This PDF file contains the front matter associated with SPIE Proceedings Volume 12373, including the Title Page, Copyright information, Table of Contents and Conference Committee lists.

Time-resolved Raman and fluorescence lifetime spectroscopy imaging yields new research insights with great potential in applications including biomedical diagnostics, carbon materials, and battery development. Single Photon Avalanche Diode (SPAD) arrays are ideal for such applications and we present to our knowledge the first time-resolved Raman images obtained with such sensors. Utilizing motorized and confocal scanning configurations we obtain near shot-noise limited performance, room temperature operation, millisecond spectral acquisition times, and simultaneous acquisition and discrimination of Raman and fluorescence with high spectral resolution and range. Detailed images and spectra from samples including calcite, diamond, and single-wall carbon nanotubes demonstrate the possibility of high-resolution time-resolved Raman and fluorescence imaging.

Amyotrophic lateral sclerosis (ALS) is a type of motor neuron disease that results in paralysis and death from a progressive loss of both upper and lower motor neurons. Emerging studies have indicated that excess production of reactive oxygen species (ROS) in dysfunctional mitochondria in addition to an inefficient antioxidant defense may contribute to the progression of the disease. L-methionine (Met) plays important roles in regulating cellular metabolism and activating endogenous antioxidant enzymes. We hypothesized that excess methionine treatment can provide ALS patients with an efficient antioxidant defense mechanism to control their disease. Here, we apply an optical imaging approach that combines deuterium oxide (D2O)-probed stimulated Raman scattering (DO-SRS) and two photon excitation fluorescence (2PEF) microscopies to directly image the effects of methionine-enriched diet on oxidative imbalance and cellular metabolism in neurodegenerative cells. Our preliminary data revealed that excess methionine increases syntheses of lipid and unsaturated lipid membranes. Meanwhile, the same diet decreases protein synthesis and oxidative imbalance. Our study suggests that excess methionine can provide a protective mechanism against oxidative imbalance and promote cellular repair in neurodegenerative diseases.

By combining biosensor microscopy that provides high contrast for detecting individual biomolecules with novel biochemistry methods that can effectively turn each target molecule into many digitally-counted sensing events, it is possible to achieve attomolar-scale limits of detection for cancer-specific nucleic acid target molecules (miRNA and ctDNA) while simultaneously obtaining thousands-to-one selectivity against single base variants. The presentation will describe the use of photonic metamaterials and associated detection instruments to amplify optical absorption and fluorescence emission. The biodetection technology platforms are used to perform assays using nucleic acid strand displacement reactions and CRISPR/Cas chemistry to sense target biomarkers from complex media. The “amplify-then-digitize” approach represents a new and powerful paradigm for molecular diagnostics, compared to the “digitize-then amplify” approach utilized in methods such as droplet digital PCR (ddPCR). Overall, we seek simple, rapid, room temperature, single-step assay methods that can be operate with small, inexpensive, and robust detection systems for applications in point of care diagnostics, laboratory-based diagnostics, and life science research applications.

The gold standard diagnosis of oral pre-cancer/cancer is an invasive biopsy followed by a histological examination, which may present psychological trauma and risk of infection to patients. Moreover, multiple biopsies are often required to monitor precancer lesions' progression. With the aim of reducing the need for multiple biopsies, this study was undertaken to identify pre-cancerous lesions using minimally invasive brush biopsy and saliva samples, analysed by Raman spectroscopy.

Native fluorescence spectra of retinoic acid (RA)-treated and untreated human breast cancer cells were measured using selective wavelengths of 300 nm and 340 nm for excitation. The spectral data of the two types of cells were analyzed using machine learning algorithms for linear unmixing and classification which yielded high accuracy. The results show that the concentrations of the native fluorophores such as tryptophan, NADH and flavins in the human malignant breast cells change when they are treated with RA. The study shows the dual-wavelength fluorescence spectroscopy aided by machine learning has potential clinical applications in drug development and chemotherapeutic studies.

Optical biopsy methods, which consists of analysing the response of tissue to light excitation, are being increasingly used in recent years for the diagnosis of skin pathologies. At the same time, the use of multimodal methods often significantly increases diagnostic efficiency as well as extending the limits of applicability of the methods. This contribution presents the results of in vivo analysis of precancerous and benign skin conditions (compensatory hyperplasia, atypical hyperplasia and dysplasia) in mice preclinical model, based on bimodal spectroscopic data, including multiply excited autofluorescence with 7 autofluorescence excitation wavelengths in the 360-430 nm range and diffuse reflectance spectroscopy with xenon lamp, that emits mainly in the 300-800 nm spectral range, as a source. The instrument used in this study provided the ability to collect spectra in the spectral range 317 - 789 nm at three different source-detector separations: 271, 536 and 834 μm. The results were processed using machine learning methods (principal component analysis, support vector machine, linear discriminant analysis, artificial neural network) and then various data fusion methods (Stacking, Begging, Boosting, Voting) were implemented to combine the results of analysis of all the modalities. This study presents a comparison of the performance of these data fusion methods. The results obtained in this work can be further applied to the diagnosis of carcinoma using optical biopsy methods.

