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

REverse transcriptase ACTivity (REACT) assay for point-of-care measurement of established and emerging antiretrovirals for HIV treatment and prevention

Maintaining adequate levels of antiretroviral (ARV) medications is crucial for the efficacy of HIV treatment and prevention regimens. Monitoring ARV levels can predict or prevent adverse health outcomes like treatment failure or drug resistance. However, conventional ARV measurement using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is slow, expensive, and centralized delaying clinical and behavioral interventions. We previously developed a rapid enzymatic assay for measuring nucleotide reverse transcriptase inhibitors (NRTIs) - the backbone of HIV treatment and prevention regimens - based on the drugs' termination of DNA synthesis by HIV reverse transcriptase (RT) enzyme. Here, we expand our work to include non-nucleoside reverse transcriptase inhibitors (NNRTIs) - an ARV class used in established and emerging HIV treatment and prevention regimens. We demonstrate that the REverse Transcriptase ACTivity (REACT) assay can detect NNRTIs including medications used in oral and long-acting/extended-release HIV treatment and prevention. We demonstrate that REACT can measure NNRTIs spiked in either buffer or diluted plasma and that fluorescence can be measured on both a traditional plate reader and an inexpensive portable reader that can be deployed in point-of-care (POC) settings. REACT measured clinically relevant concentrations of five NNRTIs spiked in aqueous buffer. REACT measurements showed excellent agreement between the plate reader and the portable reader, with a high correlation in both aqueous buffer (Pearson's r = 0.9807, P < 0.0001) and diluted plasma (Pearson's r = 0.9681, P < 0.0001). REACT has the potential to provide rapid measurement of NNRTIs in POC settings and may help to improve HIV treatment and prevention outcomes.

Characterization and classification of pathogenic bacteria using native fluorescence and spectral deconvolution

Identification and classification of pathogenic bacterial strains is of current interest for the early treatment of diseases. In this work, protein fluorescence from eight different pathogenic bacterial strains were characterized using steady state and time resolved fluorescence spectroscopy. The spectral deconvolution method was also employed to decompose the emission contribution from different intrinsic fluorophores and extracted various key parameters, such as intensity, emission maxima, emission line width of the fluorophores, and optical redox ratio. The change in average lifetime values across different bacterial strains exhibits good statistical significance (p ≤ 0.01). The variations in the photophysical characteristics of bacterial strains are due to the different conformational states of the proteins. The stepwise multiple linear discriminate analysis of fluorescence emission spectra at 280 nm excitation across eight different bacterial strains classifies the original groups and cross validated group with 100% and 99.5% accuracy, respectively.

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.