13,648 research outputs found
Using bacterial biomarkers to identify early indicators of cystic fibrosis pulmonary exacerbation onset
Acute periods of pulmonary exacerbation are the single most important cause of morbidity in cystic fibrosis patients, and may be associated with a loss of lung function. Intervening prior to the onset of a substantially increased inflammatory response may limit the associated damage to the airways. While a number of biomarker assays based on inflammatory markers have been developed, providing useful and important measures of disease during these periods, such factors are typically only elevated once the process of exacerbation has been initiated. Identifying biomarkers that can predict the onset of pulmonary exacerbation at an early stage would provide an opportunity to intervene before the establishment of a substantial immune response, with major implications for the advancement of cystic fibrosis care. The precise triggers of pulmonary exacerbation remain to be determined; however, the majority of models relate to the activity of microbes present in the patient's lower airways of cystic fibrosis. Advances in diagnostic microbiology now allow for the examination of these complex systems at a level likely to identify factors on which biomarker assays can be based. In this article, we discuss key considerations in the design and testing of assays that could predict pulmonary exacerbations
Tuberculosis diagnostics and biomarkers: needs, challenges, recent advances, and opportunities
Tuberculosis is unique among the major infectious diseases in that it lacks accurate rapid point-of-care diagnostic tests. Failure to control the spread of tuberculosis is largely due to our inability to detect and treat all infectious cases of pulmonary tuberculosis in a timely fashion, allowing continued Mycobacterium tuberculosis transmission within communities. Currently recommended gold-standard diagnostic tests for tuberculosis are laboratory based, and multiple investigations may be necessary over a period of weeks or months before a diagnosis is made. Several new diagnostic tests have recently become available for detecting active tuberculosis disease, screening for latent M. tuberculosis infection, and identifying drug-resistant strains of M. tuberculosis. However, progress toward a robust point-of-care test has been limited, and novel biomarker discovery remains challenging. In the absence of effective prevention strategies, high rates of early case detection and subsequent cure are required for global tuberculosis control. Early case detection is dependent on test accuracy, accessibility, cost, and complexity, but also depends on the political will and funder investment to deliver optimal, sustainable care to those worst affected by the tuberculosis and human immunodeficiency virus epidemics. This review highlights unanswered questions, challenges, recent advances, unresolved operational and technical issues, needs, and opportunities related to tuberculosis diagnostics
General Design Bayesian Generalized Linear Mixed Models
Linear mixed models are able to handle an extraordinary range of
complications in regression-type analyses. Their most common use is to account
for within-subject correlation in longitudinal data analysis. They are also the
standard vehicle for smoothing spatial count data. However, when treated in
full generality, mixed models can also handle spline-type smoothing and closely
approximate kriging. This allows for nonparametric regression models (e.g.,
additive models and varying coefficient models) to be handled within the mixed
model framework. The key is to allow the random effects design matrix to have
general structure; hence our label general design. For continuous response
data, particularly when Gaussianity of the response is reasonably assumed,
computation is now quite mature and supported by the R, SAS and S-PLUS
packages. Such is not the case for binary and count responses, where
generalized linear mixed models (GLMMs) are required, but are hindered by the
presence of intractable multivariate integrals. Software known to us supports
special cases of the GLMM (e.g., PROC NLMIXED in SAS or glmmML in R) or relies
on the sometimes crude Laplace-type approximation of integrals (e.g., the SAS
macro glimmix or glmmPQL in R). This paper describes the fitting of general
design generalized linear mixed models. A Bayesian approach is taken and Markov
chain Monte Carlo (MCMC) is used for estimation and inference. In this
generalized setting, MCMC requires sampling from nonstandard distributions. In
this article, we demonstrate that the MCMC package WinBUGS facilitates sound
fitting of general design Bayesian generalized linear mixed models in practice.Comment: Published at http://dx.doi.org/10.1214/088342306000000015 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Biomarkers of Tuberculosis Severity and Treatment Effect: A Directed Screen of 70 Host Markers in a Randomized Clinical Trial.
More efficacious treatment regimens are needed for tuberculosis, however, drug development is impeded by a lack of reliable biomarkers of disease severity and of treatment effect. We conducted a directed screen of host biomarkers in participants enrolled in a tuberculosis clinical trial to address this need. Serum samples from 319 protocol-correct, culture-confirmed pulmonary tuberculosis patients treated under direct observation as part of an international, phase 2 trial were screened for 70 markers of infection, inflammation, and metabolism. Biomarker assays were specifically developed for this study and quantified using a novel, multiplexed electrochemiluminescence assay. We evaluated the association of biomarkers with baseline characteristics, as well as with detailed microbiologic data, using Bonferroni-adjusted, linear regression models. Across numerous analyses, seven proteins, SAA1, PCT, IL-1β, IL-6, CRP, PTX-3 and MMP-8, showed recurring strong associations with markers of baseline disease severity, smear grade and cavitation; were strongly modulated by tuberculosis treatment; and had responses that were greater for patients who culture-converted at 8weeks. With treatment, all proteins decreased, except for osteocalcin, MCP-1 and MCP-4, which significantly increased. Several previously reported putative tuberculosis-associated biomarkers (HOMX1, neopterin, and cathelicidin) were not significantly associated with treatment response. In conclusion, across a geographically diverse and large population of tuberculosis patients enrolled in a clinical trial, several previously reported putative biomarkers were not significantly associated with treatment response, however, seven proteins had recurring strong associations with baseline radiographic and microbiologic measures of disease severity, as well as with early treatment response, deserving additional study
Automated sample preparation for streamlined proteomic profiling of clinical specimens
The genetic information of all life is encoded within DNA molecules that are translated into functional entities, so-called proteins. They are responsible for operating and controlling a vast array of molecular mechanisms in any biological system and ubiquitous in
(patho)physiology as a result. Besides, proteins are the primary target of drugs and can
have a central role as biomarkers for diagnostic, prognostic, or predictive purposes. Here,
many regulatory mechanisms and spatiotemporal influences prevent an accurate
prediction of a proteins’ abundance and its associated functionality based on the genome
information alone. Nowadays, it has become possible to measure and quantify thousands
of proteins simultaneously, however, involving comprehensive sample preparation
procedures. Currently, no universally standardized method enables a routine application of
proteome profiling in a clinical environment.
