Postobturation Pain Associated with Tricalcium Silicate and Resin-based Sealer Techniques: A Randomized Clinical Trial

IntroductionThe incidence of immediate postobturation pain associated with 2 sealer techniques was compared and potential prognostic factors identified.MethodsPatients referred for endodontic treatment were recruited with informed consent. Root canals were debrided and teeth rendered asymptomatic before random allocation to receive TotalFill BC (FKG Dentaire SA, La Chaux-de-Fonds, Switzerland) or AH Plus sealer (Dentsply Maillefer, Ballaigues, Switzerland). Patients blinded to the sealer reported their postobturation pain experience 1, 3, and 7 days after treatment. Blinded and calibrated assessors independently reviewed treatment quality, sealer extrusion, and radiographic data under standardized conditions.ResultsOne hundred sixty eligible patients (163 teeth, 95.3%) returned their pain diary. No postobturation pain difference was found between the 2 sealers (P > .05), although the AH Plus sealer technique was significantly associated with extrusion beyond the apex (P < .05; odds ratio [OR] = 3.02; 95% confidence interval [CI], 1.39–6.57). Thirty-three (20.6%) patients reported pain on day 1 (median 1 = very mild pain), 16 (10.0%) on day 3 (median 1 = very mild pain), and 9 (5.6%) on day 7 (median 2 = mild pain). The prognostic factors were as follows:(1) moderate/severe preoperative pain (OR = 4.41; 95% CI, 1.42–13.76 on day 3 and OR = 5.16; 95% CI, 1.17–22.78 on day 7),(2) provoked preoperative pain (OR = 4.24; 95% CI, 1.40–12.78 on day 3 and OR = 5.35; 95% CI, 1.27–22.51 on day 7),(3) pulpless tooth (OR = 0.11; 95% CI, 0.02–0.57 on day 3), and(4) sonic activation during treatment (OR = 3.02; 95% CI, 1.39–6.57 on day 1 and OR = 3.01; 95% CI, 1.05–8.59 on day 3).ConclusionsThere was no significant difference in pain experience between teeth filled using AH Plus or TotalFill BC Sealer 1, 3, and 7 days after obturation. Patient- and treatment-related factors could influence postobturation pain.     

Pathoscope: Species identification and strain attribution with unassembled sequencing data

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly—which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico “environmental” samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.The accurate and rapid identification of species and strains of pathogens is an essential component of biosurveillance from both human health and biodefense perspectives (Vaidyanathan 2011). For example, misidentification was among the issues that resulted in a 3-wk delay in accurate diagnosis of the recent outbreak of hemorrhagic Escherichia coli being due to strain O104:H4, resulting in over 3800 infections across 13 countries in Europe with 54 deaths (Frank et al. 2011). The most accurate diagnostic information, necessary for species identification and strain attribution, comes from the most refined level of biological data—genomic DNA sequences (Eppinger et al. 2011). Advances in DNA-sequencing technologies allows for the rapid collection of extraordinary amounts of genomic data, yet robust approaches to analyze this volume of data are just developing, from both statistical and algorithmic perspectives.Next-generation sequencing approaches have revolutionized the way we collect DNA sequence data, including for applications in pathology, bioforensics, and biosurveillance. Given a particular clinical or metagenomic sample, our goal is to identify the specific species, strains, or substrains present in the sample, as well as accurately estimate the proportions of DNA originating from each source genome in the sample. Current approaches for next-gen sequencing usually have read lengths between 25 and 1000 bp; however, these sequencing technologies include error rates that vary by approach and by samples. Such variation is typically less important for species identification given the relatively larger genetic divergences among species than among individuals within species. But for strain attribution, sequencing error has the potential to swamp out discriminatory signal in a data set, necessitating highly sensitive and refined computational models and a robust database for both species identification and strain attribution.Current methods for classifying metagenomic samples rely on one or more of three general approaches: composition or pattern matching (McHardy et al. 2007; Brady and Salzberg 2009; Segata et al. 2012), taxonomic mapping (Huson et al. 2007; Meyer et al. 2008; Monzoorul Haque et al. 2009; Gerlach and Stoye 2011; Patil et al. 2012; Segata et al. 2012), and whole-genome assembly (Kostic et al. 2011; Bhaduri et al. 2012). Composition and pattern-matching algorithms use predetermined patterns in the data, such as taxonomic clade markers (Segata et al. 2012), k-mer frequency, or GC content, often coupled with sophisticated classification algorithms such as support vector machines (McHardy et al. 2007; Patil et al. 2012) or interpolated Markov Models (Brady and Salzberg 2009) to classify reads to the species of interest. These approaches require intensive preprocessing of the genomic database before application. In addition, the classification rule and results can often change dramatically depending on the size and composition of the genome database.Taxonomy-based approaches typically rely on a “lowest common ancestor” approach (Huson et al. 2007), meaning that they identify the most specific taxonomic group for each read. If a read originates from a genomic region that shares homology with other organisms in the database, the read is assigned to the lowest taxonomic group that contains all of the genomes that share the homologous region. These methods are typically highly accurate for higher-level taxonomic levels (e.g., phylum and family), but experience reduced accuracy at lower levels (e.g., species and strain) (Gerlach and Stoye 2011). Furthermore, these approaches are not informative when the reads originate from one or more species or strains that are closely related to each other or different organisms in the database. In these cases, all of the reads can be reassigned to higher-level taxonomies, thus failing to identify the specific species or strains contained in the sample.Assembly-based algorithms can often lead to the most accurate strain identification. However, these methods also require the assembly of a whole genome from a sample, which is a computationally difficult and time-consuming process that requires large numbers of reads to achieve an adequate accuracy—often on the order of 50–100× coverage of the target genome (Schatz et al. 2010). Given current sequencing depths, obtaining this level of coverage is usually possible for purified samples, but coverage levels may not be sufficient for mixed samples or in multiplexed sequencing runs. Assembly approaches are further complicated by the fact that data collection at a crime scene or hospital might include additional environmental components in the biological sample (host genome or naturally occurring bacterial and viral species), thus requiring multiple filtering and alignment steps in order to obtain reads specific to the pathogen of interest.Here we describe an accurate and efficient approach to analyze next-generation sequence data for species identification and strain attribution that capitalizes on a Bayesian statistical framework implemented in the new software package Pathoscope v1.0. Our approach accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of reference genomes. Importantly, our approach incorporates the possibility that multiple species can be present in the sample or that the target strain is not even contained within the reference database. It also accurately discriminates between very closely related strains of the same species with much less than 1× coverage of the genome and without the need for sequence assembly or complex preprocessing of the database or taxonomy. No other method in the literature can identify species or substrains in such a direct and automatic manner and without the need for large numbers of reads. We demonstrate our approach through application to next-generation DNA sequence data from a recent outbreak of the hemorrhagic E. coli (O104:H4) strain in Europe (Frank et al. 2011; Rohde et al. 2011; Turner 2011) and on purified and in silico mixed samples from several other known bacterial agents that impact human health. Software and data examples for our approach are freely available for download at https://sourceforge.net/projects/pathoscope/.     

