“It still haunts me whether we did the right thing”: a qualitative analysis of free text survey data on the bereavement experiences and support needs of family caregivers

Background Research suggests that there may be bereavement experiences and support needs which are specific to family caregivers providing end of life care (EoLC), although this remains an under-researched area. This paper focuses on themes relating to bereavement which were derived from an analysis of free text survey responses collected in a research priority setting exercise for palliative and EoLC. Methods The priority setting exercise involved a public survey, designed to generate research priorities. Rather than identify research topics, many people instead described their experiences and raised more general questions relating to palliative and end of life care. To explore these experiences and perspectives a supplementary thematic analysis was conducted on the survey responses. 1403 respondents took part, including patients, current and bereaved carers, health and social care professionals, volunteers and members of the public. Results Several grief issues were identified, which seem specific to the experiences of family caregivers. Responses demonstrated a relationship between death experiences, feelings of guilt and bereavement outcomes for some family caregivers, as well as caregiver experiences of a “void” created by the withdrawal of professional support after death. Communication and support needs were also identified by participants. Conclusion This analysis provides further evidence of some of the specific effects that caring for a loved one at the end of life can have on bereavement experiences. Finding ways of improving communication around the time of death and effective follow up approaches post death could help to address some of these issues

Raman Spectroscopic Characterisation of Non Stimulated and Stimulated Human Whole Saliva

Human saliva is a unique biofluid which can reflect the physiopathological state of an individual. The wide spectrum of molecules present in saliva, compounded by the close association of salivary composition to serum metabolites, can provide valuable information for clinical diagnostic applications through highly sensitive vibrational spectroscopic techniques such as Raman spectroscopy. However, the nature of saliva, in terms of collection and patient-related characteristics, can be considered factors which may strongly affect the Raman spectral profile of salivary samples and disrupt the search for specific salivary biomarkers in the detection of diseases. The main objective of this study was to highlight spectral features associated with the type of collection in an intra- and inter-patient approach. Saliva was collected using both stimulated and non-stimulated approaches from 20 donors, concentrated by centrifugal filtration and further analysed using Raman spectroscopy. The methodology adopted for liquid saliva showed consistency in the qualitative analysis of the groups, confirming the reproducibility of this Raman spectroscopic approach. Using principal component analysis (PCA) and partial least squares – discriminant analysis (PLSDA), non stimulated saliva could be differentiated from stimulated saliva in both intra- and inter-patient analysis, with a classification efficiency of 77 and 87%, respectively. The bicinchoninic acid (BCA) assay showed a similar trend in terms of total protein concentration, showing a slight increase in stimulated saliva samples. These results are valuable in the process of developing and establishing Raman spectroscopy as a novel diagnostic tool in the future as well as controlling variability, in order to determine specific spectroscopic markers related to a multifactorial disease for diagnostic or follow-up purposes

Vibrational Microspectroscopy for Cancer Screening

Vibrational spectroscopy analyses vibrations within a molecule and can be used to characterise a molecular structure. Raman spectroscopy is one of the vibrational spectroscopic techniques, in which incident radiation is used to induce vibrations in the molecules of a sample, and the scattered radiation may be used to characterise the sample in a rapid and non-destructive manner. Infrared (IR) spectroscopy is a complementary vibrational spectroscopic technique based on the absorption of IR radiation by the sample. Molecules absorb specific frequencies of the incident light which are characteristic of their structure. IR and Raman spectroscopy are sensitive to subtle biochemical changes occurring at the molecular level allowing spectral variations corresponding to disease onset to be detected. Over the past 15 years, there have been numerous reports demonstrating the potential of IR and Raman spectroscopy together with multivariate statistical analysis techniques for the detection of a variety of cancers including, breast, lung, brain, colon, oral, oesophageal, prostate and cervical cancer. This paper discusses the recent advances and the future perspectives in relation to cancer screening applications, focussing on cervical and oral cancer

The Potential of Vibrational Spectroscopy in the Early Detection of Cervical Cancer: an Exciting Emerging Field

The application of vibrational spectroscopy to disease diagnosis is a relatively new, rapidly evolving scientific field. Techniques such as Raman and infrared spectroscopy have shown great promise in this regard over the past number of years. This study directly compared Raman spectroscopy and synchrotron infrared (SR-IR) spectroscopy on parallel cervical cancer samples. Both frozen and dewaxed formalin fixed paraffin preserved tissue sections were examined. Both tissue types produced good quality Raman and SR-IR spectra, although the lesser processed, frozen tissue sections displayed the most detailed spectra. Spectroscopy was shown capable of discriminating between different cell types in normal cervical tissue. Spectra recorded from invasive carcinoma showed a marked difference from those recorded from normal cervical epithelial cells. Spectral differences identified with the onset of carcinogenesis include increased nucleic acid contributions and decreased glycogen levels. These investigations pave the way for an enlarged study into this exciting new diagnostic field

