A Statistically Rigorous Method for Determining Antigenic Switching Networks

Many vector-borne pathogens rely on antigenic variation to prolong infections and increase their likelihood of onward transmission. This immune evasion strategy often involves mutually exclusive switching between members of gene families that encode functionally similar but antigenically different variants during the course of a single infection. Studies of different pathogens have suggested that switching between variant genes is non-random and that genes have intrinsic probabilities of being activated or silenced. These factors could create a hierarchy of gene expression with important implications for both infection dynamics and the acquisition of protective immunity. Inferring complete switching networks from gene transcription data is problematic, however, because of the high dimensionality of the system and uncertainty in the data. Here we present a statistically rigorous method for analysing temporal gene transcription data to reconstruct an underlying switching network. Using artificially generated transcription profiles together with in vitro var gene transcript data from two Plasmodium falciparum laboratory strains, we show that instead of relying on data from long-term parasite cultures, accuracy can be greatly improved by using transcription time courses of several parasite populations from the same isolate, each starting with different variant distributions. The method further provides explicit indications about the reliability of the resulting networks and can thus be used to test competing hypotheses with regards to the underlying switching pathways. Our results demonstrate that antigenic switch pathways can be determined reliably from short gene transcription profiles assessing multiple time points, even when subject to moderate levels of experimental error. This should yield important new information about switching patterns in antigenically variable organisms and might help to shed light on the molecular basis of antigenic variation

Validation of two generic patient-reported outcome measures in patients with type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Prior to using a generic patient-reported outcome measure (PRO), the measure should be validated within the target population. The purpose of the current study was to validate two generic measures in patients with type 2 diabetes.</p> <p>Methods</p> <p>Patients with type 2 diabetes in Scotland and England completed two generic measures: EQ-5D and Psychological General Well-Being Index (PGWB). Two diabetes-specific measures were administered: ADS and DSC-R. Analyses assessed reliability and validity.</p> <p>Results</p> <p>There were 130 participants (53 Scotland; 77 England; 64% male; mean age = 55.7 years). Responses on the EQ-5D and PGWB reflected moderate impairment consistent with previous diabetes samples: mean EQ-5D Index score, 0.75; EQ-5D VAS, 68.8; PGWB global score, 67.9. All scales of the PGWB demonstrated good internal consistency reliability (Cronbach's alpha = 0.77 to 0.97). The EQ-5D and PGWB demonstrated convergent validity through significant correlations with the ADS (r = 0.48 to 0.61), DSC-R scales (r = 0.33 to 0.81 except ophthalmology subscale), and Body Mass Index (r = 0.15 to 0.38). The EQ-5D and PGWB discriminated between groups of patients known to differ in diabetes-related characteristics (e.g., history of hypoglycemia).</p> <p>Conclusion</p> <p>Results support the use of the EQ-5D and PGWB among patients with type 2 diabetes, possibly in combination with condition-specific measures.</p

Diagnosis and management of unusual dental abscesses in children

Although the majority of dental abscesses in children originate from dental caries or trauma, a few are associated with unusual conditions which challenge diagnosis and management. Recent research findings have shed light on these unusual entities and greatly improved understanding of their clinical implications. These conditions include developmental abnormalities such as dens invaginatus in which there is an invagination of dental tissues into the pulp chamber and dens evaginatus in which a tubercle containing pulp is found on the external surface of a tooth crown. In addition, inherited conditions which show abnormal dentine such as dentine dysplasia, dentinogenesis imperfecta, and osteogenesis imperfecta predispose the dentition to abscess formation. Furthermore, 'spontaneous' dental abscesses are frequently encountered in familial hypophosphataemia, also known as vitamin D-resistant rickets, in which there is hypomineralization of dentine and enlargement of the pulp. In addition to developmental conditions, there are also acquired conditions which may cause unusual dental abscesses,. These include pre-eruptive intracoronal resorption which was previously known as 'pre-eruptive caries' or the 'fluoride bomb'. In addition, some undiagnosed infections associated with developing teeth are now thought to be the mandibular infected buccal cysts which originate from infection of the developing dental follicles. In the present paper, these relatively unknown entities Which cause unusual abscesses in children are reviewed with the aim of updating the general practitioner in their diagnosis and management

A hybrid Bayesian hierarchical model combining cohort and case–control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented