Native Fluorescence Spectroscopy of Blood Plasma in the Characterization of Oral Malignancy¶

Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Hyperspectral and multispectral imaging of cell and tissue autofluorescence is an emerging technology in which fluorescence imaging is applied to biological materials across multiple spectral channels. This produces a stack of images where each matched pixel contains information about the sample's spectral properties at that location. This allows precise collection of molecularly specific data from a broad range of native fluorophores. Importantly, complex information, directly reflective of biological status, is collected without staining and tissues can be characterised in situ, without biopsy. For oncology, this can spare the collection of biopsies from sensitive regions and enable accurate tumour mapping. For in vivo tumour analysis, the greatest focus has been on oral cancer, whereas for ex vivo assessment head-and-neck cancers along with colon cancer have been the most studied, followed by oral and eye cancer. This review details the scope and progress of research undertaken towards clinical translation in oncology.

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning

While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy and machine learning for the rapid and accurate identification of patients with cholecystitis. Significant differences were observed between the fluorescence spectral intensities of the serum of cholecystitis patients (n = 74) serum and those of healthy subjects (n = 71) at 455, 480, 485, 515, 625 and 690 nm. The ratios of characteristic fluorescence spectral peak intensities were first calculated, and principal component analysis (PCA)-linear discriminant analysis (LDA) and PCA-support vector machine (SVM) classification models were then constructed using the ratios as variables. Compared with the PCA-LDA model, the PCA-SVM model displayed better diagnostic performance in differentiating cholecystitis patients from healthy subjects, with an overall accuracy of 96.55%. This exploratory study showed that serum fluorescence spectroscopy combined with the PCA-SVM algorithm has significant potential for the development of a rapid cholecystitis screening method.

Autofluorescence of blood and its application in biomedical and clinical research

Autofluorescence of blood has been explored as a label free approach for detection of cell types, as well as for diagnosis and detection of infection, cancer, and other diseases. Although blood autofluorescence is used to indicate the presence of several physiological abnormalities with high sensitivity, it often lacks disease specificity due to use of a limited number of fluorophores in the detection of several abnormal conditions. In addition, the measurement of autofluorescence is sensitive to the type of sample, sample preparation, and spectroscopy method used for the measurement. Therefore, while current blood autofluorescence detection approaches may not be suitable for primary clinical diagnosis, it certainly has tremendous potential in developing methods for large scale screening that can identify high risk groups for further diagnosis using highly specific diagnostic tests. This review discusses the source of blood autofluorescence, the role of spectroscopy methods, and various applications that have used autofluorescence of blood, to explore the potential of blood autofluorescence in biomedical research and clinical applications.

Fluorescence Liquid Biopsy for Cancer Detection Is Improved by Using Cationic Dendronized Hyperbranched Polymer

(1) Background: Biophysical techniques applied to serum samples characterization could promote the development of new diagnostic tools. Fluorescence spectroscopy has been previously applied to biological samples from cancer patients and differences from healthy individuals were observed. Dendronized hyperbranched polymers (DHP) based on bis(hydroxymethyl)propionic acid (bis-MPA) were developed in our group and their potential biomedical applications explored. (2) Methods: A total of 94 serum samples from diagnosed cancer patients and healthy individuals were studied (20 pancreatic ductal adenocarcinoma, 25 blood donor, 24 ovarian cancer, and 25 benign ovarian cyst samples). (3) Results: Fluorescence spectra of serum samples (fluorescence liquid biopsy, FLB) in the presence and the absence of DHP-bMPA were recorded and two parameters from the signal curves obtained. A secondary parameter, the fluorescence spectrum score (FSscore), was calculated, and the diagnostic model assessed. For pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, the classification performance was improved when including DHP-bMPA, achieving high values of statistical sensitivity and specificity (over 85% for both pathologies). (4) Conclusions: We have applied FLB as a quick, simple, and minimally invasive promising technique in cancer diagnosis. The classification performance of the diagnostic method was further improved by using DHP-bMPA, which interacted differentially with serum samples from healthy and diseased subjects. These preliminary results set the basis for a larger study and move FLB closer to its clinical application, providing useful information for the oncologist during patient diagnosis.

Spectroscopic whole-blood indicators of end-stage renal disease and the hemodialysis treatment

The diffuse reflection spectrum in the 500-1670 nm region for whole blood taken from healthy subjects and end-stage renal disease (ESRD) patients was measured to test the feasibility of optically monitoring ESRD and its treatment by hemodialysis. Spectral regions where optical absorption significantly differed between healthy subjects and ESRD patients were used to form a multiple linear discriminant classification model. With this model a total of 41 whole-blood samples were classified into healthy, pretreatment and posttreatment ESRD classes. 96.7% of original and cross-validated cases and 100% of independent validation cases were correctly classified, indicating ESRD and its treatment exhibit characteristic spectral features in whole blood. Upon comparison of the discriminant model variables with a few key clinical blood parameters, model variables were found to significantly correlate with hematocrit and plasma levels of urea and potassium (P<0.05). The results of this study suggest that the optical signature of whole blood conveys basic clinical status information, and provides a path for investigating improved indices of hemodialysis toxicity, adequacy and patient outcome.