Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

Among different radiotherapy techniques, proton irradiation is an established and effective method for treatment of several types of cancer, because less healthy tissue is exposed with respect to conventional radiotherapy by photons/electrons. Recently, proton therapy has been proposed for the treatment of breast cancer. In vitro studies of proton irradiated normal human breast cells can provide information about cellular radioresponse, particularly as far as healthy tissue is concerned. In this paper, a study of the effects at different time points, following proton irradiation at different doses, of human normal MCF10A breast cells is performed by Raman spectroscopy. The aim of this investigation is to detect the unwanted effects of proton treatment and to investigate the possibility of monitoring them and of making an assessment of the cellular sensitivity by means of such a technique. The obtained results seem to indicate a rather significant sensitivity of MCF10A cells to proton irradiation. In fact, even at doses as low as 0.5 Gy, biological effects are clearly detectable in Raman spectra. In particular, ratiometric analysis of the Raman spectra measured from the nucleoplasm compartment showed that DNA/RNA damage increases with time, suggesting that most cells are unable to repair DNA/RNA broken bonds. The results obtained by the Raman spectroscopy analysis exhibit a similar trend with regard to dose to those obtained by commonly used radiobiological assays (i.e. MTT, clonogenic assay, senescence, apoptosis and necrosis). The results of this study strongly suggest the possibility that the Raman technique can be used to identify molecular markers predicting radiation response.

Fourier transform infrared micro-spectroscopy (μ-FT-IR) is nowadays considered a valuable tool for investigating the changes occurring in human cells after exposure to ionizing radiation. Recently, considerable attention has been devoted to the use of this optical technique in the study of cells exposed to proton beams, that are being increasingly adopted in cancer therapy. Different experimental configurations are used for proton irradiation and subsequent spectra acquisition. To facilitate the use of μ-FT-IR, it may be useful to investigate new experimental approaches capable of speeding up and simplifying the irradiation and measurements phases. Here, we propose the use of low-e-substrates slides for cell culture, allowing the irradiation and spectra acquisition in transflection mode in a fast and direct way. In recent years, there has been a wide debate about the validity of these supports, but many researchers agree that the artifacts due to the presence of the electromagnetic standing wave effects are negligible in many practical cases. We investigated human normal breast cells (MCF-10 cell line) fixed immediately after the irradiation with graded proton radiation doses (0, 0.5, 2, and 4 Gy). The spectra obtained in transflection geometry showed characteristics very similar to those present in the spectra acquired in transmission geometry and confirm the validity of the chosen approach. The analysis of spectra indicates the occurrence of significant changes in DNA and lipids components of cells. Modifications in protein secondary structure are also evidenced.

Fluorinated nanocrystalline silicon films, nc‐Si:H,F, have been deposited from SiF₄–SiH₄–H₂ mixtures by means of the plasma enhanced chemical vapor deposition technique. The presence of fluorine atoms, which are effective etchant species, promotes selective etching giving nanocrystalline films. These materials, with grain size of 100–200 Å, show a room temperature photoluminescence centered at 1.62 eV. Also, the widening of the optical energy gap (E_g=2.12 eV) is mainly due to the presence of nanocrystals rather than to the H content of 4.5 at. %.