used for statistical inference of the efficacy of newly developed treatments. Importantly, the model takes into account that multiple recrudescence events can precede rebound. We test the model using data from early treated SIV infected macaques.

scholarly journals Viral Dynamics and Real-Time RT-PCR Ct Values Correlation with Disease Severity in COVID-19

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1091
Author(s):  
Ali A. Rabaan ◽  
Raghavendra Tirupathi ◽  
Anupam A Sule ◽  
Jehad Aldali ◽  
Abbas Al Mutair ◽  
...  
Keyword(s):  
Viral Load ◽  
Real Time ◽  
Disease Severity ◽  
False Negative ◽  
Influential Factors ◽  
Confirmatory Test ◽  
Acid Extraction ◽  
Rt Pcr ◽  
Viral Kinetics ◽  
Ct Value

Real-time RT-PCR is considered the gold standard confirmatory test for coronavirus disease 2019 (COVID-19). However, many scientists disagree, and it is essential to understand that several factors and variables can cause a false-negative test. In this context, cycle threshold (Ct) values are being utilized to diagnose or predict SARS-CoV-2 infection. This practice has a significant clinical utility as Ct values can be correlated with the viral load. In addition, Ct values have a strong correlation with multiple haematological and biochemical markers. However, it is essential to consider that Ct values might be affected by pre-analytic, analytic, and post-analytical variables such as collection technique, specimen type, sampling time, viral kinetics, transport and storage conditions, nucleic acid extraction, viral RNA load, primer designing, real-time PCR efficiency, and Ct value determination method. Therefore, understanding the interpretation of Ct values and other influential factors could play a crucial role in interpreting viral load and disease severity. In several clinical studies consisting of small or large sample sizes, several discrepancies exist regarding a significant positive correlation between the Ct value and disease severity in COVID-19. In this context, a revised review of the literature has been conducted to fill the knowledge gaps regarding the correlations between Ct values and severity/fatality rates of patients with COVID-19. Various databases such as PubMed, Science Direct, Medline, Scopus, and Google Scholar were searched up to April 2021 by using keywords including “RT-PCR or viral load”, “SARS-CoV-2 and RT-PCR”, “Ct value and viral load”, “Ct value or COVID-19”. Research articles were extracted and selected independently by the authors and included in the present review based on their relevance to the study. The current narrative review explores the correlation of Ct values with mortality, disease progression, severity, and infectivity. We also discuss the factors that can affect these values, such as collection technique, type of swab, sampling method, etc.

scholarly journals STING and cGAS gene expressions were downregulated among HIV-1-infected persons after antiretroviral therapy

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Lorena Leticia Peixoto de Lima ◽  
Allysson Quintino Tenório de Oliveira ◽  
Tuane Carolina Ferreira Moura ◽  
Ednelza da Silva Graça Amoras ◽  
Sandra Souza Lima ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Load ◽  
T Cell ◽  
Antiretroviral Therapy ◽  
Cell Count ◽  
Enzyme Linked Immunosorbent Assay ◽  
Type I ◽  
Α Level ◽  
The Impact ◽  
Hiv 1

Abstract Background The HIV-1 epidemic is still considered a global public health problem, but great advances have been made in fighting it by antiretroviral therapy (ART). ART has a considerable impact on viral replication and host immunity. The production of type I interferon (IFN) is key to the innate immune response to viral infections. The STING and cGAS proteins have proven roles in the antiviral cascade. The present study aimed to evaluate the impact of ART on innate immunity, which was represented by STING and cGAS gene expression and plasma IFN-α level. Methods This cohort study evaluated a group of 33 individuals who were initially naïve to therapy and who were treated at a reference center and reassessed 12 months after starting ART. Gene expression levels and viral load were evaluated by real-time PCR, CD4+ and CD8+ T lymphocyte counts by flow cytometry, and IFN-α level by enzyme-linked immunosorbent assay. Results From before to after ART, the CD4+ T cell count and the CD4+/CD8+ ratio significantly increased (p < 0.0001), the CD8+ T cell count slightly decreased, and viral load decreased to undetectable levels in most of the group (84.85%). The expression of STING and cGAS significantly decreased (p = 0.0034 and p = 0.0001, respectively) after the use of ART, but IFN-α did not (p = 0.1558). Among the markers evaluated, the only markers that showed a correlation with each other were STING and CD4+ T at the time of the first collection. Conclusions ART provided immune recovery and viral suppression to the studied group and indirectly downregulated the STING and cGAS genes. In contrast, ART did not influence IFN-α. The expression of STING and cGAS was not correlated with the plasma level of IFN-α, which suggests that there is another pathway regulating this cytokine in addition to the STING–cGAS pathway.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

scholarly journals Persistence of SARS-CoV-2 Viral RNA in Nasopharyngeal Swabs after Death: An Observational Study

2021 ◽  
Vol 9 (4) ◽  
pp. 800
Author(s):  
Francesca Servadei ◽  
Silvestro Mauriello ◽  
Manuel Scimeca ◽  
Bartolo Caggiano ◽  
Marco Ciotti ◽  
...  
Keyword(s):  
Viral Load ◽  
Observational Study ◽  
Real Time ◽  
Real Time Pcr ◽  
Pcr Detection ◽  
Viral Rna ◽  
Clinical Documentation ◽  
Post Mortem ◽  
Medical Assistance ◽  
Time Points

The aim of this study was to investigate the persistence of SARS-CoV-2 in post-mortem swabs of subjects who died from SARS-CoV-2 infection. The presence of the virus was evaluated post-mortem from airways of 27 SARS-CoV-2 positive patients at three different time points (T1 2 h; T2 12 h; T3 24 h) by real-time PCR. Detection of antibodies to SARS-CoV-2 was performed by Maglumi 2019-nCoV IgM/IgG chemiluminescence assay. SARS-CoV-2 viral RNA was still detectable in 70.3% of cases within 2 h after death and in 66,6% of cases up to 24 h after death. Our data showed an increase of the viral load in 78,6% of positive individuals 24 h post-mortem (T3) in comparison to that evaluated 2 h after death (T1). Noteworthy, we detected a positive T3 post-mortem swab (24 h after death) from 4 subjects who were negative at T1 (2 h after death). The results of our study may have an important value in the management of deceased subjects not only with a suspected or confirmed diagnosis of SARS-CoV-2, but also for unspecified causes and in the absence of clinical documentation or medical assistance.

Proviral HIV-1 DNA in subjects followed since primary HIV-1 infection who suppress plasma viral load after one year of highly active antiretroviral therapy

AIDS ◽  
2001 ◽  
Vol 15 (6) ◽  
pp. 665-673 ◽  
Author(s):  
Nicole Ngo-Giang-Huong ◽  
Christiane Deveau ◽  
Isabelle Da Silva ◽  
Isabelle Pellegrin ◽  
Alain Venet ◽  
...  
Keyword(s):  
Viral Load ◽  
Antiretroviral Therapy ◽  
Highly Active Antiretroviral Therapy ◽  
Plasma Viral Load ◽  
Highly Active ◽  
One Year ◽  
Hiv 1
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