Unified model of short- and long-term HIV viral rebound for clinical trial planning

Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Typically suspension of therapy is rapidly followed by rebound of viral loads to high, pre-therapy levels. Indeed, a recent study showed that approximately 90% of treatment interruption study participants show viral rebound within at most a few months of therapy suspension, but the remaining 10%, showed viral rebound some months, or years, after ART suspension. Some may even never rebound. We investigate and compare branching process models aimed at gaining insight into these viral dynamics. Specifically, we provide a theory that explains both short- and long-term viral rebounds, and post-treatment control, via a multitype branching process with time-inhomogeneous rates, validated with data from Li et al. (Li et al. 2016 AIDS 30, 343–353. (doi:10.1097/QAD.0000000000000953)). We discuss the associated biological interpretation and implications of our best-fit model. To test the effectiveness of an experimental intervention in delaying or preventing rebound, the standard practice is to suspend therapy and monitor the study participants for rebound. We close with a discussion of an important application of our modelling in the design of such clinical trials.     

Time variation in the probability of failing to detect a case of polymerase chain reaction testing for SARS-CoV-2 as estimated from a viral dynamics model

Viral tests including polymerase chain reaction (PCR) tests are recommended to diagnose COVID-19 infection during the acute phase of infection. A test should have high sensitivity; however, the sensitivity of the PCR test is highly influenced by viral load, which changes over time. Because it is difficult to collect data before the onset of symptoms, the current literature on the sensitivity of the PCR test before symptom onset is limited. In this study, we used a viral dynamics model to track the probability of failing to detect a case of PCR testing over time, including the presymptomatic period. The model was parametrized by using longitudinal viral load data collected from 30 hospitalized patients. The probability of failing to detect a case decreased toward symptom onset, and the lowest probability was observed 2 days after symptom onset and increased afterwards. The probability on the day of symptom onset was 1.0% (95% CI: 0.5 to 1.9) and that 2 days before symptom onset was 60.2% (95% CI: 57.1 to 63.2). Our study suggests that the diagnosis of COVID-19 by PCR testing should be done carefully, especially when the test is performed before or way after symptom onset. Further study is needed of patient groups with potentially different viral dynamics, such as asymptomatic cases.     

Prediction of the containment of HIV infection by antiretroviral therapy – a variable structure control approach

It is demonstrated that the reachability paradigm from variable structure control theory is a suitable framework to monitor and predict the progression of the human immunodeficiency virus (HIV) infection following initiation of antiretroviral therapy (ART). A manifold is selected which characterises the infection‐free steady‐state. A model of HIV infection together with an associated reachability analysis is used to formulate a dynamical condition for the containment of HIV infection on the manifold. This condition is tested using data from two different HIV clinical trials which contain measurements of the CD4+ T cell count and HIV load in the peripheral blood collected from HIV infected individuals for the six month period following initiation of ART. The biological rates of the model are estimated using the multi‐point identification method and data points collected in the initial period of the trial. Using the parameter estimates and the numerical solutions of the model, the predictions of the reachability analysis are shown to be consistent with the clinical diagnosis at the conclusion of the trial. The methodology captures the dynamical characteristics of eventual successful, failed and marginal outcomes. The findings evidence that the reachability analysis is an appropriate tool to monitor and develop personalised antiretroviral treatment.Inspec keywords: microorganisms, patient treatment, drugs, parameter estimationOther keywords: HIV infection, antiretroviral therapy, variable structure control theory, human immunodeficiency virus, antiretroviral drugs, multipoint identification method, parameter estimation, reachability analysis     

The effect of changes in condom usage and antiretroviral treatment coverage on human immunodeficiency virus incidence in South Africa: a model-based analysis

