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Objective: The goal of this study is to evaluate the feasibility and efficacy of an auricular point acupressure smartphone app (mAPA) to self-manage chronic musculoskeletal pain. Methods: A prospective, longitudinal, randomized, controlled pilot trial was conducted using a three-group design (self-guided mAPA (n = 14); in-person mAPA (n = 12); and control (n = 11)). The primary outcomes included physical function and pain intensity. Results: After a 4-week APA intervention, participants in the in-person mAPA group had improved physical function of 32% immediately post-intervention and 29% at the 1M follow-up. Participants in the self-guided mAPA group had higher improvement (42% at post-intervention and 48% at the 1M follow-up). Both mAPA groups had similar degrees of pain intensity relief at post-intervention (45% for in-person and 48% for the self-guided group) and the 1M follow-up (42% for in-person and 45% for the self-guided group). Over 50% of the participants in each group reached at least 30% reduced pain intensity at post-intervention, and this was sustained in the mAPA groups at the 1M follow-up. Approximately 80% of the participants in both mAPA groups were satisfied with the treatment outcomes and adhered to the suggested APA practice; however, participants in the self-guided group had higher duration and more frequency in APA use. The attrition rate was 16% at the 1M follow-up. No adverse effects of APA were reported, and participants found APA to be beneficial and the app to be valuable. Conclusions: The study findings indicate that participants effectively learned APA using a smartphone app, whether they were self-guided or received in-person training. They were able to self-administer APA to successfully manage their pain. Participants found APA to be valuable in their pain self-management and expressed satisfaction with the intervention using the app.

Sarcopenia, generally described as "aging-related loss of skeletal muscle mass and function", can occur secondary to a systemic disease.This project aimed to study the prevalence of sarcopenia in chronic ambulatory stroke survivors and its associated risk factors using the two most recent diagnostic criteria.A cross-sectional observational study.A scientific laboratory.Chronic stroke.Twenty-eight ambulatory chronic stroke survivors (12 females; mean age=57.8±11.8 years; time after stroke=76±45 months), hand-grip strength, gait speed, and appendicular skeletal muscle mass (ASM) were measured to define sarcopenia. Risk factors, including motor impairment and spasticity, were identified using regression analysis.The prevalence of sarcopenia varied between 18% and 25% depending on the diagnostic criteria used. A significant difference was seen in the prevalence of low hand grip strength on the affected side (96%) when compared to the contralateral side (25%). The prevalence of slow gait speed was 86% while low ASM was present in 89% of the subjects. Low ASM was marginally negatively correlated with time since stroke and gait speed, but no correlation was observed with age, motor impairment, or spasticity. ASM loss, bone loss and fat deposition were significantly greater in the affected upper limb than in the affected lower limb. Regression analyses showed that time since stroke was a factor associated with bone and muscle loss in the affected upper limb, spasticity had a protective role for muscle loss in the affected lower limb, and walking had a protective role for bone loss in the lower limb.The prevalence of sarcopenia in stroke survivors is high and is a multifactorial process that is not age-related. Different risk factors contribute to muscle loss in the upper and lower limbs after stroke.Clinicians need to be aware of high prevalence of sarcopenia in chronic stroke survivors. Sarcopenia is more evident in the upper than lower limbs. Clinicians also need to understand potential protective roles of some factors, such as spasticity and walking for the muscles in the lower limb.

COVID-19 epidemics exhibited multiple waves regionally and globally since 2020. It is important to understand the insight and underlying mechanisms of the multiple waves of COVID-19 epidemics in order to design more efficient non-pharmaceutical interventions (NPIs) and vaccination strategies to prevent future waves. We propose a multi-scale model by linking the behaviour change dynamics to the disease transmission dynamics to investigate the effect of behaviour dynamics on COVID-19 epidemics using game theory. The proposed multi-scale models are calibrated and key parameters related to disease transmission dynamics and behavioural dynamics with/without vaccination are estimated based on COVID-19 epidemic data (daily reported cases and cumulative deaths) and vaccination data. Our modeling results demonstrate that the feedback loop between behaviour changes and COVID-19 transmission dynamics plays an essential role in inducing multiple epidemic waves. We find that the long period of high-prevalence or persistent deterioration of COVID-19 epidemics could drive almost all of the population to change their behaviours and maintain the altered behaviours. However, the effect of behaviour changes fades out gradually along the progress of epidemics. This suggests that it is essential to have not only persistent, but also effective behaviour changes in order to avoid subsequent epidemic waves. In addition, our model also suggests the importance to maintain the effective altered behaviours during the initial stage of vaccination, and to counteract relaxation of NPIs, it requires quick and massive vaccination to avoid future epidemic waves.

Objective Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients. Methods The cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts® EMR database (2000–2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice. Machine learning‐based models were developed and primarily assessed by area under the receiver operating characteristic curve (AUC). Results A total of 2467 nontraumatic SAH patients (64% female; median age [interquartile range]: 56 [47–66]) who were treated with vasopressors for presumed DCI were included in the study. 934 (38%) patients died or were discharged to hospice. The model achieved an AUC of 0.88 (95% CI, 0.84–0.92) with only the initial 24 h EHR data, and 0.94 (95% CI, 0.92–0.96) after the next 24 h. Interpretation EHR data and machine learning models can accurately predict the risk of the adverse outcome for critically ill nontraumatic SAH patients. It is possible to use EHR data and machine learning techniques to help with clinical decision‐making.

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from ‘big data’ are lacking – the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of endocrine resistance and identified a novel group of genes whose time course expression significantly change during cell modelling of endocrine resistant BC development. Expression of a subset of these genes was also differentially expressed in microarray analysis of endocrine-resistant and endocrine-sensitive tumor samples. Surprisingly, a subset of those genes was also differentially genes expressed in triple-negative breast cancer (TNBC) as compared with ER-positive BC. The findings suggest shared genetic mechanisms may underlie the development of endocrine resistant BC and TNBC. Our findings identify 34 novel genes for further study as potential therapeutic targets for treatment of endocrine-resistant BC and TNBC.

