ASU Scholarship Showcase

Improved tools for providing specific intraoperative diagnoses could improve patient care. In neurosurgery, intraoperatively differentiating non-operative lesions such as CNS B-cell lymphoma from operative lesions can be challenging, often necessitating immunohistochemical (IHC) procedures which require up to 24-48 hours. Here, we evaluate the feasibility of generating rapid ex vivo specific labeling using a novel lymphoma-specific fluorescent switchable aptamer. Our B-cell lymphoma-specific switchable aptamer produced only low-level fluorescence in its unbound conformation and generated an 8-fold increase in fluorescence once bound to its target on CD20-positive lymphoma cells. The aptamer demonstrated strong binding to B-cell lymphoma cells within 15 minutes of incubation as observed by flow cytometry. We applied the switchable aptamer to ex vivo xenograft tissue harboring B-cell lymphoma and astrocytoma, and within one hour specific visual identification of lymphoma was routinely possible. In this proof-of-concept study in human cell culture and orthotopic xenografts, we conclude that a fluorescent switchable aptamer can provide rapid and specific labeling of B-cell lymphoma, and that developing aptamer-based labeling approaches could simplify tissue staining and drastically reduce time to histopathological diagnoses compared with IHC-based methods. We propose that switchable aptamers could enhance expeditious, accurate intraoperative decision-making.

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration.

Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings.

Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.

Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

This randomized prospective trial aimed to assess the feasibility and efficacy of a team-based worksite health and safety intervention for law enforcement personnel. Four-hundred and eight subjects were enrolled and half were randomized to meet for weekly, peer-led sessions delivered from a scripted team-based health and safety curriculum. Curriculum addressed: exercise, nutrition, stress, sleep, body weight, injury, and other unhealthy lifestyle behaviors such as smoking and heavy alcohol use. Health and safety questionnaires administered before and after the intervention found significant improvements for increased fruit and vegetable consumption, overall healthy eating, increased sleep quantity and sleep quality, and reduced personal stress.

Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase power has not been investigated. To address this deficit, in a first study we compared the analytical power values of the mediated effect and the total effect in a single-mediator model, to identify the situations in which the inclusion of one mediator increased statistical power. The results from this first study indicated that including a mediator increased statistical power in small samples with large coefficients and in large samples with small coefficients, and when coefficients were nonzero and equal across models. Next, we identified conditions under which power was greater for the test of the total mediated effect than for the test of the total effect in the parallel two-mediator model. These results indicated that including two mediators increased power in small samples with large coefficients and in large samples with small coefficients, the same pattern of results that had been found in the first study. Finally, we assessed the analytical power for a sequential (three-path) two-mediator model and compared the power to detect the three-path mediated effect to the power to detect both the test of the total effect and the test of the mediated effect for the single-mediator model. The results indicated that the three-path mediated effect had more power than the mediated effect from the single-mediator model and the test of the total effect. Practical implications of these results for researchers are then discussed.