We report a label-free Surface Enhanced Raman Spectroscopy (SERS) for pleural fluid analysis to distinguish Lung cancer from controls patients. Herein, we have used a novel silver coated silicon Nanopillar (SCSNP) as SERS substrate to acquire multiple SERS spectra for each pleural fluid sample and advanced chemometrics methods. We report a classification accuracy of 85% along with sensitivity and specificity of 87% and 83% respectively for the detection of Lung cancer over control pleural fluid samples with a receiver operating characteristics (ROC) area under curve value of 0.92 using PLS-DA binary classifier to distinguish between lung cancer over control subjects.
A flexible membrane based Surface-Enhanced Raman Spectroscopy (SERS) sensor was developed as a viable point-of-care platform to monitor changes of these surrogate indicators of healing status in chronic wounds, such as tumor necrosis factor alpha (TNFα) and matrix metalloproteinase (MMPs). In terms of performance, SERS approach is superior to enzyme-based assays, which are resource intensive. We demonstrated the efficiency of this flexible SERS platform for the sensitive detection of TNFα and MMP9 in the nM to pM range. These substrates may be incorporated into wound dressings to permit routine monitoring of wound status.