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Background Preconception pregnancy risk profiles—characterizing the likelihood that a pregnancy attempt results in a full-term birth, preterm birth, clinical pregnancy loss, or failure to conceive—can provide critical information during the early stages of a pregnancy attempt, when obstetricians are best positioned to intervene to improve the chances of successful conception and full-term live birth. Yet the task of constructing and validating risk assessment tools for this earlier intervention window is complicated by several statistical features: the final outcome of the pregnancy attempt is multinomial in nature, and it summarizes the results of two intermediate stages, conception and gestation, whose outcomes are subject to competing risks, measured on different time scales, and governed by different biological processes. In light of this complexity, existing pregnancy risk assessment tools largely focus on predicting a single adverse pregnancy outcome, and make these predictions at some later, post-conception time point. Methods We reframe the individual pregnancy attempt as a multistate model comprised of two nested multinomial prediction tasks: one corresponding to conception and the other to the subsequent outcome of that pregnancy. We discuss the estimation of this model in the presence of multiple stages of outcome missingness and then introduce an inverse-probability-weighted Hypervolume Under the Manifold statistic to validate the resulting multivariate risk scores. Finally, we use data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial to illustrate how this multistate competing risks framework might be utilized in practice to construct and validate a preconception pregnancy risk assessment tool. Results In the EAGeR study population, the resulting risk profiles are able to meaningfully discriminate between the four pregnancy attempt outcomes of interest and represent a significant improvement over classification by random chance. Conclusions As illustrated in our analysis of the EAGeR data, our proposed prediction framework expands the pregnancy risk assessment task in two key ways—by considering a broader array of pregnancy outcomes and by providing the predictions at an earlier, preconception intervention window—providing obstetricians and their patients with more information and opportunities to successfully guide pregnancy attempts.

Chlamydia genital infection caused by Chlamydia trachomatis is the most common bacterial sexually transmitted disease worldwide. A mouse model has been developed in our laboratory to better understand the effect of cold-induced stress on chlamydia genital infection and immune response. However, the stress mechanism affecting the host response to Chlamydia muridarum genital infection remains unclear. Here, we demonstrate a role for the beta2-adrenergic receptor (β2-AR), which binds noradrenaline and modulates the immune response against chlamydia genital infection in a mouse model. A successful β2-AR homozygous knockout (KO) mouse model was used to study the infection and analyze the immune response. Our data show that stressed mice lacking the β2-AR are less susceptible to C. muridarum genital infection than controls. A correlation was obtained between lower organ load and higher interferon-gamma production by CD4+ and CD8+ cells of the KO mice. Furthermore, exposure of CD4+ T cells to noradrenaline alters the production of cytokines in mice during C. muridarum genital infection. This study suggests that the blockade of β2-AR signaling could be used to increase resistance to chlamydia genital infection. We value the β2-AR KO as a viable model that can provide reproducible results in investigating medical research, including chlamydia genital infection. Deficiency in a receptor leads to a reduced disease of chlamydia in a mouse model.

Assays for cancer diagnosis via the analysis of biomarkers on circulating extracellular vesicles (EVs) typically have lengthy sample workups, limited throughput or insufficient sensitivity, or do not use clinically validated biomarkers. Here we report the development and performance of a 96-well assay that integrates the enrichment of EVs by antibody-coated magnetic beads and the electrochemical detection, in less than one hour of total assay time, of EV-bound proteins after enzymatic amplification. By using the assay with a combination of antibodies for clinically relevant tumour biomarkers (EGFR, EpCAM, CD24 and GPA33) of colorectal cancer (CRC), we classified plasma samples from 102 patients with CRC and 40 non-CRC controls with accuracies of more than 96%, prospectively assessed a cohort of 90 patients, for whom the burden of tumour EVs was predictive of five-year disease-free survival, and longitudinally analysed plasma from 11 patients, for whom the EV burden declined after surgery and increased on relapse. Rapid assays for the detection of combinations of tumour biomarkers in plasma EVs may aid cancer detection and patient monitoring. A device that integrates the enrichment and electrochemical detection, in less than one hour, of tumour extracellular vesicles bearing clinically relevant tumour biomarkers accurately classifies patients with colorectal cancer.

