The clinical Transferability of Raman Micro-Spectroscopic Systems for Cervical Cytopathology

The clinical potential for Raman microscopic systems is well established for early diagnosis via cytology. Although Raman systems offer a complementary diagnostic tool providing molecular information, it is not yet utilised substantially in clinics. A few challenges for the clinical implementation of Raman spectroscopy are system and user variability. In this study, we asked how much variability occurs due to different Raman systems or users. To address these questions, we measured the same set of cells using two different Raman microscopes and by two different users. And classification models were generated using multivariate partial least squares discriminant analysis (PLS-DA) and analysed for clinical implementation. Raman spectra were measured from single exfoliated cells (n=400) from ThinPrep samples with negative cytology (n=10) and high-grade cytology (n=10). Raman spectra were acquired from the same set of cells via two identical HORIBA Jobin Yvon XploRATM systems (Villeneuve d\u27Ascq, France), as well as two different users. The Raman data was subjected to PLS-DA and cross-validated via leave-one-patient out. The study\u27s findings suggest that the data acquired from the two Raman systems are 99% identical. However, the observed classification accuracy for the data obtained by user-1 was 92%, whereas by user-2 was 99%

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage