Citations

Mathematical models are non-invasive tools used for analyzing biomechanical conditions. Various models of the knee using the finite element analysis technique [1–6] and rigid body spring model (RBSM) [7] have been developed to predict pressure distribution in articulating joints, although the RBSM is considered the simplest of the modeling techniques, while still effectively predicting load distribution. These models can be used to study altered joint loading, which causes a degeneration of the articular cartilage, in order to gain an understanding of the etiology of osteoarthritis (OA). The purpose of this study is to develop a subject-specific analytical knee model using the RBSM approach that incorporates subject-specific geometry, joint health, and loading patterns to predict tibiofemoral load distributions.

Abstract Objective Risk scores and accelerated diagnostic protocols can identify chest pain patients with low risk of major adverse cardiac event who could be discharged early from the ED , saving time and costs. We aimed to derive and validate a chest pain score and accelerated diagnostic protocol ( ADP ) that could safely increase the proportion of patients suitable for early discharge. Methods Logistic regression identified statistical predictors for major adverse cardiac events in a derivation cohort. Statistical coefficients were converted to whole numbers to create a score. Clinician feedback was used to improve the clinical plausibility and the usability of the final score (Emergency Department Assessment of Chest pain Score [ EDACS ]). EDACS was combined with electrocardiogram results and troponin results at 0 and 2 h to develop an ADP ( EDACS‐ADP ). The score and EDACS‐ADP were validated and tested for reproducibility in separate cohorts of patients. Results In the derivation ( n = 1974) and validation ( n = 608) cohorts, the EDACS‐ADP classified 42.2% (sensitivity 99.0%, specificity 49.9%) and 51.3% (sensitivity 100.0%, specificity 59.0%) as low risk of major adverse cardiac events, respectively. The intra‐class correlation coefficient for categorisation of patients as low risk was 0.87. Conclusion The EDACS‐ADP identified approximately half of the patients presenting to the ED with possible cardiac chest pain as having low risk of short‐term major adverse cardiac events, with high sensitivity. This is a significant improvement on similar, previously reported protocols. The EDACS‐ADP is reproducible and has the potential to make considerable cost reductions to health systems.

Abstract Cohen’s kappa coefficient, which was introduced in 1960, serves as the most widely employed coefficient to assess inter-observer agreement for categorical outcomes. However, the original kappa can only be applied to cross-sectional binary measurements and, therefore, cannot be applied in the practical situation when the observers evaluate the same subjects at repeated time intervals. This study summarizes six methods of assessing agreement of repeated binary outcomes under different assumptions and discusses under which condition we should use the most appropriate method in practice. These approaches are illustrated using data from the CDC anthrax vaccine adsorbed (AVA) human clinical trial comparing the agreement for two solicited adverse events after AVA between the 1–3 day in-clinic medical record and the patient’s diary on the same day. We hope this article can inspire researchers to choose the most appropriate method to assess agreement for their own study with longitudinal binary data.

Objectives. To assess the clinical features and local control (LC) outcomes in adult patients with localized Ewing Sarcoma (ES).Methods. The records of 102 ES patients with localized disease ≥18 years of age seen from 1977 to 2007 were reviewed. Factors relevant to prognosis, survival, and LC were analyzed.Results.The 5-year overall survival (OS) and event-free survival (EFS) were 60% and 52%, respectively, for the entire cohort. Treatment era (1977–1992 versus 1993–2007) remained an independent prognostic factor for OS on multivariate analysis, with improved outcomes observed in the 1993–2007 era (P=0.02). The 5-year OS and EFS for the 1993–2007 era were 73% and 60%, respectively. Ifosfamide and etoposide based chemotherapy and surgery were more routinely used in the 1993–2007 era (P<0.01). The 5-year local failure rate (LFR) was 14%, with a 5-year LFR of 18% for surgery, 33% for radiation, and 0% for combined surgery and radiation in the 1993–2007 era (P=0.17).Conclusion. Modern survival outcomes for adults with localized ES are similar to multi-institutional results in children. This improvement over time is associated with treatment intensification with chemotherapy and increased use of surgery. Aggressive LC (combined surgery and radiation) may improve outcomes in poor prognosis patients.