BackgroundAs the availability of COVID-19 vaccines, it is badly needed to develop vaccination guidelines to prioritize the vaccination delivery in order to effectively stop COVID-19 epidemic and minimize the loss.MethodsWe evaluated the effect of age-specific vaccination strategies on the number of infections and deaths using an SEIR model, considering the age structure and social contact patterns for different age groups for each of different countries.ResultsIn general, the vaccination priority should be given to those younger people who are active in social contacts to minimize the number of infections; while the vaccination priority should be given to the elderly to minimize the number of deaths. But this principle may not always apply when the interaction of age structure and age-specific social contact patterns is complicated. Partially reopening schools, workplaces or households, the vaccination priority may need to be adjusted accordingly.ConclusionsPrematurely reopening social contacts could initiate a new outbreak or even a new pandemic out of control if the vaccination rate and the detection rate are not high enough. Our result suggests that it requires at least nine months of vaccination before fully reopening social contacts in order to avoid a new pandemic.

Objective To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner. Material and methods This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed. Results The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values. Conclusions Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.

It is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. Sharing data with fellow researchers helps in increasing the visibility of the work. On the other hand, there are researchers who are inhibited by the lack of data resources. To overcome this challenge, many repositories and knowledge bases have been established to date to ease data sharing. Further, in the past two decades, there has been an exponential increase in the number of datasets added to these dataset repositories. However, most of these repositories are domain-specific, and none of them can recommend datasets to researchers/users. Naturally, it is challenging for a researcher to keep track of all the relevant repositories for potential use. Thus, a dataset recommender system that recommends datasets to a researcher based on previous publications can enhance their productivity and expedite further research. This work adopts an information retrieval (IR) paradigm for dataset recommendation. We hypothesize that two fundamental differences exist between dataset recommendation and PubMed-style biomedical IR beyond the corpus. First, instead of keywords, the query is the researcher, embodied by his or her publications. Second, to filter the relevant datasets from non-relevant ones, researchers are better represented by a set of interests, as opposed to the entire body of their research. This second approach is implemented using a non-parametric clustering technique. These clusters are used to recommend datasets for each researcher using the cosine similarity between the vector representations of publication clusters and datasets. The maximum normalized discounted cumulative gain at 10 (NDCG@10), precision at 10 (p@10) partial and p@10 strict of 0.89, 0.78 and 0.61, respectively, were obtained using the proposed method after manual evaluation by five researchers. As per the best of our knowledge, this is the first study of its kind on content-based dataset recommendation. We hope that this system will further promote data sharing, offset the researchers’ workload in identifying the right dataset and increase the reusability of biomedical datasets. Database URL: http://genestudy.org/recommends/#/

OBJECTIVESubarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH.METHODSThe authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000–2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined.RESULTSIn total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70–2.81) and norepinephrine (OR 2.24, 95% CI 1.80–2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings.CONCLUSIONSIn patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

Objective The centrality of data to biomedical research is difficult to understate, and the same is true for the importance of the biomedical literature in disseminating empirical findings to scientific questions made on such data. But the connections between the literature and related datasets are often weak, hampering the ability of scientists to easily move between existing datasets and existing findings to derive new scientific hypotheses. This work aims to recommend relevant literature articles for datasets with the ultimate goal of increasing the productivity of researchers. Our approach to literature recommendation for datasets is a part of the dataset reusability platform developed at the University Texas Health Science Center at Houston for datasets related to gene expression. This platform incorporates datasets from Gene Expression Omnibus (GEO). An average of 34 datasets were added to GEO daily in the last five years (i.e. 2014 to 2018), demonstrating the need for automatic methods to connect these datasets with relevant literature. The relevant literature for a given dataset may describe that dataset, provide a scientific finding based on that dataset, or even describe prior and related work to the dataset’s topic that is of interest to users of the dataset. Materials and methods We adopt an information retrieval paradigm for literature recommendation. In our experiments, distributional semantic features are created from the title and abstract of MEDLINE articles. Then, related articles are identified for datasets in GEO. We evaluate multiple distributional methods such as TF-IDF, BM25, Latent Semantic Analysis, Latent Dirichlet Allocation, word2vec, and doc2vec. Top similar papers are recommended for each dataset using cosine similarity between the dataset’s vector representation and every paper’s vector representation. We also propose several novel re-ranking and normalization methods over embeddings to improve the recommendations. Results The top-performing literature recommendation technique achieved a strict precision at 10 of 0.8333 and a partial precision at 10 of 0.9000 using BM25 based on a manual evaluation of 36 datasets. Evaluation on a larger, automatically-collected benchmark shows small but consistent gains by emphasizing the similarity of dataset and article titles. Conclusion This work is the first step toward developing a literature recommendation tool by recommending relevant literature for datasets. This will hopefully lead to better data reuse experience.

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae.

Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these models demonstrated promising results on relatively small data sets, the generalizability and transferability of those models and its applicability to different patient populations across hospitals have not been evaluated. In this study, we evaluated an RNN model, RETAIN, over Cerner Health Facts® EMR data, for heart failure onset risk prediction. Our data set included over 150,000 heart failure patients and over 1,000,000 controls from nearly 400 hospitals. Convincingly, RETAIN achieved an AUC of 82% in comparison to an AUC of 79% for logistic regression, demonstrating the power of more expressive deep learning models for EHR predictive modeling. The prediction performance fluctuated across different patient groups and varied from hospital to hospital. Also, we trained RETAIN models on individual hospitals and found that the model can be applied to other hospitals with only about 3.6% of reduction of AUC. Our results demonstrated the capability of RNN for predictive modeling with large and heterogeneous EHR data, and pave the road for future improvements.

The latent HIV-1 reservoir of memory CD4+ T cells that persists during combination antiviral therapy prevents a cure of infection. Insight into mechanisms of latency and viral reactivation are essential for the rational design of strategies to reduce the latent reservoir. In this study, we quantified the levels of >2,600 proteins in the CCL19 primary CD4+ T cell model of HIV-1 latency. We profiled proteins under conditions that promote latent infection and after cells were treated with phorbol 12-myristate 13-acetate (PMA) + ionomycin, which is known to efficiently induce reactivation of latent HIV-1. In an analysis of cells from two healthy blood donors, we identified 61 proteins that were upregulated ≥2-fold, and 36 proteins that were downregulated ≥2-fold under conditions in which latent viruses were reactivated. These differentially expressed proteins are, therefore, candidates for cellular factors that regulate latency or viral reactivation. Two unexpected findings were obtained from the proteomic data: (1) the interactions among the majority of upregulated proteins are largely undetermined in published protein–protein interaction networks and (2) downregulated proteins are strongly associated with Gene Ontology terms related to mitochondrial protein synthesis. This proteomic data set provides a useful resource for future mechanistic studies of HIV-1 latency.