This study aims to assess trends in human immunodeficiency virus (HIV) incidence in South Africa, and to assess the extent to which prevention and treatment programmes have reduced HIV incidence. Two models of the South African HIV epidemic, the STI (sexually transmitted infection)–HIV Interaction model and the ASSA2003 AIDS and Demographic model, were adapted. Both models were fitted to age-specific HIV prevalence data from antenatal clinic surveys and household surveys, using a Bayesian approach. Both models suggest that HIV incidence in 15–49 year olds declined significantly between the start of 2000 and the start of 2008: by 27 per cent (95% CI: 21–32%) in the STI–HIV model and by 31 per cent (95% CI: 23–39%) in the ASSA2003 model, when expressed as a percentage of incidence rates in 2000. By 2008, the percentage reduction in incidence owing to increased condom use was 37 per cent (95% CI: 34–41%) in the STI–HIV model and 23 per cent (95% CI: 14–34%) in the ASSA2003 model. Both models also estimated a small reduction in incidence owing to antiretroviral treatment by 2008. Increased condom use therefore appears to be the most significant factor explaining the recent South African HIV incidence decline.     

Improving Timing Offset Estimation by Alias Sampling

In a previous paper, we reported an algorithm that can be used to accurately measure sampling timing errors in a data acquisition system that encounters nonuniform sampling. In this paper, we first study the sensitivity of the algorithm to input frequency inaccuracy. We then investigate the dependency of the accuracy of the algorithm on the number of effective bits in an analog-to-digital (A/D) converter. It is observed that, if the initial timing error is "reasonably large," then the residual timing error decreases by one order of magnitude for each increase in the number of effective bits by four. Finally, we propose the use of "alias sampling" to "magnify" the timing error so that the algorithm's sensitivity is greatly improved and can be used to estimate a much smaller timing offset with only a modest number of effective bits in the A/D converter.     

Effect of different modes of viral spread on the dynamics of multiply infected cells in human immunodeficiency virus infection

Infection of individual cells with more than one HIV particle is an important feature of HIV replication, which may contribute to HIV pathogenesis via the occurrence of recombination, viral complementation and other outcomes that influence HIV replication and evolutionary dynamics. A previous mathematical model of co-infection has shown that the number of cells infected with i viruses correlates with the ith power of the singly infected cell population, and this has partly been observed in experiments. This model, however, assumed that virus spread from cell to cell occurs only via free virus particles, and that viruses and cells mix perfectly. Here, we introduce a cellular automaton model that takes into account different modes of virus spread among cells, including cell to cell transmission via the virological synapse, and spatially constrained virus spread. In these scenarios, it is found that the number of multiply infected cells correlates linearly with the number of singly infected cells, meaning that co-infection plays a greater role at lower virus loads. The model further indicates that current experimental systems that are used to study co-infection dynamics fail to reflect the true dynamics of multiply infected cells under these specific assumptions, and that new experimental techniques need to be designed to distinguish between the different assumptions.     

Why CCR5 is chosen as the target for stem cell gene therapy for HIV infection?

Since the first case reported in 1981, more than 60 million individuals have succumbed to HIV worldwide. Although great efforts have been put forth in developing therapeutic drugs and effective vaccines for the treatment and prevention of HIV infection, these efforts are not correlated to the reported case of cured HIV infection. The first case of cured HIV infection, from allogenic stem cell transplantation, may shed light on future prevention and therapy of HIV infection. The choice of gene target, however, must first be evaluated regarding stem cell based gene therapy for HIV infection. Out of the tens of genes that had shown anti-HIV infection potentials, CCR5 was described to be effective in stem cell based gene therapy in 2005. Here, we appreciate the clinical observations that directly led to the choice of CCR5, rather than other genes for stem cell gene therapy.     

Modelling HIV-1 2-LTR dynamics following raltegravir intensification

A model of reservoir activation and viral replication is introduced accounting for the production of 2-LTR HIV-1 DNA circles following antiviral intensification with the HIV integrase inhibitor raltegravir, considering contributions of de novo infection events and exogenous sources of infected cells, including quiescent infected cell activation. The model shows that a monotonic increase in measured 2-LTR concentration post intensification is consistent with limited de novo infection primarily maintained by sources of infected cells unaffected by raltegravir, such as quiescent cell activation, while a transient increase in measured 2-LTR concentration is consistent with significant levels of efficient (R₀ > 1) de novo infection. The model is validated against patient data from the INTEGRAL study and is shown to have a statistically significant fit relative to the null hypothesis of random measurement variation about a mean. We obtain estimates and confidence intervals for the model parameters, including 2-LTR half-life. Seven of the 13 patients with detectable 2-LTR concentrations from the INTEGRAL study have measured 2-LTR dynamics consistent with significant levels of efficient replication of the virus prior to treatment intensification.     

A Comparison of Inspector Performance Measures

Using experimental data from their signal-detection study, the authors examine traditional measures of inspector performance along with measures based on Bayes'rule. Results indicate that the inspector's decision process is affected by the a priori probability of nonconforming product in the inspection lot and that inspector decision error may result in a tighter OC curve for a sampling plan. The conclusions are that performance measures should be selected relative to performance evaluation objectives and that graphs describing acceptance sampling plans for ideal inspectors should be modified to reflect performance of real inspectors under actual conditions.     

Protocols for sampling viral sequences to study epidemic dynamics

With more emphasis being put on global infectious disease monitoring, viral genetic data are being collected at an astounding rate, both within and without the context of a long-term disease surveillance plan. Concurrent with this increase have come improvements to the sophisticated and generalized statistical techniques used for extracting population-level information from genetic sequence data. However, little research has been done on how the collection of these viral sequence data can or does affect the efficacy of the phylogenetic algorithms used to analyse and interpret them. In this study, we use epidemic simulations to consider how the collection of viral sequence data clarifies or distorts the picture, provided by the phylogenetic algorithms, of the underlying population dynamics of the simulated viral infection over many epidemic cycles. We find that sampling protocols purposefully designed to capture sequences at specific points in the epidemic cycle, such as is done for seasonal influenza surveillance, lead to a significantly better view of the underlying population dynamics than do less-focused collection protocols. Our results suggest that the temporal distribution of samples can have a significant effect on what can be inferred from genetic data, and thus highlight the importance of considering this distribution when designing or evaluating protocols and analysing the data collected thereunder.     

Workload control in unbalanced job shops

Workload control (WLC) has been developed as a production planning and control approach for job shop manufacturing. By balancing loads across work centres, WLC anticipates the fact that multiple work centres may become potential bottlenecks in the short term. This approach is generally tested in job shop models that assume equal utilisation levels for all work centres, which will create maximum bottleneck shiftiness. However, job shop practice clearly shows differences in utilisation levels, which means that some work centres can be seen as non-bottlenecks, having protective capacity. This study investigates the effect of different levels of protective capacity on the performance of state-of-the-art WLC release methods. More in detail, it shows how the level of protective capacity interacts with the influence of workload norms at work centres. Despite the fact that WLC has been developed for highly balanced utilisations, results indicate that WLC could also be effective in unbalanced situations. However, norm setting requires careful attention. Disregarding the norms of non-bottleneck work centres, a common sense approach, might strongly deteriorate performance when the level of protective capacity is not sufficiently high. Contrarily, tighter norms for non-bottleneck work centres are shown to perform better in this situation.     

Binding of HIV-1 gp120 glycoprotein to silica nanoparticles modified with CD4 glycoprotein and CD4 peptide fragments

An important step in human immunodeficiency virus infection involves the interaction between the viral envelope glycoprotein gp120 and the human host cell surface receptor CD4. Herein, we describe a CD4-functionalized mesoporous silica-based system to selectively capture HIV-gp120 with high binding efficiency. Using a protection-deprotection strategy developed recently by our group, the external surface of the mesoporous particles was selectively functionalized with soluble CD4 ("sCD4") or an 18-peptide fragment mimicking the gp120 binding region. Confocal microscopy confirmed the CD4 locations and showed that the internal pores can be made accessible after external modification in a controlled manner. An evaluation of the ability of an 18-peptide CD4 fragment versus amide-immobilized sCD4 and sCD4 immobilized through its glycosidic group indicated that while all peptides were selective, the latter method was clearly best, with nearly complete removal of whole gp120 from solution. This study shows, for the first time, that sCD4 bound to mesoporous silica particles actively recognizes and retains high binding affinity for HIV-gp120. It is anticipated that, by proper modification of the accessible internal pores, our methodology can be adopted to develop porous platforms for HIV diagnosis, imaging, drug delivery, and vaccine development.     

Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza and Streptococcus pneumoniae

A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with both Streptococcus pneumoniae and Staphylococcus aureus. Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immune-mediated model of the viral–bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus and S. pneumoniae in the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4–6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.     

Occurrence of HIV eradication for preexposure prophylaxis treatment with a deterministic HIV model

The authors examine the human immunodeficiency virus (HIV) eradication in this study using a mathematical model and analyse the occurrence of virus eradication during the early stage of infection. To this end they use a deterministic HIV‐infection model, modify it to describe the pharmacological dynamics of antiretroviral HIV drugs, and consider the clinical experimental results of preexposure prophylaxis HIV treatment. They also use numerical simulation to model the experimental scenario, thereby supporting the clinical results with a model‐based explanation. The study results indicate that the protocol employed in the experiment can eradicate HIV in infected patients at the early stage of the infection.Inspec keywords: diseases, numerical analysis, drugs, patient treatmentOther keywords: preexposure prophylaxis HIV treatment, antiretroviral HIV drug, pharmacological dynamics, deterministic HIV‐infection model, early infection stage, virus eradication, mathematical model, human immunodeficiency virus, HIV eradication     

基于EDFT的非均匀欠采样叶尖定时信号分析

针对叶尖定时信号因严重的非均匀采样和欠采样导致的谱分析难题,提出了基于扩展离散傅里叶变换(EDFT)的分析方法。不同于传统的傅里叶变换,EDFT是以傅里叶积分变换为目标,在扩展的频率范围内通过构造和优化变换基函数替代传统FFT变换中的指数基来实现非均匀和欠采样信号的谱分析,在分析中利用原始数据迭代和近似拟合保证了分析精度。该方法突破了奈奎斯特采样定理的限制,扩大了分析频率范围,分析谱线数也不再受限于采样点数,提高了频率分辨率。为了验证所提方法的可行性和可靠性,采用所构建的叶尖定时系统采样数学模型生成数据和试验台测试数据分别进行了方法应用分析,结果显示无论是仿真数据或试验数据,当传感器数量大于等于3时,基于该方法能够准确分析得到叶片振动的谱信号,表明该方法可以有效地解决非均匀欠采样叶尖定时信号的谱分析的难题,且具有良好的抗干扰性。     

Traceable Waveform Calibration With a Covariance-Based Uncertainty Analysis

We describe a method for calibrating the voltage that a step-like pulse generator produces at a load at every time point in the measured waveform. The calibration includes an equivalent-circuit model of the generator that can be used to determine how the generator behaves when it is connected to arbitrary loads. The generator is calibrated with an equivalent-time sampling oscilloscope and is traceable to fundamental physics via the electro-optic sampling system at the National Institute of Standards and Technology. The calibration includes a covariance-based uncertainty analysis that provides the uncertainty at each time in the waveform vector and the correlations between the uncertainties at the different times. From the calibrated waveform vector and its covariance matrix, we calculate pulse parameters and their uncertainties. We compare our method with a more traditional parameter-based uncertainty analysis.     

