OBJECTIVE: In this presented work, an analytical method by gas chromatography coupled with flame ionization detection (GC-FID) has been developed to determine organic solvents in radiopharmaceutical samples. The effect of injection holding time, temperature variation in the injection port, and the column temperature on the analysis time and resolution (R ≥ 1.5) of ethanol and acetonitrile was studied extensively.
METHODS: The experimental conditions were optimized with the aid of further statistical analysis; thence, the proposed method was validated following the International Council for Harmonisation (ICH) Q2 (R1) guideline.
RESULTS: The proposed analytical method surpassed the acceptance criteria including the linearity > 0.990 (correlation coefficient of R2), precision < 2%, LOD, and LOQ, accuracy > 90% for all solvents. The separation between ethanol and acetonitrile was acceptable with a resolution R > 1.5. Further statistical analysis of Oneway ANOVA revealed that the increment in injection holding time and variation of temperature at the injection port did not significantly affect the analysis time. Nevertheless, the variation in injection port temperature substantially influenced the resolution of ethanol and acetonitrile peaks (p < 0.05).
CONCLUSION: The proposed analytical method has been successfully implemented to determine the organic solvent in the [18F]fluoro-ethyl-tyrosine ([18F]FET), [18F]fluoromisonidazole ([18F]FMISO), and [18F]fluorothymidine ([18F]FLT).
METHOD: The performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.
RESULTS: This study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.
CONCLUSION: The findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.
METHODS: Pooled urine samples of patients with BTG (n=10), patients with PTC (n=9) and healthy controls (n=10) were subjected to iTRAQ analysis and immunoblotting.
RESULTS: The ITRAQ analysis of the urine samples detected 646 proteins, 18 of which showed significant altered levels (p<0.01; fold-change>1.5) between patients and controls. Whilst four urinary proteins were commonly altered in both BTG and PTC patients, 14 were unique to either BTG or PTC. Amongst these, four proteins were further chosen for validation using immunoblotting, and the enhanced levels of osteopontin in BTG patients and increased levels of a truncated gelsolin fragment in PTC patients, relative to controls, appeared to corroborate the findings of the iTRAQ analysis.
CONCLUSION: The data of the present study is suggestive of the potential application of urinary osteopontin and gelsolin to discriminate patients with BTG from those with PTC non-invasively. However, this needs to be further validated in studies of individual urine samples.