In this thesis, an automated workflow for the efficient processing of the most common and
quantity-limited specimens is described. In order to demonstrate the usefulness of the end-to-
end pipeline, which was termed autoSP3, it was applied to the proteome profiling of
histologically defined and WHO recognized growth patterns of pulmonary adenocarcinoma
(ADC) that currently have a limited clinical implication. Secondly, we investigated the
proteome composition of a molecularly well-defined cohort of Ependymoma (EPN)
pediatric brain tumors. Despite the availability of substantial NGS data and their ability to
differentiate nine distinct subgroups, the majority of tumors remained without a functional
insight. Here, the proteome profiling could provide a missing link and emphasize several
subgroup-specific protein targets.
In summary, this thesis describes the optimization of SP3 and its automation into a robust
and cost-efficient pipeline for quantity-limited sample preparation and biological insight
into the proteome composition of ADC growth patterns and EPN tumor subgroups
The Tumor Cytosol miRNAs, Fluid miRNAs, and Exosome miRNAs in Lung Cancer.
The focus of this review is to provide an update on the progress of microRNAs (miRNAs) as potential biomarkers for lung cancer. miRNAs are single-stranded, small non-coding RNAs that regulate gene expression and show tissue-specific signatures. Accumulating evidence indicates that miRNA expression patterns represent the in vivo status in physiology and disease. Moreover, miRNAs are stable in serum and other clinically convenient and available tissue sources, so they are being developed as biomarkers for cancer and other diseases. Cancer is currently the primary driver of the field, but miRNA biomarkers are being developed for many other diseases such as cardiovascular and central nervous system diseases. Here, we examine the framework and scope of the miRNA landscape as it specifically relates to the translation of miRNA expression patterns/signatures into biomarkers for developing diagnostics for lung cancer. We focus on examining tumor cytosol miRNAs, fluid miRNAs, and exosome miRNAs in lung cancer, the connections among these miRNAs, and the potential of miRNA biomarkers for the development of diagnostics. In lung cancer, miRNAs have been studied in both cell populations and in the circulation. However, a major challenge is to develop biomarkers to monitor cancer development and to identify circulating miRNAs that are linked to cancer stage. Importantly, the fact that miRNAs can be successfully harvested from biological fluids allows for the development of biofluid biopsies, in which miRNAs as circulating biomarkers can be captured and analyzed ex vivo. Our hope is that these minimally invasive entities provide a window to the in vivo milieu of the patients without the need for costly, complex invasive procedures, rapidly moving miRNAs from research to the clinic
Respiratory Syncytial Virus
RSV infection has an estimated global incidence of 33 million cases in children <5 years of age, with 10% requiring hospital admission and up to 199 000 dying of the disease. There is growing evidence that severe infantile RSV bronchiolitis, a condition characterised by an inflammatory reaction to the virus, is associated with later childhood wheeze in some vulnerable children; however, a direct causal relationship with asthma has not yet been established. RSV infection is also increasingly recognised as a cause of morbidity and mortality in those with underlying airway disease, the immunocompromised and frail elderly persons. Novel molecular-based diagnostic tools are becoming established, but treatment remains largely supportive, with palivizumab the only licensed agent currently available for passive prophylaxis of selected pre-term infants. While effective treatments remain elusive, there is optimism about the testing of novel antiviral drugs and the development of vaccines that may induce long-lasting immunity without the risk of disease augmentation
Negative Effect of Smoking on the Performance of the QuantiFERON TB Gold in Tube Test.
False negative and indeterminate Interferon Gamma Release Assay (IGRA) results are a well documented problem. Cigarette smoking is known to increase the risk of tuberculosis (TB) and to impair Interferon-gamma (IFN-γ) responses to antigenic challenge, but the impact of smoking on IGRA performance is not known. The aim of this study was to evaluate the effect of smoking on IGRA performance in TB patients in a low and high TB prevalence setting respectively. Patients with confirmed TB from Denmark (DK, n = 34; 20 smokers) and Tanzania (TZ, n = 172; 23 smokers) were tested with the QuantiFERON-TB Gold In tube (QFT). Median IFN-γ level in smokers and non smokers were compared and smoking was analysed as a risk factor for false negative and indeterminate QFT results. Smokers from both DK and TZ had lower IFN-γ antigen responses (median 0.9 vs. 4.2 IU/ml, p = 0.04 and 0.4 vs. 1.6, p < 0.01), less positive (50 vs. 86%, p = 0.03 and 48 vs. 75%, p < 0.01) and more false negative (45 vs. 0%, p < 0.01 and 26 vs. 11%, p = 0.04) QFT results. In Tanzanian patients, logistic regression analysis adjusted for sex, age, HIV and alcohol consumption showed an association of smoking with false negative (OR 17.1, CI: 3.0-99.1, p < 0.01) and indeterminate QFT results (OR 5.1, CI: 1.2-21.3, p = 0.02). Cigarette smoking was associated with false negative and indeterminate IGRA results in both a high and a low TB endemic setting independent of HIV status
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