Assessing the impact of adherence to anti-retroviral therapy on treatment failure and resistance evolution in HIV

The adherence of patients to therapy is a crucial factor for successful HIV anti-retroviral therapy. Imperfect adherence may lead to treatment failure, which can cause the emergence of resistance within viral populations. We have developed a stochastic model that incorporates compartments of latently infected cells and virus genotypes with different susceptibilities to three simultaneously used drugs. With this model, we study the impact of several key parameters on the probability of treatment failure, i.e. insufficient viral suppression, and the emergence of resistance. Specifically, we consider the impact of drug dosage, drug half-lives, fitness costs for resistance, different basic reproductive numbers of the virus and the influence of pre-existing mutations under various levels of adherence. Furthermore, we also investigate the influence of different temporal distributions of non-adherent days (drug holidays) during a treatment. Factors that promote resistance evolution include a high reproductive number, extended drug holidays and poor adherence. Pre-existing mutations only have a substantial effect if they confer resistance against more than one drug. Overall, our study highlights the importance of the interactions between imperfect adherence, pharmacodynamics, pharmacokinetics and latently infected cells for our understanding of drug resistance and therapy failure in HIV anti-retroviral therapy.     

A deep learning approach for predicting critical events using event logs

Event logs, comprising data on the occurrence of different types of events and associated times, are commonly collected during the operation of modern industrial machines and systems. It is widely believed that the rich information embedded in event logs can be used to predict the occurrence of critical events. In this paper, we propose a recurrent neural network model using time-to-event data from event logs not only to predict the time of the occurrence of a target event of interest, but also to interpret, from the trained model, significant events leading to the target event. To improve the performance of our model, sampling techniques and methods dealing with the censored data are utilized. The proposed model is tested on both simulated data and real-world datasets. Through these comparison studies, we show that the deep learning approach can often achieve better prediction performance than the traditional statistical model, such as, the Cox proportional hazard model. The real-world case study also shows that the model interpretation algorithm proposed in this work can reveal the underlying physical relationship among events.     

Variance‐corrected proportional hazard models for the analysis of multiple failures in personal computers

Proportional hazard (PH) modeling is widely used in several areas of study to estimate the effect of covariates on event timing. In this paper, this model is explored for the analysis of multiple occurrences of hardware failures in personal computers. Multiple failure events consist of correlated data, and thus the assumption of independence among failure times is violated. This study critically describes a class of models known as variance‐corrected PH models for modeling multiple failure time data, without assuming independence among failure times. The objective of this study is to determine the effect of the computer brand on event timings of hardware failures and to test whether this effect varies over multiple failure occurrences. This study revealed that the computer brand affects hardware failure event timing and that further, this effect of the brand does not change over the multiple failure occurrences. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust control of HIV infection by antiretroviral therapy: a super‐twisting sliding mode control approach

Acquired immune deficiency syndrome is an epidemic infectious disease which is caused by the human immunodeficiency virus (HIV) and that has proliferated across worldwide. It has been a matter of concern for the scientific community to develop an antiretroviral therapy, which will prompt a rapid decline in viral abundance. With this motivation, this study proposes the design of a robust super twisting sliding mode controller based on output information for an uncertain HIV infection model. The control objective is to decrease the concentration of infected CD4+ T cells to a specified level by drug administration using only the output information of the uncertain HIV infection model which is total CD4+ T cell concentration. The robust output‐feedback controller has been developed in combination with a robust exact differentiator, functioning as an observer. The reported analysis demonstrates that the approach proposed here is capable of ensuring robust performance under several operating conditions, measurement and modelling error, parametric uncertainties and external disturbances and the simulation results prove the proficiency of the controller proposed.Inspec keywords: control system synthesis, observers, robust control, drugs, medical control systems, diseases, uncertain systems, variable structure systems, patient treatment, feedback, cellular biophysics, microorganismsOther keywords: robust control, antiretroviral therapy, sliding mode control approach, acquired immune deficiency syndrome, epidemic infectious disease, human immunodeficiency virus, scientific community, robust super, mode controller, output information, uncertain HIV infection model, control objective, infected CD4, total CD4, T cell concentration, robust output‐feedback controller, robust exact differentiator, robust performance