Objectives During the earlier years of the HIV/AIDS epidemic, initial reports described sensorineural hearing loss in up to 49% of individuals with HIV/AIDS. During those years, patients commonly progressed to advanced stages of HIV disease and frequently had neurological complications. However, the abnormalities on pure-tone audiometry and brainstem-evoked responses outlined in small studies were not always consistently correlated with advanced stages of HIV/AIDS. Moreover, these studies could not exclude the confounding effect of concurrent opportunistic infections and syphilis. Additional reports also have indicated that some antiretroviral medications may be ototoxic; thus, it has been difficult to make conclusions regarding the cause of changes in hearing function in HIV-infected patients. More recently, accelerated aging has been suggested as a potential explanation for the disproportionate increase in complications of aging described in many HIV-infected patients; hence, accelerated aging-associated hearing loss may also be playing a role in these patients. Design We conducted a large cross-sectional analysis of hearing function in over 300 patients with HIV-1 infection and in 137 HIV-uninfected controls. HIV-infected participants and HIV-uninfected controls underwent a 2-hr battery of hearing tests including the Hearing Handicap Inventory, standard audiometric pure-tone air and bone conduction testing, tympanometric testing, and speech reception and discrimination testing. Results Three-way analysis of variance (ANOVA) and logistic regression analysis of 278 eligible HIV-infected subjects stratified by disease stage in early HIV disease (n = 127) and late HIV disease (n = 148) and 120 eligible HIV-uninfected controls revealed no statistically significant differences among the three study groups in either overall 4-frequency pure-tone average (4-PTA) or hearing loss prevalence in either ear. Three-way ANOVA showed significant differences in word recognition scores in the right ear among groups, a significant group effect on tympanogram static admittance in both ears and a significant group effect on tympanic gradient in the right ear. There was significantly larger admittance and gradient in controls as compared to the HIV-infected group at late stage of disease. Hearing loss in the HIV-infected groups was associated with increased age and was similar to that described in the literature for the general population. Three-way ANOVA analysis also indicated significantly greater pure-tone thresholds (worse hearing) at low frequencies in HIV patients in the late stage of disease compared with HIV-uninfected controls. This difference was also found by semi-parametric mixed effects models. Conclusions Despite reports of "premature" or "accelerated" aging in HIV-infected subjects, we found no evidence of hearing loss occurring at an earlier age in HIV-infected patients compared to HIV-uninfected controls. Similar to what is described in the general population, the probability of hearing loss increased with age in the HIV-infected subjects and was more common in patients over 60 years of age. Interestingly, HIV-infected subjects had worse hearing at lower frequencies and have significant differences in tympanometry compared to HIV-uninfected controls; these findings deserve further study.

The strategies of structured treatment interruptions (STIs) of antiretroviral therapies have been proposed for clinical management of HIV infected patients, but clinical studies on STIs failed to achieve a consistent conclusion for this strategy. To evaluate the STI strategies, in particular, CD4(+) T cell count-guided STIs, and explain these controversial conclusions from different clinical studies, in this paper we propose to use piecewise HIV virus dynamic models to quantitatively explore the STI strategies and investigate their dynamic behaviors. Our analysis results indicate that CD4(+) T cell counts can be maintained above a safe level using the STI with a single threshold or a threshold window. Numerical simulations show that the CD4(+) T cell counts either fluctuate or approach a stable level for a patient, depending on the prescribed upper or lower threshold values. In particular, the CD4(+) T cell counts can be stabilized at a desired level if the threshold policy control is applied. The durations of drug-on and drug-off are very sensitive to the prescribed upper or lower threshold levels, which possibly explains why the on-off strategy with fixed schedule or an STI strategy with frequent switches are associated with the high rate of failure. Our findings suggest that it is critical to carefully choose the thresholds of CD4(+) T cell count and individualize the STIs for each individual patient based on initial CD4(+) T cell counts.

Although previous studies have found minimal changes in CD4 T cell responses after vaccination of adults with trivalent inactivated influenza vaccine, daily sampling and monitoring of the proliferation marker Ki-67 have now been used to reveal that a substantial fraction of influenza-specific CD4 T cells respond to vaccination. At 4-6 days after vaccination, there is a sharp rise in the numbers of Ki-67-expressing PBMC that produce IFNγ, IL-2 and/or TNFα in vitro in response to influenza vaccine or peptide. Ki-67(+) cell numbers then decline rapidly, and 10 days after vaccination, both Ki-67(+) and overall influenza-specific cell numbers are similar to pre-vaccination levels. These results provide a tool for assessing the quality and quantity of CD4 T cell responses to different influenza vaccines, and raise the possibility that the anti-influenza T cell memory response may be qualitatively altered by vaccination, even if the overall memory cell numbers do not change significantly.

Differential equations are extensively used for modeling dynamics of physical processes in many scientific fields such as engineering, physics, and biomedical sciences. Parameter estimation of differential equation models is a challenging problem because of high computational cost and high-dimensional parameter space. In this paper, we propose a novel class of methods for estimating parameters in ordinary differential equation (ODE) models, which is motivated by HIV dynamics modeling. The new methods exploit the form of numerical discretization algorithms for an ODE solver to formulate estimating equations. First a penalized-spline approach is employed to estimate the state variables and the estimated state variables are then plugged in a discretization formula of an ODE solver to obtain the ODE parameter estimates via a regression approach. We consider three different order of discretization methods, Euler’s method, trapezoidal rule and Runge-Kutta method. A higher order numerical algorithm reduces numerical error in the approximation of the derivative, which produces a more accurate estimate, but its computational cost is higher. To balance the computational cost and estimation accuracy, we demonstrate, via simulation studies, that the trapezoidal discretization-based estimate is the best and is recommended for practical use. The asymptotic properties for the proposed numerical discretization-based estimators (DBE) are established. Comparisons between the proposed methods and existing methods show a clear benefit of the proposed methods in regards to the trade-off between computational cost and estimation accuracy. We apply the proposed methods to an HIV study to further illustrate the usefulness of the proposed approaches.