ABSTRACTVenous thromboembolism (VTE) is a common, heritable disease resulting in high rates of hospitalization and mortality. Yet few associations between VTE and genetic variants, all in the coagulation pathway, have been established. To identify additional genetic determinants of VTE, we conducted a two‐stage genome‐wide association study (GWAS) among individuals of European ancestry in the extended cohorts for heart and aging research in genomic epidemiology (CHARGE) VTE consortium. The discovery GWAS comprised 1,618 incident VTE cases out of 44,499 participants from six community‐based studies. Genotypes for genome‐wide single‐nucleotide polymorphisms (SNPs) were imputed to approximately 2.5 million SNPs in HapMap and association with VTE assessed using study‐design appropriate regression methods. Meta‐analysis of these results identified two known loci, in F5 and ABO. Top 1,047 tag SNPs (P ≤ 0.0016) from the discovery GWAS were tested for association in an additional 3,231 cases and 3,536 controls from three case‐control studies. In the combined data from these two stages, additional genome‐wide significant associations were observed on 4q35 at F11 (top SNP rs4253399, intronic to F11) and on 4q28 at FGG (rs6536024, 9.7 kb from FGG; P < 5.0 × 10−13 for both). The associations at the FGG locus were not completely explained by previously reported variants. Loci at or near SUSD1 and OTUD7A showed borderline yet novel associations (P < 5.0 × 10−6) and constitute new candidate genes. In conclusion, this large GWAS replicated key genetic associations in F5 and ABO, and confirmed the importance of F11 and FGG loci for VTE. Future studies are warranted to better characterize the associations with F11 and FGG and to replicate the new candidate associations.

AbstractBackgroundApolipoprotein E (APOE) ε4 allele's role as a modulator of the relationship between soluble plasma amyloid beta (Aβ) and fibrillar brain Aβ measured by Pittsburgh compound B positron emission tomography ([11C]PiB PET) has not been assessed.MethodsNinety‐six Alzheimer's Disease Neuroimaging Initiative participants with [11C]PiB scans and plasma Aβ1–40 and Aβ1–42 measurements at the time of PET scanning were included. Regional and voxelwise analyses of [11C]PiB data were used to determine the influence of APOE ε4 allele on association of plasma Aβ1–40, Aβ1–42, and Aβ1–40/Aβ1–42 with [11C]PiB uptake.ResultsIn APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ1–40/Aβ1–42 and [11C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1–40/Aβ1–42 improved the explained variance in [11C]PiB binding compared with using APOE and plasma Aβ1–40/Aβ1–42 as separate terms.ConclusionsThe results suggest that plasma Aβ is a potential Alzheimer's disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.

Podocyturia, the shedding of live podocytes, is present at delivery in women with preeclampsia. The aim of this study was to test whether podocyturia is present earlier in pregnancy and predicts for preeclampsia. We also aimed to compare test characteristics of podocyturia with those of angiogenic factors previously implicated in the pathogenesis of this disorder. We prospectively enrolled 315 women who provided blood and urine samples at the end of the second trimesters of their pregnancies (median, 27 gestational weeks) and within 24 hours of their deliveries (median, 39.5 gestational weeks). Blood samples were analyzed for angiogenic markers, including placental growth factor, the soluble receptor fms-like tyrosine kinase receptor-1 for vascular endothelial growth factor, and endoglin. The urine sediments were analyzed for podocytes, identified by staining for podocin after culturing the urinary sediments for 24 hours. This analysis included all women who developed preeclampsia (n=15), gestational hypertension (n=15), and a subsample of women who remained normotensive throughout pregnancy (n=44), matched for maternal age and number of previous pregnancies to those who developed preeclampsia. At the second trimester collection, all women who developed preeclampsia had podocyturia, compared with none of those who remained normotensive or were diagnosed with gestational hypertension. Podocyturia in the second trimester had a significantly greater sensitivity and specificity for the subsequent diagnosis of preeclampsia than any single angiogenic marker or a combination thereof. Screening for podocyturia at the end of the second trimester may allow for accurate identification of pregnant women at risk for preeclampsia.

Abstract In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.