Raman spectroscopy with a 1064-nm wavelength laser as a potential molecular tool for prostate cancer diagnosis: a pilot study

Investigating the Growth of Pseudomonas aeruginosa and Its Influence on Osteolysis in Human Bone: An In Vitro Study

Background

Isolation of the causal microorganisms in osteomyelitis presents a major challenge for treating clinicians. Several methods have been proposed to rapidly and accurately identify microorganisms. There has been an increasing interest in using Raman spectroscopy in the field of microbial detection and characterisation. This paper explores the use of Raman spectroscopy identification as one of the most difficult-to-isolate microorganisms causing osteomyelitis.

Methods and results

Fresh healthy human bone samples were collected from patients undergoing a total knee replacement. These samples were then inoculated with fresh overnight Pseudomonas aeruginosa (PAO) cultures. Bacteria growth and bone ultrastructural changes were monitored over a period of 6 weeks. The experiment demonstrated ultrastructural bony destruction caused by osteolytic PAO secretions. Raman-specific spectral signatures related to the cellular membranes of PAO structures were spotted indicating survival of bacteria on the bone surface.

Conclusion

This study showed the promising ability of Raman spectroscopy to identify the presence of bacteria on the surface of inoculated bone samples over time. It was able to detect the osteolytic activity of the bacteria as well as ultrastructure specific to PAO virulence. This method may have a role as an aid to existing diagnostic methods for fast and accurate bacterial identification in bone infection.

How to cite this article

Al Ghaithi A, Al Bimani A, Al Maskari S. Investigating the Growth of Pseudomonas aeruginosa and Its Influence on Osteolysis in Human Bone: An In Vitro Study. Strategies Trauma Limb Reconstr 2021;16(3):127–131.

Using the Method of "Optical Biopsy" of Prostatic Tissue to Diagnose Prostate Cancer

Simple Summary

Analytical discrimination models of Raman spectra of prostate cancer tissue were constructed by using the projections onto latent structures data analysis (PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers, and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid.

Abstract

The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.

Rapid identification of cervical adenocarcinoma and cervical squamous cell carcinoma tissue based on Raman spectroscopy combined with multiple machine learning algorithms

Cervical cancer has a long latency, and early screening greatly reduces mortality. In this study, cervical adenocarcinoma and cervical squamous cell carcinoma tissue data were collected by Raman spectroscopy, and then, the adaptive iteratively reweighted penalized least squares (airPLS) algorithm and Vancouver Raman algorithm (VRA) were used to subtract the background of the collected data. The following five feature extraction algorithms were applied: partial least squares (PLS), principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (isomap) and locally linear embedding (LLE). The k-nearest neighbour (KNN), extreme learning machine (ELM), decision tree (DT), backpropagation neural network (BP), genetic optimization backpropagation neural network (GA-BP) and linear discriminant analysis (LDA) classification models were then established through the features extracted by different feature extraction algorithms. In total, 30 types of classification models were established in this experiment. This research includes eight good models, airPLS-PLS-KNN, airPLS-PLS-ELM, airPLS-PLS-GA-BP, airPLS-PLS-BP, airPLS-PLS-LDA, airPLS-PCA-KNN, airPLS-PCA-LDA, and VRA-PLS-KNN, whose diagnostic accuracy was 96.3 %, 95.56 %, 95.06 %, 94.07 %, 92.59 %, 85.19 %, 85.19 % and 85.19 %, respectively. The experimental results showed that the model established in this article is simple to operate and highly accurate and has a good reference value for the rapid screening of cervical cancer.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.