Kaplan-Meier Survival Analysis Overestimates the Risk of Revision Arthroplasty: A Meta-analysis

Background

Although Kaplan-Meier survival analysis is commonly used to estimate the cumulative incidence of revision after joint arthroplasty, it theoretically overestimates the risk of revision in the presence of competing risks (such as death). Because the magnitude of overestimation is not well documented, the potential associated impact on clinical and policy decision-making remains unknown.

Questions/purposes

We performed a meta-analysis to answer the following questions: (1) To what extent does the Kaplan-Meier method overestimate the cumulative incidence of revision after joint replacement compared with alternative competing-risks methods? (2) Is the extent of overestimation influenced by followup time or rate of competing risks?

Methods

We searched Ovid MEDLINE, EMBASE, BIOSIS Previews, and Web of Science (1946, 1980, 1980, and 1899, respectively, to October 26, 2013) and included article bibliographies for studies comparing estimated cumulative incidence of revision after hip or knee arthroplasty obtained using both Kaplan-Meier and competing-risks methods. We excluded conference abstracts, unpublished studies, or studies using simulated data sets. Two reviewers independently extracted data and evaluated the quality of reporting of the included studies. Among 1160 abstracts identified, six studies were included in our meta-analysis. The principal reason for the steep attrition (1160 to six) was that the initial search was for studies in any clinical area that compared the cumulative incidence estimated using the Kaplan-Meier versus competing-risks methods for any event (not just the cumulative incidence of hip or knee revision); we did this to minimize the likelihood of missing any relevant studies. We calculated risk ratios (RRs) comparing the cumulative incidence estimated using the Kaplan-Meier method with the competing-risks method for each study and used DerSimonian and Laird random effects models to pool these RRs. Heterogeneity was explored using stratified meta-analyses and metaregression.

Results

The pooled cumulative incidence of revision after hip or knee arthroplasty obtained using the Kaplan-Meier method was 1.55 times higher (95% confidence interval, 1.43–1.68; p < 0.001) than that obtained using the competing-risks method. Longer followup times and higher proportions of competing risks were not associated with increases in the amount of overestimation of revision risk by the Kaplan-Meier method (all p > 0.10). This may be due to the small number of studies that met the inclusion criteria and conservative variance approximation.

