We report a camptothecin (CPT) prodrug that was well formulated in solution and rapidly transformed into long-circulating nanocomplexes in vivo for highly efficient drug delivery and effective cancer therapy. Specifically, using a redox-responsive disulfide linker, CPT was conjugated with an albumin-binding Evans blue (EB) derivative; the resulting amphiphilic CPT-ss-EB prodrug self-assembled into nanostructures in aqueous solution, thus conferring high solubility and stability. By binding CPT-ss-EB to endogenous albumin, the 80 nm CPT-ss-EB nanoparticles rapidly transformed into 7 nm albumin/prodrug nanocomplexes. CPT-ss-EB was efficient at intracellular delivery into cancer cells, released intact CPT in a redox-responsive manner, and exhibited cytotoxicity as potent as CPT. In mice, the albumin/CPT-ss-EB nanocomplex exhibited remarkably long blood circulation (130-fold greater than CPT) and efficient tumor accumulation (30-fold of CPT), which consequently contributed to excellent therapeutic efficacy. Overall, this strategy of transformative nanomedicine is promising for efficient drug delivery.

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We propose SPHARM-OT, an enhanced spherical harmonic (SPHARM) surface modeling method using optimal transport (OT) for spherical parameterization. To demonstrate its effectiveness, we apply it to shape analysis of amygdala atrophy in Alzheimer's disease (AD). Of note, identifying morphological abnormalities of medial temporal structures such as hippocampus and amygdala is an important research topic for early diagnosis of AD. Our empirical study includes two steps: (1) the newly proposed SPHARM-OT method is used to model amygdala shapes of 101 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI); (2) using the random field theory to perform surface-based statistical analysis for detecting shape changes between cognitively normal (CN) and AD participants. We demonstrate that (1) the new SPHARM-OT method could effectively reduce surface mapping distortion and lead to a more accurate shape reconstruction result; and (2) significant shape changes between CN and AD participants are identified on certain amygdalar surface regions.

Unpredictability in query runtimes can arise in a shared cluster as a result of resource contentions caused by inter-query interactions. iQCAR - inter Query Contention AnalyzeR is a system that formally models these interferences between concurrent queries and provides a framework to attribute blame for contentions. iQCAR leverages a multi-level directed acyclic graph called iQC-Graph to diagnose the aberrations in query schedules that lead to these resource contentions. The demonstration will enable users to perform a step-wise deep exploration of such resource contentions faced by a query at various stages of its execution. The interface will allow users to identify top-k victims and sources of contentions, diagnose high-contention nodes and resources in the cluster, and rank their impacts on the performance of a query. Users will also be able to navigate through a set of rules recommended by iQCAR to compare how application of each rule by the cluster scheduler resolves the contentions in subsequent executions.