Selection of Preprocessing Methodology for Multivariate Regression of Cellular FTIR and Raman Spectra in Radiobiological Analyses

Vibrational spectra of biological species suffer from the influence of many extraneous interfering factors that require removal through preprocessing before analysis. The present study was conducted to optimise the preprocessing methodology and variable subset selection during regression of and confocal Raman microspectroscopy (CRM) and Fourier Transform Infrared microspectroscopy (FTIRM) spectra against ionizing radiation dose. Skin cells were γ-irradiated in-vitro and their Raman and FTIRM spectra were used to retrospectively predict the radiation dose using linear and nonlinear partial least squares (PLS) regression algorithms in addition to support vector regression (SVR). The optimal preprocessing methodology (which comprised combinations of spectral filtering, baseline subtraction, scaling and normalization options) was selected using a genetic algorithm (GA) with the root mean squared error of prediction (RMSEP) used as the fitness criterion for selection of the preprocessing chromosome (where this was calculated on an independent set of test spectra randomly selected from the dataset on each pass of the algorithm). The results indicated that GA selection of the optimal preprocessing methodology substantially improved the predictive capacity of the regression algorithms over baseline methodologies, although the optimal preprocessing chromosomes were similar for various regression algorithms, suggesting an optimal preprocessing methodology for radiobiological analyses with biospectroscopy. Feature selection of both FTIRM and CRM spectra using genetic algorithms and multivariate regression provided further decreases in RMSEP, but only with non-linear multivariate regression algorithms

Raman Spectroscopic Analysis of Oral Squamous Cell Carcinoma and Oral Dysplasia in the High-Wavenumber Region

Raman spectroscopy can provide a molecular-level signature of the biochemical composition and structure of cells with excellent spatial resolution and could be useful to monitor changes in composition for early stage and non-invasive cancer diagnosis, both ex-vivo and in vivo. In particular, the fingerprint spectral region (400–1,800 cm-1) has been shown to be very promising for optical biopsy purposes. However, limitations to discrimination of dysplastic and inflammatory processes based on the fingerprint region still persist. In addition, the Raman spectral signal of dysplastic cells is one important source of misdiagnosis of normal versus pathological tissues. The high wavenumber region (2,800–3,600 cm-1) provides more specific information based on N-H, O-H and C-H vibrations and can be used to identify the subtle changes which could be important for discrimination of samples. In this study, we demonstrate the potential of the high-wavenumber spectral region by collecting Raman spectra of nucleoli, nucleus and cytoplasm from oral epithelial cancer (SCC-4) and dysplastic (DOK) cell lines and from normal oral epithelial primary cells, in vitro, which were then analyzed by area under the curve as a method to discriminate the spectra. In this region, we will show the discriminatory potential of the CHvibrational modes of nucleic acids, proteins and lipids. This technique demonstrated more efficient discrimination than the fingerprint region when we compared the cell cultures

Raman Micro Spectroscopy Study of the Interaction of Vincristine with A549 Cells Supported by Expression Analysis of bcl-2 Protein

Understanding the interaction of anticancer drugs with model cell lines is important to elucidate the mode of action of these drugs as well as to develop cost effective and rapid screening methods. Raman spectroscopy has been demonstrated to be a valuable technique for high throughput, noninvasive analysis. The interaction of vincristine with a human lung adenocarcinoma cell line (A549)was investigated using Raman micro spectroscopy. The results were correlated with parallel measurements from the MTT cytotoxicity assay, which yielded an IC50 value of 0.10 ± 0.03 μM. The Raman spectral data acquired from vincristine treated A549 cells was analysed to understand its interaction with the nucleus in the cell and elucidate DNA intercalation. The dose dependent spectral changes in the nucleus are analysed by PLS-Jack knifing for the identification of the more significant changes associated with the mode of action of the drug. Results are correlated with a similar dose dependent expression analysis of the bcl-2 protein, an anti-apoptotic protein associated with DNA damage, in the vincristine treated A549 cells using flow cytometry. The results indicate the co-existence of two modes of action, microtubule binding at low doses and DNA intercalation at high doses

The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter and intra observer and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%