Spontaneous and coherent anti-Stokes Raman spectroscopy of human gastrocnemius muscle biopsies in CH-stretching region for discrimination of peripheral artery disease

Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence

Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm-1. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.

Aortic aneurysm evaluation by scanning acoustic microscopy and Raman spectroscopy

Aortic aneurysm is observed as a result of the extensive alteration in the elasticity of the aortic wall due to the breakdown of elastin and collagen. In this study, we studied the feasibility of scanning acoustic microscopy (SAM) and Raman spectroscopy (RS) in characterizing the dilated segments of the aorta from male and female patients with aortic aneurysm. SAM determined the acoustic property variation in the aorta by calculating the acoustic impedance values of aorta samples of 18 patients. RS determined the disease states by analyzing the chemical variation especially in the peaks related to elastin and collagen using the k-means classification method. Consequently, we assume that combining these two techniques in clinics will help to investigate the dilated segment of the aorta with micrometer resolution, which will reduce the possibility of new aneurysm formation due to a segment not excised during the surgery.

Nonlinear Optical Characterization of 2D Materials

Characterizing the physical and chemical properties of two-dimensional (2D) materials is of great significance for performance analysis and functional device applications. As a powerful characterization method, nonlinear optics (NLO) spectroscopy has been widely used in the characterization of 2D materials. Here, we summarize the research progress of NLO in 2D materials characterization. First, we introduce the principles of NLO and common detection methods. Second, we introduce the recent research progress on the NLO characterization of several important properties of 2D materials, including the number of layers, crystal orientation, crystal phase, defects, chemical specificity, strain, chemical dynamics, and ultrafast dynamics of excitons and phonons, aiming to provide a comprehensive review on laser-based characterization for exploring 2D material properties. Finally, the future development trends, challenges of advanced equipment construction, and issues of signal modulation are discussed. In particular, we also discuss the machine learning and stimulated Raman scattering (SRS) technologies which are expected to provide promising opportunities for 2D material characterization.

Subcutaneous and transcutaneous monitoring of murine hindlimb ischemia by in vivo Raman spectroscopy

We have investigated the development of murine hindlimb ischemia from day 1 to day 55 after femoral artery ligation (FAL) using blood flow analysis, functional tests, histopathological staining, and in vivo Raman spectroscopy. FAL resulted in hindlimb blood deprivation and the loss of functionality as attested by the blood flow analysis and functional tests, respectively. The limbs recovered a normal circulation progressively without recovering complete functionality. Histological analysis showed changes in the morphology of muscle fibers with intense inflammation. From day 22 to day 55 post-ischemia, regeneration of the myofibers was observed. Raman spectroscopic results related to subcutaneous analysis made the identification of modification in the biochemical constituents of hindlimb muscles possible during disease progression. Ischemia was characterized by a quantitative increase in the lipid content and a decrease in the protein content. The lipid to protein ratio can be used as a spectroscopic marker to score the severity of ischemia. Multivariate statistical analysis PC-LDA (Principal Component-Linear Discriminant Analysis) was used to classify all the data measured for the normal and ischemic tissues. This classification illustrated an excellent separation between the control and ischemic tissues at any time during the course of ischemic development. In vivo Raman spectroscopy was then applied to assess the potential of this technique as a screening tool to explore an ischemic disease non-invasively (transcutaneously). For this purpose, the influence of skin on the diagnostic accuracy was evaluated; transcutaneous analysis revealed the accuracy of this technique, indicating its potential in the in situ monitoring of muscle structural changes during ischemia.

Label-free characterization of exosome via surface enhanced Raman spectroscopy for the early detection of pancreatic cancer

Pancreatic cancer is a highly lethal malignancy. Lack of early diagnostic markers makes timely detection of pancreatic cancer a highly challenging endeavor. Exosomes have emerged as information-rich cancer specific biomarkers. However, characterization of tumor-specific exosomes has been challenging. This study investigated the proof of principle that exosomes could be used for the detection of pancreatic cancer. Label-free analysis of exosomes purified from normal and pancreatic cancer cell lines was performed using surface enhanced Raman Spectroscopy (SERS) and principal component differential function analysis (PC-DFA), to identify tumor-specific spectral signatures. This method differentiated exosomes originating from pancreatic cancer or normal pancreatic epithelial cell lines with 90% accuracy. The cell line trained PC-DFA algorithm was next applied to SERS spectra of serum-purified exosomes. This method exhibited up to 87% and 90% predictive accuracy for HC and EPC individual samples, respectively. Overall, our study identified utility of SERS spectral signature for deciphering exosomal surface signature.

Raman Spectroscopy and Microscopy Applications in Cardiovascular Diseases: From Molecules to Organs

Noninvasive and label-free vibrational spectroscopy and microscopy methods have shown great potential for clinical diagnosis applications. Raman spectroscopy is based on inelastic light scattering due to rotational and vibrational modes of molecular bonds. It has been shown that Raman spectra provide chemical signatures of changes in biological tissues in different diseases, and this technique can be employed in label-free monitoring and clinical diagnosis of several diseases, including cardiovascular studies. However, there are very few literature reviews available to summarize the state of art and future applications of Raman spectroscopy in cardiovascular diseases, particularly cardiac hypertrophy. In addition to conventional clinical approaches such as electrocardiography (ECG), echocardiogram (cardiac ultrasound), positron emission tomography (PET), cardiac computed tomography (CT), and single photon emission computed tomography (SPECT), applications of vibrational spectroscopy and microscopy will provide invaluable information useful for the prevention, diagnosis, and treatment of cardiovascular diseases. Various in vivo and ex vivo investigations can potentially be performed using Raman imaging to study and distinguish pathological and physiological cardiac hypertrophies and understand the mechanisms of other cardiac diseases. Here, we have reviewed the recent literature on Raman spectroscopy to study cardiovascular diseases covering investigations on the molecular, cellular, tissue, and organ level.