Viral decay rates during efavirenz-based therapy were compared between human immunodeficiency virus (HIV)-infected patients without tuberculosis (n = 40) and those with tuberculosis coinfection who were receiving concurrent antituberculous therapy (n = 34). Phase I and II viral decay rates were similar in the 2 groups (P > .05). Overall, concurrent antituberculous therapy did not reduce the efficacy of the HIV treatment.

The replication fitness of HIV-1 drug-resistant mutants has been measured using either multiple-cycle or single-cycle assays (MCAs or SCAs); these assays have not been systematically compared. We developed an MCA and an SCA that utilized either intact or env -deleted recombinant viral vectors, respectively, in which virus-infected cells were detected by flow cytometry of a reporter gene product. Fitness was measured using each assay for 11 protease mutants, 9 reverse transcriptase mutants, and two mutants with mutations in gag p6, which is important for the release of virus particles from the cell membrane. In the SCA, fitness (replication capacity [RC]) was defined as the proportion of cells infected by the mutant compared to the wild type 40 h after infection. MCA fitness (1+s) was determined by comparing the changes in the relative proportions of cells infected by the mutant and the wild type between 3 and 5 days after infection. Five protease mutants showed statistically different fitness values by the MCA versus the SCA: the D30N, G48V, I50V, I54L, and I54M mutants. When all the mutants were ranked in order from most to least fit for both assays, 4 protease mutants moved more than 5 positions in rank: the D30N, I54L, I54M, and V82A mutants. There were no significant differences in fitness for the gag p6 or reverse transcriptase mutants. We propose that discordant results in the MCA and SCA are due to alterations in late events in the virus life cycle that are not captured in an SCA, such as burst size, cell-to-cell transmission, or infected-cell life span.

Studies on HIV dynamics in AIDS research are very important in understanding the pathogenesis of HIV-1 infection and also in assessing the effectiveness of antiretroviral (ARV) treatment. Viral dynamic models can be formulated through a system of nonlinear ordinary differential equations (ODE), but there has been only limited development of statistical methodologies for inference. This article, motivated by an AIDS clinical study, discusses a hierarchical Bayesian nonlinear mixed-effects modeling approach to dynamic ODE models without a closed-form solution. In this model, we fully integrate viral load, medication adherence, drug resistance, pharmacokinetics, baseline covariates and time-dependent drug efficacy into the data analysis for characterizing long-term virologic responses. Our method is implemented by a data set from an AIDS clinical study. The results suggest that modeling HIV dynamics and virologic responses with consideration of time-varying clinical factors as well as baseline characteristics may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to ARV treatment and to help the evaluation of clinical trial design in response to existing therapies.

article i nfo We measured the effects of non-nucleoside reverse transcriptase (RT) inhibitor-resistant mutations K101E+ G190S, on replication fitness and EFV-resistance of HIVNL4-3. K101E+G190S reduced fitness in the absence of EFV and increased EFV resistance, compared to either single mutant. Unexpectedly, K101E+G190S also replicated more efficiently in the presence of EFV than in its absence. Addition of the nucleoside resistance mutations L74V or M41L+T215Y to K101E+G190S improved fitness and abolished EFV-dependent stimulation of replication. D10, a clinical RT backbone containing M41L+T215Y and K101E+G190S, also demonstrated EFV-dependent stimulation that was dependent on the presence of K101E. These studies demonstrate that non-nucleoside reverse transcriptase inhibitors can stimulate replication of NNRTI- resistant HIV-1 and that nucleoside-resistant mutants can abolish this stimulation. The ability of EFV to stimulate NNRTI-resistant mutants may contribute to the selection of HIV-1 mutants in vivo. These studies have important implications regarding the treatment of HIV-1 with combination nucleoside and non- nucleoside therapies.

B cell responses after immunization with a drifted H5 influenza/A/Vietnam/1203/04 vaccine were characterized in the peripheral blood of human subjects primed with experimental recombinant H5 influenza A/Hong Kong/156/97 vaccine. Antibody secreting cells were assayed by ELISPOT against a panel of recombinant hemagglutinin and control proteins. Increased frequencies of H5 HA-specific antibody secreting and memory B cells could be observed within 7 days of re-vaccination. Furthermore, these responses were cross-reactive to both H5 HA variants, but not H3 or avian H6 HA strains. These observations suggest prior vaccination against H5 influenza HA induces cellular immune responses that cross-react among drifted variants, without precluding a response to new, or existing HA strains.

The activities of protease inhibitors in vivo may depend on plasma concentrations and viral susceptibility. This nonrandomized, open-label study evaluated the relationship of the inhibitory quotient (IQ [the ratio of drug exposure to viral phenotypic susceptibility]) to the human immunodeficiency virus type 1 (HIV-1) viral load (VL) change for ritonavir-enhanced protease inhibitors (PIs). Subjects on PI-based regimens replaced their PIs with ritonavir-enhanced indinavir (IDV/r) 800/200 mg, fosamprenavir (FPV/r) 700/100 mg, or lopinavir (LPV/r) 400/200 mg twice daily. Pharmacokinetics were assessed at day 14; follow-up lasted 24 weeks. Associations between IQ and VL changes were examined. Fifty-three subjects enrolled, 12 on IDV/r, 33 on FPV/r, and 8 on LPV/r. Median changes (n-fold) (FC) of 50% inhibitory concentrations (IC50s) to the study PI were high. Median 2-week VL changes were −0.7, −0.1, and −1.0 log10for IDV/r, FPV/r, and LPV/r. With FPV/r, correlations between the IQ and the 2-week change in VL were significant (Spearman'srrange, −0.39 to −0.50;P≤ 0.029). The strongest correlation with response to FPV/r was the IC50FC (r= 0.57;P= 0.001), which improved when only adherent subjects were included (r= 0.68;P= 0.001). In multivariable analyses of the FPV/r arm that included FC, one measure of the drug concentration, corresponding IQ, baseline VL, and CD4, the FC to FPV was the only significant predictor of VL decline (P< 0.001). In exploratory analyses of all arms, the area under the concentration-time curve IQ was correlated with the week 2 VL change (r= −0.72;P< 0.001). In conclusion, in PI-experienced subjects with highly resistant HIV-1, short-term VL responses to RTV-enhanced FPV/r correlated best with baseline susceptibility. The IQ improved correlation in analyses of all arms where a greater range of virologic responses was observed.