Breast cancer is most common cancer among women world-wide and can be treated if diagnosed at early-stage. Fluorescence (FL) techniques have a considerable impact in tumor detection as FL is highly sensitive to biochemical and biophysical structure of the tissues, providing novel techniques for early and noninvasive diagnosis of cancer. Due to high sensitivity of FL, it can be used in early-stage breast cancer detection. Fluorescence-guided-surgery (FGS) using exogenous agent in breast cancer is a well-known method, which locates tumor and margins during intra-operative procedure. FL-spectroscopy and FL-imaging has shown the potentiality in FGS of breast cancer independently. Combination of these two modalities in a single system improves the success rate of FGS which can give tumor free tissues. The goal of the current study is to analyze and classify the early-stage breast cancer, locally advanced breast cancer (invasive ductal carcinoma) and normal tissue. For that, we combine FL-imaging and FL-spectroscopy in a single smartphone-based point-of-care devices and recorded data during intra-operative procedure. A total 21 patients of invasive ductal carcinoma and fibroadenoma are included in this study. Total 65 FL-spectra are recorded during intra-operative procedure which are further used in support vector machine (SVM) based classification of fibroadenoma, invasive ductal carcinoma (IDC) and normal tissue. The sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy (total efficiency) is 78.6 %, 90%, 91.6%, 75%, 95%, respectively. Additionally, we observed a red shift in case of IDC and fibroadenoma from normal tissue, which is 5.22±1.77 nm, and 4.96±2.61 nm, respectively.

Melanoma skin cancer is one of the most dangerous types of cancer and responsible for more than half of the skin cancer-related deaths. Common methods for skin cancer detection are dermoscopy and visual inspection, the accuracy of which is greatly influenced by the training and experience of the physician. In some extreme cases, histological findings, which are considered the gold standard for detecting skin cancer, might also cause a debate among competent clinicians. To achieve accurate non-invasive skin cancer diagnostics, a dual-mode optical biopsy system made up of a Raman spectrometer and an OCT system was built. OCT morphological images enable precise localization of the position and depth of lesions for subsequent Raman detection. Skin models were generated by dissolving synthetic melanin in dimethyl sulfoxide (DMSO) and applying it to fresh porcine skin samples to investigate the influence of melanin concentration on skin spectra. The amide-I band and the CH2-deformation band detected by using Raman spectroscopy had a lower intensity in the spectra of the samples treated with melanin, which may imply that the addition of melanin promotes the breakdown of proteins and lipids. The intensities of the CH2 twist and C-C stretch bands increased compared to the drop of the aforesaid bands. Given that these two structures are often present in phospholipids, which are the fundamental components of cell membranes, one possible explanation is that melanin has less influence on membranes than on biological macromolecules.

The risk of large-scale radiological/nuclear events has notably increased in recent years. Biodosimetry is considered an essential tool for emergency management following such unplanned exposures to ionizing radiation. For example, by assessing an individual’s received dose to blood, biodosimetry can support medical screening and individual health management. Current biodosimetry techniques, such as the dicentric chromosome assay, are based on the analysis of chromosomal aberrations Although highly accurate, these methods are time-consuming and labour-intensive. We recently developed a new high-throughput approach based on Raman spectroscopy of blood combined with covariate-adjusted multivariate analysis for the detection of irradiated blood. We found that the protein bands in the Raman spectra were the main sources of discrimination between unirradiated (control) and irradiated blood. In this follow up work, we explored the application of Raman spectroscopy and multivariate analysis to blood plasma to avoid dominant hemoglobin contributions. Peripheral blood drawn from a healthy volunteer was irradiated at 0 (control), 5 and 20 Gy using 250 kV X-rays. After a 4 hour incubation time, plasma centrifuged from the blood sample was immediately frozen at -80 deg C. Raman measurements were performed in triplicate on thawed blood plasma samples. Partial least squares-discriminant analysis (PLS-DA) was utilized for multi-class differentiation between Raman spectra of 0, 5 and 20 Gy irradiated plasma. Sparse PLS-DA (sPLS-DA) provided improved dose classification after combining Raman spectral data from different batches. Biomarker information related to radiation-induced changes in blood plasma was also extracted from sPLS-DA. The outcomes of these initial studies highlight the value of Raman spectroscopy to support biodosimetry.

