Xiao Wang

The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset.

Self-assembly is a powerful means to fabricate multifunctional smart nanotheranostics. However, the complicated preparation, toxicity of responsive carriers, and low loading efficiency of drug cargo hinder the outcome. Herein, we developed a responsive carrier-free noncovalent self-assembly strategy of a metallized Au(III) tetra-(4-pyridyl) porphine (AuTPyP) anticancer drug for the preparation of a heat/acid dual-stimulated nanodrug, and it generated a better photothermal effect than monomers under irradiation. The photothermal effect promoted the protonation of the hydrophobic pyridyl group and the following release into tumorous acidic microenvironments. With cRGD modification, the released drug induced the aggravation of intracellular reactive oxygen species (ROS) via the activity inhibition of thioredoxin reductase (TrxR) for synergistic chemo-photothermal therapy of tumors.

Glioblastoma multiforme (GBM) is the most common malignant brain tumor with low survival, primarily due to the blood-brain barrier (BBB) and high infiltration. Upconversion nanoparticles (UCNPs)-based near-infrared (NIR) phototherapy with deep penetration is a promising therapy method against glioma but faces low photoenergy utilization that is induced by spectral mismatch and single-site Förster resonance energy transfer (FRET). Herein, we designed a brain-targeting NIR theranostic system with a dual-site FRET route and superior spectral matching to maximize energy utilization for synergistic photodynamic and photothermal therapy of glioma. The system was fabricated by Tm-doped UCNPs, zinc tetraphenylporphyrin (ZnTPP), and copper sulfide (CuS) nanoparticles under multioptimized modulation. First, the Tm-doping ratio was precisely adjusted to improve the relative emission intensity at 475 nm of UCNPs (11.5-fold). Moreover, the J-aggregate of ZnTPP increased the absorption at 475 nm (163.5-fold) of monomer; both together optimize the FRET matching between UCNPs and porphyrin for effective NIR photodynamic therapy. Simultaneously, the emission at 800 nm was utilized to magnify the photothermal effect of CuS nanoparticles for photothermal therapy via the second FRET route. After being modified by a brain-targeted peptide, the system efficiently triggers the synergistic phototherapy ablation of glioma cells and significantly prolongs the survival of orthotopic glioma-bearing mice after traversing the BBB and targeting glioma. This success of advanced spectral modulation and dual-site FRET strategy may inspire more strategies to maximize the photoenergy utilization of UCNPs for brain diseases.