In this paper, the mechanism-based ordinary differential equation (ODE) model and the flexible semiparametric regression model are employed to identify the significant covariates for antiretroviral response in AIDS clinical trials. We consider the treatment effect as a function of three factors (or covariates) including pharmacokinetics, drug adherence and susceptibility. Both clinical and simulated data examples are given to illustrate these two different kinds of modeling approaches. We found that the ODE model is more powerful to model the mechanism-based nonlinear relationship between treatment effects and virological response biomarkers. The ODE model is also better in identifying the significant factors for virological response, although it is a little bit liberal and there is a trend to include more factors (or covariates) in the model. The semiparametric mixed-effects regression model is very flexible to fit the virological response data, but it is too liberal to identify correct factors for virological response; sometimes it may miss the correct factors. The ODE model is also biologically justifiable and good for predictions and simulations for various biological scenarios. The limitations of the ODE models include the high cost of computation and the requirement of biological assumptions that sometimes may not be easy to validate. The methodologies reviewed in this paper are also generally applicable to studies of other viruses such as hepatitis B virus (HBV) or hepatitis C virus (HCV).

Growth competition assays have been developed to quantify the relative fitness of HIV-1 mutants. In this article, we develop mathematical models to describe viral/cellular dynamic interactions in the assay system from which the competitive fitness indices or parameters are defined. In our previous HIV-viral fitness experiments, the concentration of uninfected target cells was assumed to be constant (Wu et al., 2006). But this may not be true in some experiments. In addition, dual infection may frequently occur in viral fitness experiments and may not be ignorable. Here, we relax these two assumptions and extend our earlier viral fitness model (Wu et al., 2006). The resulting models then become nonlinear ODE systems for which closed-form solutions are not achievable. In the new model, the viral relative fitness is a function of time since it depends on the target cell concentration. First, we studied the structure identifiability of the nonlinear ODE models. The identifiability analysis showed that all parameters in the proposed models are identifiable from the flow-cytometry-based experimental data that we collected. We then employed a global optimization approach (the differential evolution algorithm) to directly estimate the kinetic parameters as well as the relative fitness index in the nonlinear ODE models using nonlinear least square regression based on the experimental data. Practical identifiability was investigated via Monte Carlo simulations.

We use a technique from engineering (Xia and Moog, in IEEE Trans. Autom. Contr. 48(2):330-336, 2003; Jeffrey and Xia, in Tan, W.Y., Wu, H. (Eds.), Deterministic and Stochastic Models of AIDS Epidemics and HIV Infections with Intervention, 2005) to investigate the algebraic identifiability of a popular three-dimensional HIV/AIDS dynamic model containing six unknown parameters. We find that not all six parameters in the model can be identified if only the viral load is measured, instead only four parameters and the product of two parameters (N and lambda) are identifiable. We introduce the concepts of an identification function and an identification equation and propose the multiple time point (MTP) method to form the identification function which is an alternative to the previously developed higher-order derivative (HOD) method (Xia and Moog, in IEEE Trans. Autom. Contr. 48(2):330-336, 2003; Jeffrey and Xia, in Tan, W.Y., Wu, H. (Eds.), Deterministic and Stochastic Models of AIDS Epidemics and HIV Infections with Intervention, 2005). We show that the newly proposed MTP method has advantages over the HOD method in the practical implementation. We also discuss the effect of the initial values of state variables on the identifiability of unknown parameters. We conclude that the initial values of output (observable) variables are part of the data that can be used to estimate the unknown parameters, but the identifiability of unknown parameters is not affected by these initial values if the exact initial values are measured with error. These noisy initial values only increase the estimation error of the unknown parameters. However, having the initial values of the latent (unobservable) state variables exactly known may help to identify more parameters. In order to validate the identifiability results, simulation studies are performed to estimate the unknown parameters and initial values from simulated noisy data. We also apply the proposed methods to a clinical data set to estimate HIV dynamic parameters. Although we have developed the identifiability methods based on an HIV dynamic model, the proposed methodologies are generally applicable to any ordinary differential equation systems.

In many longitudinal studies, the individual characteristics associated with the repeated measures may be possible covariates of the time to an event of interest, and thus, it is desirable to model the time-to-event process and the longitudinal process jointly. Statistical analyses may be further complicated in such studies with missing data such as informative dropouts. This article considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the time-to-event process. We provide a method for simultaneous likelihood inference on the 2 models and allow for nonignorable data missing. The approach is illustrated with a recent AIDS study by jointly modeling HIV viral dynamics and time to viral rebound.

Background A recent study found nonweightbearing stretching exercises specific to the plantar fascia to be superior to the standard program of weightbearing Achilles tendon-stretching exercises in patients with chronic plantar fasciitis. The present study used a cadaver model to demonstrate the influence of foot and ankle position on stretching of the plantar fascia. Methods Twelve fresh-frozen lower-leg specimens were tested in 15 different configurations representing various combinations of ankle and metatarsophalangeal (MTP) joint dorsiflexion, midtarsal transverse plane abduction and adduction, and forefoot varus and valgus. Measurements were recorded by a differential variable reluctance transducer (DVRT) implanted into the medial band of the plantar fascia, and primary measurement was a percent deformation of the plantar fascia (stretch) with respect to a reference position (90 degrees ankle dorsiflexion, 0 degrees midtarsal and forefoot orientation, and 0 degrees MTP dorsiflexion). Results Ankle and MTP joint dorsiflexion produced a significant increase (14.91%) in stretch compared to the position of either ankle dorsiflexion alone (9.31% increase, p < 0.001) or MTP dorsiflexion alone (7.33% increase, p < 0.01). There was no significant increase in stretch with positions of abduction or varus (2.49%, p = 0.27 and 0.55%, p = 0.79). Conclusion This study provides a mechanical explanation for enhanced outcomes in recent clinical trials using plantar fascia tissue-specific stretching exercises and lends support to the use of ankle and MTP joint dorsiflexion when employing stretching protocols for nonoperative treatment in patients with chronic proximal plantar fasciitis.

