The (n,m) population distribution of single-walled carbon nanotubes obtained on supported CoMo catalysts has been determined by photoluminescence and optical absorption. It has been found that the (n,m) distribution can be controlled by varying the gaseous feed composition, the reaction temperature, and the type of catalyst support used. When using CO as a feed over CoMo/SiO2 catalysts, increasing the synthesis temperature results in an increase in nanotube diameter, without a change in the chiral angle. By contrast, by changing the support from SiO2 to MgO, nanotubes with similar diameter but different chiral angles are obtained. Finally, keeping the same reaction conditions but varying the composition of the gaseous feed results in different (n,m) distribution. The clearly different distributions obtained when varying catalysts support and/or reaction conditions demonstrate that the (n,m) distribution is a result of differences in the growth kinetics, which in turn depends on the nanotube cap-metal cluster interaction.
Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.
The dispersibility and bundle defoliation of single-walled carbon nanotubes (SWNTs) of small diameter (<1 nm) have been evaluated on CoMoCAT samples with narrow distribution of diameters. As previously observed by photoluminescence and Raman spectroscopy, the CoMoCAT sample exhibits a uniquely narrow distribution of (n,m) structures that remains unchanged after different dispersion conditions. This narrow distribution allowed us to develop a method for quantifying the dispersability of the samples from their optical absorption spectra in terms of two ratios: the "resonance ratio" and the "normalized width." The former is defined as the quotient of the resonant band area and its nonresonant background. The latter is defined as the ratio of the width of the band at half-height to the peak height on a spectrum that has been normalized at 900 nm, making this an intensive property, rather than varying with the path length. In this study of the CoMoCAT sample, we have used the S22 transition corresponding to the (6,5) nanotube to do these calculations, which is the most abundant species. These two ratios provide a quantitative tool to compare different dispersion parameters (time of sonication, degree of centrifugation, etc.) on the same type of sample. From this comparison, an optimal procedure that maximizes the spectral features was selected; this procedure allowed us to contrast various surfactants at different pH values and concentrations. Several surfactants were as good or even better than the one we have used in previous studies, dodecylbenesulfonic acid sodium salt (NaDDBS). Despite differences in their dispersion abilities, none of the surfactants investigated generated new features in the absorption spectra nor changed the distribution of nanotube types, which confirms that the high selectivity of the CoMoCAT sample is in the original sample rather than caused by selective suspension of specific (n,m) nanotubes.
Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74–0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean ± std: ICC = 0.96 ± 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.
GGO volume percentage in tumors with exon 21 missense mutation was significantly higher than that in tumors with other EGFR mutation status. This can be related to the fact that exon 21 missense mutation was significantly more frequent in lepidic predominant adenocarcinomas, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and lepidic predominant invasive adenocarcinoma, according to IASLE/ATS/ERS classification.
CT slice thickness and reconstruction algorithm can significantly affect the quantification of image features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used interchangeably. Sharper and smoother reconstructions significantly affect the density-based features.
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