Conclusions

The Kaplan-Meier method overestimates risk of revision after hip or knee arthroplasty in populations where competing risks (such as death) might preclude the occurrence of the event of interest (revision). Competing-risks methods should be used to more accurately estimate the cumulative incidence of revision when the goal is to plan healthcare services and resource allocation for revisions.     

How much does mental health discrimination cost: valuing experienced discrimination in relation to healthcare care costs and community participation

Aims.This study builds on existing research on the prevalence and consequences of mental illness discrimination by investigating and quantifying the relationships between experienced discrimination and costs of healthcare and leisure activities/social participation among secondary mental health service users in England.Methods.We use data from the Mental Illness-Related Investigations on Discrimination (MIRIAD) study (n = 202) and a subsample of the Viewpoint study (n = 190). We examine experiences of discrimination due to mental illness in the domains of personal relationships, community activities, and health care, and how such experienced discrimination relates to patterns of service use and engagement in leisure activities.Results.Our findings show that the cost of health services used for individuals who reported previous experiences of discrimination in a healthcare setting was almost twice as high as for those who did not report any discrimination during the last 12 months (Relative Risk: 1.73; 95% Confidence Interval (CI): 1.39, 2.17) and this was maintained after controlling for symptoms and functioning. Experienced discrimination in healthcare (Relative Risk: 0.83; 95% CI: 0.81, 0.84) or in relationships (Relative Risk: 0.89; 95% CI: 0.87, 0.91), however, was associated with lower participation in, and hence lower costs of, leisure activities. Individuals who reported any discrimination in a healthcare setting had, on average, £434 higher costs associated with health service use while reported discrimination in the community was associated with increased leisure costs of £32.Conclusions.These findings make an important initial step towards understanding the magnitude of the costs of mental health-related discrimination.Key words: Health economics, mental health services, mental illness, stigma     

Seroprevalence of HHV-8, CMV,and EBV among the general population in Ghana,West Africa

Background  

Human herpesvirus 8 (HHV-8), cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are prevalent in Africa, but less common elsewhere and the modes of transmission are still subject to debate. Generally, they rarely cause disease in the immunocompetent host but are highly oncogenic when associated with immunosuppression. Although the high prevalence of HHV-8, CMV and EBV has been well documented in Africa, such data are sparse from Ghana.     

Deciphering the canonical blockade of activated Hageman factor (FXIIa) by benzamidine in the coagulation cascade: A thorough dynamical perspective

The experimental inhibitory potency of benzamidine (BEN) paved way for further design and development of inhibitors that target β‐FXIIa. Structural dynamics of the loops and catalytic residues that encompass the binding pocket of β‐FXIIa and all serine proteases are crucial to their overall activity. Employing molecular dynamics and post‐MD analysis, this study sorts to unravel the structural and molecular events that accompany the inhibitory activity of BEN on human β‐FXIIa upon selective non‐covalent binding. Analysis of conformational dynamics of crucial loops revealed prominent alterations of the original conformational posture of FXIIa, evidenced by increased flexibility, decreased compactness, and an increased exposure to solvent upon binding of BEN, which could have in turn interfered with the essential roles of these loops in enhancing their procoagulation interactions with biological substrates and cofactors, altogether resulting in the consequential inactivation of FXIIa. A sustained interaction of the catalytic triad residues and key residues of the autolysis loop impeded their roles in catalysis which equally enhanced the inhibitory potency of BEN toward β‐FXIIa evidenced by a favorable binding. Findings provide essential structural and molecular insights that could facilitate the structure‐based design of novel antithrombotic compounds with enhanced inhibitory activities and low therapeutic risk.     

FACTORS ASSOCIATED TO ADHERENCE TO DIFFERENT TREATMENT SCHEMES WITH MEGLUMINE ANTIMONIATE IN A CLINICAL TRIAL FOR CUTANEOUS LEISHMANIASIS

The favorable outcome of the treatment of a disease is influenced by the adherence to therapy. Our objective was to assess factors associated with adherence to treatment of patients included in a clinical trial of equivalence between the standard and alternative treatment schemes with meglumine antimoniate (MA) in the treatment of cutaneous leishmaniasis (CL), in the state of Rio de Janeiro. Between 2008 and 2011, 57 patients with CL were interviewed using a questionnaire to collect socioeconomic data. The following methods were used for adherence monitoring: counting of vial surplus, monitoring card, Morisky test and modified Morisky test (without the question regarding the schedule); we observed 82.1% (vial return), 86.0% (monitoring card), 66.7% (Morisky test) and 86.0% (modified Morisky test) adherence. There was a strong correlation between the method of vial counting and the monitoring card and modified Morisky test. A significant association was observed between greater adherence to treatment and low dose of MA, as well as with a lower number of people sleeping in the same room. We recommend the use of the modified Morisky test to assess adherence to treatment of CL with MA, because it is a simple method and with a good performance, when compared to other methods.