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org.

The kinetics and frequency of the primary response to human smallpox vaccine was compared to that among subjects re-vaccinated within the last 2 years, or after several decades. Vaccination induced local and systemic reactions that were mildest in the distant cohort and more severe in the primary vaccinees. The timing of IFN-gamma responses was similar in all three groups, but of higher frequency in the primary vaccinees. Responses to vaccinia re-immunization between those immunized2 years ago and those immunized10 years ago were not significantly different. Neutralizing antibodies were boosted in all groups, with the highest titers observed among the distant cohort. However, the antibody and IFN-gamma responses did not correlate strongly with the local reactions at the vaccine site. This suggests that local immune events in the tissue are distinct from the parameters measured in the peripheral blood.

We developed a novel HIV-1 dynamic model with consideration of pharmacokinetics, drug adherence and drug susceptibility to link plasma drug concentration to the long-term changes in HIV-1 RNA observation after initiation of therapy. A Bayesian approach is proposed to fit this model to clinical data from ACTG A5055, a study of two dosage regimens of indinavir (IDV) with ritonavir (RTV) in subjects failing their first protease inhibitor treatment. The HIV RNA testing was completed at days 0, 7, 14, 28, 56, 84, 112, 140, and 168. An intensive pharmacokinetic (PK) evaluation was performed on day 14 and multiple trough concentrations were subsequently collected. Pill counts were used to monitor adherence. IC(50) for IDV and RTV were determined at baseline and at virologic failure. Viral dynamic model fitting residuals were used to assess the significance of covariate effects on long-term virologic response. As univariate predictors, none of the four PK parameters C(trough), C(12 hour), C(max), and AUC was significantly related to virologic response (p0.05). By including drug susceptibility (IC(50)), or IC(50) and adherence measured by pill counts together, C(trough), C(12 hour), C(max) and AUC were each significantly correlated to long-term virologic response (p = 0.0055,0.0002,0.0136,0.0002 with IC(50) and adherence measured by pill counts considered). The IC(50) and adherence measured by pill counts alone were not related to the virologic response. In predicting virologic response adherence measured by pill counts did not provide any additional information to PK parameters (p = 0.064), to drug susceptibility IC(50) (p = 0.086), and to their combination (p = 0.22). Simple regression approaches did not detect any significant pharmacodynamic (PD) relationships. Any single factor of PK, adherence measured by pill counts and drug susceptibility did not contribute to long-term virologic response. But their combinations in viral dynamic modeling significantly predicted virologic response. The HIV dynamic modeling can appropriately capture complicated nonlinear relationships and interactions among multiple covariates.

HIV dynamics studies have significantly contributed to the understanding of HIV infection and antiviral treatment strategies. But most studies are limited to short-term viral dynamics due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. In this article, a mechanism-based dynamic model is proposed for characterizing long-term viral dynamics with antiretroviral therapy, described by a set of nonlinear differential equations without closed-form solutions. In this model we directly incorporate drug concentration, adherence, and drug susceptibility into a function of treatment efficacy, defined as an inhibition rate of virus replication. We investigate a Bayesian approach under the framework of hierarchical Bayesian (mixed-effects) models for estimating unknown dynamic parameters. In particular, interest focuses on estimating individual dynamic parameters. The proposed methods not only help to alleviate the difficulty in parameter identifiability, but also flexibly deal with sparse and unbalanced longitudinal data from individual subjects. For illustration purposes, we present one simulation example to implement the proposed approach and apply the methodology to a data set from an AIDS clinical trial. The basic concept of the longitudinal HIV dynamic systems and the proposed methodologies are generally applicable to any other biomedical dynamic systems.

Pharmacokinetic studies were conducted with human immunodeficiency virus-infected patients receiving efavirenz, nelfinavir, or both agents at weeks 4 and 32. Reductions of 25% and 45% were observed in the mean nelfinavir area under the concentration-time curve and minimum concentration of the drug in serum, and there was a 31% more rapid half-life for patients receiving both drugs compared to patients receiving nelfinavir alone. There were no significant differences in efavirenz pharmacokinetics.

Studies of HIV dynamics in AIDS research are very important for understanding pathogenesis of HIV infection and for assessing the potency of antiviral therapies. Since the viral dynamic results from clinical data were first published by Ho et al. and Wei et al., the study of HIV-1 dynamics in vivo has drawn a great attention from AIDS clinicians and researchers. Although the important findings from HIV dynamic studies have been published in many prestigious scientific journals, statistical methods for estimating viral dynamic parameters have not been paid enough attention by HIV dynamic investigators. The estimation methods in many viral dynamic studies are very crude and inefficient. In this paper, we review the statistical methods and mathematical models for HIV dynamic data analysis developed in recent years. We also address some practical issues and share our experiences in the design and analysis of viral dynamic studies. Some principles and guidelines for the design and analysis of viral dynamic studies are provided. The methodologies reviewed in this paper are also applicable to studies of other viruses such as hepatitis B virus or hepatitis C virus. We also pose some challenging statistical problems in this area in order to stimulate further study by the statistical research community.

Highly active antiretroviral therapies consisting of reverse transcriptase inhibitor drugs and protease inhibitor drugs, which can rapidly suppress HIV below the limit of detection, are currently the most effective treatment for HIV infected patients. In spite of this, many patients fail to achieve viral suppression, probably due to existing or developing drug resistance, poor adherence, pharmacokinetic problems and other clinical factors. In this paper, we develop a viral dynamic model to evaluate how time-varying drug exposure and drug susceptibility affect antiviral response. Plasma concentrations, in turn, are modeled using a standard pharmacokinetic (PK) one-compartment open model with first order absorption and elimination as a function of fixed individual PK parameters and dose times. Imperfect adherence is considered as missed doses in PK models. We discuss the analytic properties of the viral dynamic model and study how time-varying treatment efficacies affect antiviral responses, specifically viral load and T cell counts. The relationship between actual failure time (the time at which the viral growth rate changes from negative to positive) and detectable failure time (the time at which viral load rebounds to above the limit of detection) is investigated. We find that an approximately linear relationship can be used to estimate the actual rebound failure time from the detectable rebound failure time. In addition, the effect of adherence on antiviral response is studied. In particular, we examine how different patterns of adherence affect antiviral response. Results suggest that longer sequences of missed doses increase the chance of treatment failure and accelerate the failure. Simulation experiments are presented to illustrate the relationship between antiviral response and pharmacokinetics, time-varying adherence and drug resistance. The proposed models and methods may be useful in AIDS clinical trial simulations.

From a study of 71 HIV-1-infected patients receiving abacavir in combination with 1 of 5 different HIV-1 protease inhibitors (indinavir, ritonavir, saquinavir, nelfinavir, or amprenavir), we found that the baseline HIV-1 RNA levels were highly predictive of the viral decay rates. The baseline HIV-1 RNA levels were negatively correlated with the first phase viral decay rates (r = -0.77, P < 0.001) and positively correlated with the second phase viral decay rates (r = 0.68, P < 0.001). In addition, the first phase viral decay rate was positively correlated with CD4+ cell increases. No significant correlation was found between viral decay rates and longer term (24 weeks) virologic responses, and no difference in viral decay rates was found among the 5 study regimens. These data suggest that the potency of the 5 treatment regimens was similar and was not predictive of long-term virologic failure.

Adult AIDS Clinical Trials Group (AACTG) Protocol 886 examined the dispositions of indinavir, efavirenz, and abacavir in human immunodeficiency virus-infected subjects who received indinavir at 1,000 mg every 8 h (q8h) and efavirenz at 600 mg q24h or indinavir at 1,200 mg and efavirenz at 300 mg q12h with or without abacavir 300 at mg q12h. Thirty-six subjects participated. The median minimum concentration in plasma ( C min ) for indinavir administered at 1,200 mg q12h was 88.1 nM (interquartile range [IR], 61.7 to 116.5 nM), whereas the median C min for indinavir administered at 1,000 mg q8h was 139.3 nM (IR, 68.8 to 308.7 nM) ( P = 0.19). Compared to the minimum C min range for wild-type virus (80 to 120 ng/ml) estimated by the AACTG Adult Pharmacology Committee, the C min for indinavir administered at 1,200 mg q12h (54 ng/ml) is inadequate. The apparent oral clearance (CL/F) ( P = 0.28), apparent volume of distribution at steady state ( V ss /F) ( P = 0.25), and half-life ( t 1/2 ) ( P = 0.80) of indinavir did not differ between regimens. The levels of efavirenz exposure were similar between regimens. For efavirenz administered at 600 mg q24h and 300 mg q12h, the median maximum concentrations in plasma ( C max s) were 8,968 nM (IR, 5,784 to 11,768 nM) and 8,317 nM (6,587 to 10,239 nM), respectively ( P = 0.66), and the C min s were 4,289 nM (IR, 2,462 to 5,904 nM) and 4,757 nM (IR, 3,088 to 6,644 nM), respectively ( P = 0.29). Efavirenz pharmacokinetic parameters such as CL/F ( P = 0.62), V ss /F ( P = 0.33), and t 1/2 ( P = 0.37) were similar regardless of the dosing regimen. The median C max , C min , CL/F, V ss / F , and t 1/2 for abacavir were 6,852 nM (IR, 5,702 to 7,532), 21.0 nM (IR, 21.0 to 87.5), 43.7 liters/h (IR, 37.9 to 55.2), 153.9 liters (IR, 79.6 to 164.4), and 2.0 h (IR, 1.8 to 2.8), respectively. In summary, when indinavir was given with efavirenz, the trough concentration of indinavir after administration of 1,200 mg q12h was inadequate. Abacavir did not influence the pharmacokinetics or exposure parameters of either indinavir or efavirenz. The levels of efavirenz exposure were similar in subjects receiving efavirenz q12h or q24h.

Background Combined administration of the human immunodeficiency virus protease inhibitor indinavir (800 mg every 8 hours) with the antimycobacterial rifabutin (300 mg daily) results in a significant decrease in indinavir concentrations with subsequent risk of treatment failure, as well as a significant increase in rifabutin concentrations with increased toxicity. Therefore this study was designed to evaluate alternative dosing regimens. Methods Eighteen healthy volunteers received 300 mg rifabutin daily alone for 14 days and then 1000 mg indinavir every 8 hours plus rifabutin at a reduced dose of 150 mg daily, given at 8 AM or noon in a randomized crossover sequence for 14 days. Ten human immunodeficiency virus-infected subjects received 800 mg indinavir every 8 hours for 14 days and then 1000 mg indinavir every 8 hours plus 150 mg rifabutin daily at 8 AM for 14 days. Twenty-four-hour pharmacokinetic sampling was performed at the end of each 14-day study period. Results Indinavir, 1000 mg every 8 hours, coadministered with 150 mg rifabutin daily produced an area under the concentration-time curve similar to that of 800 mg indinavir every 8 hours. The mean area under the concentration-time curve values of rifabutin and 25-desacetyl rifabutin, when 150 mg rifabutin every morning was coadministered simultaneously with 1000 mg indinavir every 8 hours, were 70% and 120% higher than with 300 mg rifabutin daily alone. Drug concentrations were not different when rifabutin and indinavir were administered simultaneously at 8 AM or staggered by 4 hours. Conclusions Increasing indinavir's dose to 1000 mg every 8 hours when coadministered with rifabutin at a reduced dose of 150 mg daily compensates for rifabutin induction of indinavir metabolism. Rifabutin concentrations were still higher than with rifabutin alone despite a 50% reduction of rifabutin dose, which is the current recommendation when these 2 drugs are combined. The clinical significance of the increase in rifabutin and 25-desacetyl rifabutin concentrations is not known. Clinical Pharmacology & Therapeutics (2003) 73, 159–169; doi: 10.1067/mcp.2003.3

Medical researchers are increasingly interested in knowing how the complex community of micro-organisms living on human body impacts human health. Key to this is to understand how the microbes interact with each other. Time-course studies on human microbiome indicate that the composition of microbiome changes over short time periods, primarily as a consequence of synergistic and antagonistic interactions of the members of the microbiome with each other and with the environment. Knowledge of the abundance of bacteria-which are the predominant members of the human microbiome-in such time-course studies along with appropriate mathematical models will allow us to identify key dynamic interaction networks within the microbiome. However, the high-dimensional nature of these data poses significant challenges to the development of such mathematical models. We propose a high-dimensional linear State Space Model (SSM) with a new Expectation-Regularization-Maximization (ERM) algorithm to construct a dynamic Microbial Interaction Network (MIN). System noise and measurement noise can be separately specified through SSMs. In order to deal with the problem of high-dimensional parameter space in the SSMs, the proposed new ERM algorithm employs the idea of the adaptive LASSO-based variable selection method so that the sparsity property of MINs can be preserved. We performed simulation studies to evaluate the proposed ERM algorithm for variable selection. The proposed method is applied to identify the dynamic MIN from a time-course vaginal microbiome study of women. This method is amenable to future developments, which may include interactions between microbes and the environment.

