Comparing multiple sensitivities and specificities with different diagnostic criteria: Applications to sexual abuse and sexual health research

When comparing sensitivities and specificities from multiple diagnostic tests, particularly in biomedical research, the different test kits under study are applied to groups of subjects with the same disease status for a disease or medical condition under consideration. Although this process gives rise to clustered or correlated test outcomes, the associated inference issues are well recognized and have been widely discussed in the literature. In mental health and psychosocial research, sensitivity and specificity have also been widely used to study the reliability of instruments for diagnosing mental health and psychiatric conditions and assessing certain behavioral patterns. However, unlike biomedical applications, outcomes are often obtained under varying reference standards or different diagnostic criteria, precluding the application of existing methods for comparing multiple diagnostic tests to such a research setting. In this paper, we develop a new approach to address these problems (including that of missing data) by extending recent work on inference using inverse probability weighted estimates. The approach is illustrated with data from two studies in sexual abuse and health research as well as a limited simulation study, with the latter used to study the performance of the proposed procedure.

Power analysis for clustered non-continuous responses in multicenter trials

<p>Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.</p

In vivo 808 nm image-guided photodynamic therapy based on an upconversion theranostic nanoplatform (vol 7, pg 14914, 2015)

Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

In vivo 808 nm image-guided photodynamic therapy based on an upconversion theranostic nanoplatform

Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas

A Novel Genetic Algorithm with Orthogonal Prediction for Global Numerical Optimization

Abstract. This paper proposes a novel orthogonal predictive local search (OPLS) to enhance the performance of the conventional genetic algorithms. OPLS operation predicts the most promising direction for the individuals to explore their neighborhood. It uses the orthogonal design method to sample orthogonal combinations to make the prediction. The resulting algorithm is termed the orthogonal predictive genetic algorithm (OPGA). OPGA has been tested on eleven numerical optimization functions in comparison with some typical algorithms. The results demonstrate the effectiveness of the proposed algorithm for achieving better solutions with a faster convergence speed

A scintillating nanoplatform with upconversion function for the synergy of radiation and photodynamic therapies for deep tumors

Collaborative therapy is regarded as an effective approach in increasing the therapeutic efficacy of cancer. In this work, we have proposed and validated the concept of upconversion lumienscence image guided synergy of photodynamic therapy (PDT) and radiotherapy (RT) for deep cancer, via a specially designed nanoplatform integrating near infrared (NIR) light activated luminescence upconversion and X-ray induced scintillation. Upon NIR light irradiation, the nanoplatform emits highly monochromatic red light solely for imaging the targeted cancer cells without triggering therapy; however, when the irradiation turns to a low dose of X-rays, scintillation will occur which induces effectively the PDT destroying the cancer cells together with X-ray induced RT. The novel theranostic nanoplatform is constructed in such a way that the interactions between the upconversion core and the outmost scintillating shell are blocked effectively by an inert layer between them. This structural design not only enables a nearly perfect excitation energy delivery (similar to 100% at a spectral overlapping wavelength of similar to 540 nm) from the outermost scintellating layer to the surface-anchored photosensitizers and so a maximum yield of radical oxygen species, but also achieves a strong NIR induced upconversion luminescence for imaging. Since PDT and RT attack different parts of a cancer cell, this synergy is more effective in destroying cancer than a single therapy, resulting in the reduction of the X-ray irradiation dosage. As a proof of principle, the theranostic effect is validated by in vitro and in vivo experiments, exhibiting the great potential of this sort of nanoplatform in deep cancer treatment