Synthesis and evaluation of a near-infrared fluorescent non-peptidic bivalent integrin alpha(v)beta(3) antagonist for cancer imaging.

Computer modeling approaches to identify new inhibitors are essentially a very sophisticated and efficient way to design drugs. In this study, a bivalent nonpeptide intergrin alpha(v)beta(3) antagonist (bivalent IA) has been synthesized on the basis of an in silico rational design approach. A near-infrared (NIR) fluorescent imaging probe has been developed from this bivalent compound. In vitro binding assays have shown that the bivalent IA (IC(50) = 0.40 +/- 0.11 nM) exhibited improved integrin alpha(v)beta(3) affinity in comparison with the monovalent IA (IC(50) = 22.33 +/- 4.51 nM), resulting in an over 50-fold improvement in receptor affinity. NIR imaging probe, bivalent-IA-Cy5.5 conjugate, also demonstrated significantly increased binding affinity (IC(50) = 0.13 +/- 0.02 nM). Fluorescence microscopy studies showed integrin-mediated endocytosis of bivalent-IA-Cy5.5 in U87 cells which was effectively blocked by nonfluorescent bivalent IA. We also demonstrated tumor accumulation of this NIR imaging probe in U87 mouse xenografts.

Fabrication of localized surface plasmon resonance fiber probes using ionic self-assembled gold nanoparticles.

An nm-thickness composite gold thin film consisting of gold nanoparticles and polyelectrolytes is fabricated through ionic self-assembled multilayers (ISAM) technique and is deposited on end-faces of optical fibers to construct localized surface plasmon resonance (LSPR) fiber probes. We demonstrate that the LSPR spectrum induced by ISAM gold films can be fine-tuned through the ISAM procedure. We investigate variations of reflection spectra of the probe with respect to the layer-by-layer adsorption of ISAMs onto end-faces of fibers, and study the spectral variation mechanism. Finally, we demonstrated using this fiber probe to detect the biotin-streptavidin bioconjugate pair. ISAM adsorbed on optical fibers potentially provides a simple, fast, robust, and low-cost, platform for LSPR biosensing applications.

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification.

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Computer-assisted quantitative evaluation of therapeutic responses for lymphoma using serial PET/CT imaging.

RATIONALE AND OBJECTIVES: Molecular imaging modalities such as positron emission tomography (PET)/computed tomography (CT) have emerged as an essential diagnostic tool for monitoring treatment response in lymphoma patients. However, quantitative assessment of treatment outcomes from serial scans is often difficult, laborious, and time consuming. Automatic quantization of longitudinal PET/CT scans provides more efficient and comprehensive quantitative evaluation of cancer therapeutic responses. This study develops and validates a Longitudinal Image Navigation and Analysis (LINA) system for this quantitative imaging application. MATERIALS AND METHODS: LINA is designed to automatically construct longitudinal correspondence along serial images of individual patients for changes in tumor volume and metabolic activity via regions of interest (ROI) segmented from a given time point image and propagated into the space of all follow-up PET/CT images. We applied LINA retrospectively to nine lymphoma patients enrolled in an immunotherapy clinical trial conducted at the Center for Cell and Gene Therapy, Baylor College of Medicine. This methodology was compared to the readout by a diagnostic radiologist, who manually measured the ROI metabolic activity as defined by the maximal standardized uptake value (SUVmax). RESULTS: Quantitative results showed that the measured SUVs obtained from automatic mapping are as accurate as semiautomatic segmentation and consistent with clinical examination findings. The average of relative squared differences of SUVmax between automatic and semiautomatic segmentation was found to be 0.02. CONCLUSIONS: These data support a role for LINA in facilitating quantitative analysis of serial PET/CT images to efficiently assess cancer treatment responses in a comprehensive and intuitive software platform.

Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease.

Resting-state networks dissociate in the early stage of Alzheimer's disease (AD). The posterior cingulate cortex (PCC) in AD brain is vulnerable to isolation from the rest of brain. However, it remains unclear how this functional connectivity is related to PCC changes. We employed resting-state functional MRI (fMRI) to examine brain regions with a functional connection to PCC in a mild AD group compared with matched control subjects. PCC connectivity was gathered by investigating synchronic low frequency fMRI signal fluctuations with a temporal correlation method. We found asymmetric PCC-left hippocampus, right dorsal-lateral prefrontal cortex and right thalamus connectivity disruption. In addition, some other regions such as the bilateral visual cortex, the infero-temporal cortex, the posterior orbital frontal cortex, the ventral medial prefrontal cortex and the precuneus showed decreased functional connectivity to the PCC. There were also some regions, primarily in the left frontal-parietal cortices, that showed increased connectivity. These regions included the medial prefrontal cortex, bilateral dorsal-lateral prefrontal cortex, the left basal ganglia and the left primary motor cortex. Impairments to memory, high vision-related functions and olfaction in AD can be explained by a disruption to the functional connection of resting-state networks. The results of increased connectivity may support the compensatory-recruitment hypothesis. Our findings suggest that the characteristics of resting-state functional connectivity could plausibly provide an early imaging biomarker for AD.