Non-linear mixed-effects models are powerful tools for modelling HIV viral dynamics. In AIDS clinical trials, the viral load measurements for each subject are often sparse. In such cases, linearization procedures are usually used for inferences. Under such linearization procedures, however, standard covariate selection methods based on the approximate likelihood, such as the likelihood ratio test, may not be reliable. In order to identify significant host factors for HIV dynamics, in this paper we consider two alternative approaches for covariate selection: one is based on individual non-linear least square estimates and the other is based on individual empirical Bayes estimates. Our simulation study shows that, if the within-individual data are sparse and the between-individual variation is large, the two alternative covariate selection methods are more reliable than the likelihood ratio test, and the more powerful method based on individual empirical Bayes estimates is especially preferable. We also consider the missing data in covariates. The commonly used missing data methods may lead to misleading results. We recommend a multiple imputation method to handle missing covariates. A real data set from an AIDS clinical trial is analysed based on various covariate selection methods and missing data methods.

Background Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks. Results Here we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs. Conclusion Compared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.

Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

We propose a three-step multiple imputation method, implemented by Gibbs sampler, for estimating parameters in non-linear mixed-effects models with missing covariates. Estimates obtained by the proposed multiple imputation method are compared to those obtained by the mean-value imputation method and the complete-case method through simulations. We find that the proposed multiple imputation method offers smaller biases and smaller mean-squared errors for the estimates of covariate coefficients compared to other two methods. We apply the three missing data methods to modelling HIV viral dynamics from an AIDS clinical trial. We believe that the results from the proposed multiple imputation method are more reliable than that from the other two commonly used methods.

Summary. The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. But, although important findings in HIV dynamics have been published in prestigious scientific journals in the last 5 years, the model-fitting procedures used in these publications have not been studied in any detail. In this paper, we evaluate the performance of four model-fitting procedures proposed and used in biphasic HIV dynamic data analysis via extensive Monte Carlo simulations. We propose some guidelines for practitioners to select an appropriate method for their own data analysis. Real data examples from an AIDS clinical trial are provided as illustrations.

Recently, potent combination antiviral therapies consisting of reverse transcriptase inhibitor (RTI) drugs and protease inhibitor (PI) drugs, have been developed which can rapidly suppress HIV below the limit of detection. Two phases of plasma viral decay after initiation of treatment have been observed in clinical studies. Some researchers have suggested that the viral decay rates may reflect the potency (efficacy) of antiviral therapies. In this paper we model the effect of RTI drugs and PI drugs as inhibition rates of cell infection and infectious virus production, respectively, based on the biological mechanisms of these two different types of drugs. Through rigorous mathematical derivation, we show that the two viral decay rates are monotone functions of the treatment effects of these antiviral therapies. We derive approximation formulas for the relationships between viral decay rates and treatment effects. Computer simulations show that the approximation formulas approximate the true values very well. These formulas may be used to study what factors really affect the viral decay rates. The results in this paper provide a theoretical justification for using both viral decay rates for evaluation of the treatment efficacy of antiviral therapies.

In this paper, we introduce a novel application of hierarchical nonlinear mixed-effect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV-1 dynamics (HIV-1 RNA copies) after initiation of potent antiviral treatments and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixed-effect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV-1 RNA measurements and demonstrate the potential use of the models in clinical decision-making.

Biphasic plasma viral decays were modeled in 48 patients treated with ritonavir, zidovudine, and lamivudine. Estimated first- and second-phase decay rates were d 1 as 0.47/day and d 2 as 0.04/day. Interpatient differences in both decay rates were significant. The d 1 was directly correlated with baseline CD4 + , CD4 + CD28 + , and CD8 + CD28 + T lymphocyte counts (P< .05) and inversely correlated with baseline virus load (P =.044) and the magnitude of CD4 + and CD8 + T lymphocyte recovery (P

To identify sources of inter-subject variation in vaccine responses, we performed high-frequency sampling of human peripheral blood cells post-vaccination, followed by a novel systems biology analysis. Functional principal component analysis was used to examine time varying B cell vaccine responses. In subjects vaccinated within the previous three years, 90% of transcriptome variation was explained by a single subject-specific mathematical pattern. Within individual vaccine response patterns, a common subset of 742 genes was strongly correlated with migrating plasma cells. Of these, 366 genes were associated with human plasmablasts differentiating in vitro. Additionally, subject-specific temporal transcriptome patterns in peripheral blood mononuclear cells identified migration of myeloid/dendritic cell lineage cells one day after vaccination. Upstream analyses of transcriptome changes suggested both shared and subject-specific transcription groups underlying larger patterns. With robust statistical methods, time-varying response characteristics of individual subjects were effectively captured along with a shared plasma cell gene signature.

The ELISPOT assay is often used for cell count determination in immunological studies. Automated methods are needed to estimate cell concentrations from spot counts obtained from the assay. Three major distributions are assumed for observational cell counts. For each assumed distribution, individual least squares (LS)/ maximum likelihood and/or individual robust least squares (RLS) are applied for parameter estimation. Distributions of study endpoints (derived variables), defined as percentages of antigen-specific memory cell per total immunoglobulin G (IgG), are investigated to provide a basis for hypothesis testing. We show, under some weak conditions, that the distribution of endpoint estimates across subjects is approximately the same within a group. Thus, the t -test or the Wilcoxon Rank Sum test can be applied to detect group differences. These methods are compared through simulations and application to real data.