Cluster-randomized trials (CRTs) have seen widespread use in such fields as health policy, hospital administration and management, and infectious disease epidemiology, where ethical, pragmatic, and scientific considerations make desirable the randomization of entire groups of observations. These randomization groups are not formed by investigators, and so are comprised of individuals who might reasonably share geographic or social bonds. This results in correlation within the final study sample. If the primary outcome is the time to some asymptomatic (or otherwise not directly observable) event, then these observations are also interval-censored: the exact timing of the event is known only up to the interval between study monitoring visits. Both clustering and interval censoring are associated with a loss of statistical information and study power. Thus the task of designing, monitoring, and analyzing CRTs with these features requires efficiently leveraging all available information while making as few assumptions as possible about the outcome process and underlying dependence structure. This dissertation addresses these challenges in two particular facets of CRT conduct: interim monitoring for study futility (Chapter 1), and final analysis via semiparametric regression methods (Chapters 2 and 3). In Chapter 1, we propose a flexible framework for conditional power estimation when outcomes are clustered and interval-censored; this represents the first interim monitoring method to directly account for both of these data structures. Chapters 2 and 3 then adopt techniques from the missing data literature in order to facilitate semiparametric estimation and inference for CRTs under cluster-conditional and marginal proportional hazards models, respectively.

Prolonged field care (PFC) for treatment of battlefield and trauma injuries requires the advancement of wound management techniques in order to prevent loss of life or limb prior to hospitalization in austere combat locations where medical evacuation is delayed. The goal of this project is to design a hydrogel wound dressing capable of providing sustained release of antibiotics, analgesics, and hemostatic agents over a three-day period. Poly(ethylene glycol) (PEG) hydrogels were fabricated through crosslinking using redox initiators – ammonium persulfate (APS) and tetramethylethylene diamine (TEMED). Hydrogels were characterized through the mass swelling ratio (qm) to determine the mesh size (ξ) and thus qualitatively predict the release kinetics of the therapeutic drugs. Hydrogels with incorporated therapeutic drug were placed in known volumes of deionized water, from which aliquots were taken at set time intervals. A UV Visible Spectrophotometer determined the aliquots’ absorbance which determined the cumulative release kinetics. Ultimately, three-day sustained release of the therapeutic drugs from the PEG hydrogel was achieved through retarding the diffusion of the therapeutic drugs by incorporating acrylic acid.

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype–phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary p value are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model. To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19.

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway). Testing sets of variants such as pathways for association with a disease phenotype reduces multiple testing penalties, may increase power, and allows for straightforward biological interpretation. However, a significant variant-set association test does not indicate precisely which variants contained within that set are causal. Because pathways often contain many variants, it may be helpful to follow-up significant pathway tests by conducting gene-based tests on each gene in that pathway to narrow in on the region of causal variants. In this paper, we propose such a multistep approach for variant-set analysis that can also account for covariates and complex pedigree structure. We demonstrate this approach on simulated phenotypes from Genetic Analysis Workshop 19. We find generally better power for the multistep approach when compared to a more conventional, single-step approach that simply runs gene-based tests of association on each gene across the genome. Further work is necessary to evaluate the multistep approach on different data sets with different characteristics.

The analysis of proteins expressed on circulating extracellular vesicles (EVs) could facilitate the diagnosis of different types of cancers. EV assays however have lengthy sample workups and limited throughput and sensitivity, making them unsuitable for routine clinical use. Here, we report a high-throughput assay that integrates EV enrichment, via antibody-coated magnetic beads, with the detection of EV-bound antibodies, via an electrochemical reaction. The assay requires less than one hour, is performed on plasma samples, and its 96-well plate format enables measurements in parallel via a prototype reader. Using samples from patients with colorectal cancer or healthy volunteers, we identified a panel of biomarkers (EGFR, EpCAM, CD24, GPA33) in circulating EVs that, when combined, showed higher diagnostic accuracy (>96%) than conventional assays. In a prospective cohort, the combined biomarker profile enabled assigning patients to a high- or a low-risk 5-year disease-free survival group, and the serial monitoring of EVs during therapy showed values declined after surgery yet increased upon relapse. Biomarker panels from plasma EVs may be suitable for the non-invasive monitoring of disease trajectory.

I'm not a \"dissatisfied, pessimistic parent\" described in the letter, however, I am a dissatisfied student. Thanks to reconfiguration, my education and athletics have been compromised. One reason for reconfiguring was due to the lack of students enrolling in Ladner and Tswassen, which didn't directly affect North Delta. Trustee Heather King has told me that another reason for reconfiguration was because \"...students attending NDSS in the past were not achieving the desired percentage on their provincials.\" To remedy this, the previous school board(s) decided to reconfigure the junior and senior high schools in North Delta so the students will hopefully achieve the desired percentage on their provincials.