Alzheimer’s disease (AD) pathogenesis is widely believed to be associated with the production and deposition of the β-amyloid peptide (Aβ) and neurofibrillary tangles (NFTs) which are composed of a highly-phosphorylated form of the microtubule-associated protein tau. Based on the above hypothesis, there are currently no sufficiently effective technologies and drugs for early detection and treatment of AD. Even the most promising new drug Lecanemab that is based on an anti-amyloid monoclonal antibody therapy, has only partially slowed down the cognitive performance of patients with mild impairment caused by Alzheimer's disease. The main symptoms of AD brain tissue lesions in patients are the deposition of β-amyloid peptide and the hyperphosphorylation of tau protein, which aggregates the microtubule structure of neurons. Therefore, Aβ deposition and hyperphosphorylation of Tau are important pathological biomarkers of Alzheimer's disease. Therefore, the main targets of research for AD prevention, detection and pharmaceuticals are still Aβ and Tau protein. The aim of this study was to detect the changes of Aβ and Tau proteins in the mouse brain tissue with AD and control samples using Visible Resonance Raman (VRR) spectroscopic technology. An attempt was made to develop criteria for the detection of early AD lesions by optical spectroscopy technology. The VRR spectra of AD, the control mouse brain tissues, and Aβ and Tau proteins were recorded and analyzed. The AD and the control mouse brain tissue samples were selected from the thalamus, frontal lobe cortex and hippocampus brain areas. VRR technology with high spatial resolution and the resonance-enhanced features of certain protein molecules is first used in this study to detect and characterize the changes of Aβ and Tau proteins in AD mouse brain model. The optical spectroscopy biomarkers of AD and Control brain tissue were identified in fingerprint and the high-wavenumber regions. The Raman spectra of the secondary structure of protein in amide (I-II-III-B-A) are detected and analyzed. The results indicate that the intensity of Amide I decreased at the 1666 cm^-1 corresponding to the β-sheet structure, and the intensity of the amide III bands (1220- 1320 cm^-1) increased in all AD brain tissues. It was also observed that the Raman peaks of 1448 and 980 cm^-1 related to the abundance of proline, serine, and threonine at tau phosphorylation sites were significantly enhanced in the frontal lobe cortex and hippocampus of AD brain tissues. The intensity ratio biomarker of high phosphorylation in the high wavenumber range from 2898 to 2932 cm-1 increased in all AD brain tissues. Changes of protein secondary conformation and abnormally phosphorylated tau or tauopathies were observed. In summary, VRR is a sensitive tool for characterizing protein structural changes and monitoring the tau phosphorylation. It may potentially be used for early detection of AD.

There is interest in using photon entanglement in biomedical applications. In one application, polarization entangled photons pass through brain tissue. The effect of the brain tissue on the photon entanglement is measured via the decoherence that is imparted on the entangled state. Our current method to obtain a measure of the decoherence involves quantum state tomography, where a minimum of 16 measurements are used in conjunction with tomographic optimization to obtain the density matrix representing the state of the photons. In this work we report on a method to avoid tomographic optimization on behalf of a direct measurement of the elements of the density matrix. We make preliminary comparisons between the two methods.

Skin color varies depending upon the individual's melanin content; the more Ultraviolet (UV) radiation exposure, the more increases in melanin, and the darker the skin color. Protection from UV exposure is important because it is associated with the risk of pigmentation and skin cancer. Currently, Sun Protection Factor (SPF) and Protection of A (PA) indicated on sunscreen bottles are difficult to apply universally; a method of evaluating sunscreen performance that is customized to individual skins is required. This study confirmed that differences in skin color can be distinguished based on the pixels of images acquired with a UV camera; thus differences in the degree of UV protection could be analyzed according to skin color. The 36 skin sample images acquired with a UV camera were divided into 'Light', 'Medium', and 'Dark' according to the average pixel value of each image. A one-way analysis of variance (ANOVA) revealed a significant difference in the average pixel value between the three groups. The differences in average pixel value between the skin images immediately after applying sunscreen and 150 minutes after application was 3.91 in the ‘Light’, 3.27 in the ‘Medium’, and 3.65 in the ‘Dark’ group.

Poor recovery of peripheral neuron damage caused by trauma is currently among the most common causes for low quality of life among US population. Disabilities caused by neuron damage and poor recovery tend to cause early workforce retirement, creating a gap in skilled workforce in the US. Recent studies suggest that biomechanical vectors play an important role in nerve recovery and functionality restoration. This preliminary study focuses on investigating of correlation between biomechanical properties of murine sciatic nerve and Brillouin elasticity data. Combined Brillouin-Raman spectroscopy was used as a label-free method of elasticity measurement.

Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468 − 787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.