A major limiting factor in stem cell therapy is the accurate identification of the differentiation state of cells destined for transplantation. This study aimed to evaluate the potential of synchrotron radiation Fourier transform infrared (SR-FTIR) microspectroscopy as a novel technique to probe the differentiation state of human mesenchymal stem cells (hMSCs) to chondrocytes over a period of 7, 14 and 21 days of induction. The chondrogenic markers were determined using reverse transcription polymerase chain reaction, histology and immunohistochemistry. The changes of average spectra located near 1338–1230 and 1175–960 cm⁻¹ indicated increased levels of collagen and aggrecan, respectively, in chondrocyte-induced hMSCs compared with control cells. Classification of independent test spectra using partial least squares discriminant analysis (PLS-DA) could distinguish control and chondrocyte-induced cells with 100% accuracy. We conclude that the SR-FTIR microspectroscopy technique is sensitive for monitoring the differentiation state of stem cells under chondrogenic induction particularly at an early stage. It provides biochemical information that is complimentary to that obtained from conventional techniques, and may give more unambiguous results particularly at the very early stage of cellular differentiation. In addition, the spectroscopic approach is more straightforward, non-destructive and requires less sample preparation compared with the conventional methodologies.

Infection with high-risk human papillomavirus (HPV) is a major risk factor for oral and cervical cancers. Hence, we developed a multianalyte electrochemical DNA biosensor that could be used for both oral and cervical samples to detect the high-risk HPV genotypes 16 and 18. The assay involves the sandwich hybridization of the HPV target to the silica-redox dye reporter probe and capture probe, followed by electrochemical detection. The sensor was found to be highly specific and sensitive, with a detection limit of 22 fM for HPV-16 and 20 fM for HPV-18, between the range of 1 fM and 1 µM. Evaluation with oral and cervical samples showed that the biosensor result was consistent with the nested PCR/gel electrophoresis detection. The biosensor assay could be completed within 90 min. Due to its simplicity, rapidity, and high sensitivity, this biosensor could be used as an alternative method for HPV detection in clinical laboratories as well as for epidemiological studies.

Cholangiocarcinoma (CCA) is a malignancy of the bile duct epithelium. Opisthorchis viverrini infection is a known high-risk factor for CCA and in found, predominantly, in Northeast Thailand. The silent disease development and ineffective diagnosis have led to late-stage detection and reduction in the survival rate. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) is currently being explored as a diagnostic tool in medicine. In this study, we apply ATR-FTIR to discriminate CCA sera from hepatocellular carcinoma (HCC), biliary disease (BD) and healthy donors using a multivariate analysis. Spectral markers differing from healthy ones are observed in the collagen band at 1284, 1339 and 1035 cm⁻¹, the phosphate band ($vsP{O}_2^{-}$) at 1073 cm⁻¹, the polysaccharides band at 1152 cm⁻¹ and 1747 cm⁻¹ of lipid ester carbonyl. A Principal Component Analysis (PCA) shows discrimination between CCA and healthy sera using the 1400–1000 cm⁻¹ region and the combined 1800—1700 + 1400–1000 cm⁻¹ region. Partial Least Square-Discriminant Analysis (PLS-DA) scores plots in four of five regions investigated, namely, the 1400–1000 cm⁻¹, 1800–1000 cm⁻¹, 3000–2800 + 1800–1000 cm⁻¹ and 1800–1700 + 1400–1000 cm⁻¹ regions, show discrimination between sera from CCA and healthy volunteers. It was not possible to separate CCA from HCC and BD by PCA and PLS-DA. CCA spectral modelling is established using the PLS-DA, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). The best model is the NN, which achieved a sensitivity of 80–100% and a specificity between 83 and 100% for CCA, depending on the spectral window used to model the spectra. This study demonstrates the potential of ATR-FTIR spectroscopy and spectral modelling as an additional tool to discriminate CCA from other conditions.