In-vivo Raman spectroscopy: from basics to applications

Reverse confocal polarized Raman spectroscopy (RCPRS) for tissue analysis

Confocal Raman spectroscopy (CRS) being one of the most widely used depth-sensitive techniques for measuring layer wise Raman characteristics of layered biological tissues faces two practical problems. First, the overall probing depth is limited in a given optical design of the CRS system and second, the objective lens used for focusing touches to surface of the target sample during probing of deeper sub-surface layers. To facilitate deeper probing in a given CRS system and to avoid lens-sample touching issue, an alternate scheme of depth-sensitive Raman measurement is presented. The scheme, named reverse confocal polarized Raman spectroscopy (RCPRS), uses an experimental arrangement of plane polarized illumination and orthogonal polarized detection in which depth-sensitive measurements are performed by moving the focal plane of the illumination beam away from the tissue surface unlike to CRS which obtains depth-separation by traversing across different depths of the target tissue. The performance of the RCPRS is evaluated using a non-biological phantom and a biological tissue. It is found that the introduction of polarization reduces the interference of the signals originating from the layers surrounding the target layer and thereby improving the depth-selectivity.

Raman analysis of lipids in cells: Current applications and future prospects

Lipids play an important role in the regulation of cell life processes. Although there are various lipid detection methods, Raman spectroscopy, a non-invasive technique, provides the detailed chemical composition of lipid profiles without a complex sample preparation procedure and possesses greater potential in basic biology, clinical diagnosis and disease therapy. In this review, we summarized the characteristics and advantages of Raman-based techniques and their primary contribution to illustrating cellular lipid metabolism.

Comparative Study Into the Effect of Detector Noises and Sensitivity on the Serum SERS Analysis: Example of Non-Communicable Diseases Discrimination

The aim of the study is to compare the performance of surface-enhanced Raman spectroscopy (SERS) analysis of serum using a non-cooled detector (EnSpectr R785) and a high spectral resolution detector (Renishaw) in the task of discrimination between the patients with chronic heart failure and the patients with chronic obstructive pulmonary disease. The SERS-based solution of the classification problem demonstrates an insignificant relationship between the disease classification accuracy and detector quality (classification accuracy for the high-resolution setup is 0.84 and for the low-cost setup is 0.81). In the data recorded on Renishaw setup, the most significant bands are 611, 675, 720, 804, 1187, 1495, and 1847 cm-1; for the EnSpectr setup-721, 1051, and 1665 cm-1. The results have revealed equal discrimination capabilities in the EnSpectr and the Renishaw setups; however, the Renishaw setup has more prospects for identifying the spectral contribution of pathologically associated analytes.

Guidance on biomaterials for periodontal tissue regeneration: Fabrication methods, materials and biological considerations

Regeneration of the multiple tissues and interfaces in the periodontal complex necessitates multidisciplinary evaluation to establish structure/function relationships. This article, an initiative of the Academy of Dental Materials, provides guidance for performing chemical, structural, and mechanical characterization of materials for periodontal tissue regeneration, and outlines important recommendations on methods of testing bioactivity, biocompatibility, and antimicrobial properties of biomaterials/scaffolds for periodontal tissue engineering. First, we briefly summarize periodontal tissue engineering fabrication methods. We then highlight critical variables to consider when evaluating a material for periodontal tissue regeneration, and the fundamental tests used to investigate them. The recommended tests and designs incorporate relevant international standards and provide a framework for characterizing newly developed materials focusing on the applicability of those tests for periodontal tissue regeneration. The most common methods of biofabrication (electrospinning, injectable hydrogels, fused deposition modelling, melt electrowriting, and bioprinting) and their specific applications in periodontal tissue engineering are reviewed. The critical techniques for morphological, chemical, and mechanical characterization of different classes of materials used in periodontal regeneration are then described. The major advantages and drawbacks of each assay, sample sizes, and guidelines on specimen preparation are also highlighted. From a biological standpoint, fundamental methods for testing bioactivity, the biocompatibility of materials, and the experimental models for testing the antimicrobial potential are included in this guidance. In conclusion, researchers performing studies on periodontal tissue regeneration will have this guidance as a tool to assess essential properties and characteristics of their materials/scaffold-based strategies.

Clinical study of the diagnosis of thyroid tumours using Raman spectroscopy

OBJECTIVE

The feasibility of the RS for the clinical diagnosis of thyroid tumours was explored.

METHODS

The tumour specimens from 30 benign patients and 30 malignant patients were collected. The collected specimens were subjected to RS and histopathological analysis. The Raman peak intensities of all the specimens were calculated, and the data were analysed using discriminant analysis.

RESULTS

(1) The prevalence rate of malignant tumours in females was as high as 76.7%. Central lymph node metastasis of malignant thyroid tumours accounted for 33.3% of cases, and lateral cervical lymph node metastasis accounted for only 6.7%. (2) The spectral intensity of malignant thyroid tumours was significantly greater than benign thyroid tumours at 1309 cm-1, which should be the characteristic peak of thyroid cancer. The accuracy, sensitivity, and specificity of the RS for differentiating benign from malignant thyroid tumours were 95%, 83.3% and 89.2%.

CONCLUSION

RS is feasible for the diagnosis of thyroid tumours. This study provides experimental and clinical support for the wider application of RS in the evaluation of thyroid tissue.

LEVELS OF EVIDENCE

Levels 4.

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Routine detection methods, including medical imaging, endoscopic examination, and pathological biopsy, often suffer from drawbacks such as low sensitivity and laborious and complex procedures. Raman spectroscopy is a non-invasive and label-free optical technique that provides highly sensitive biomolecular information to facilitate effective tumor identification. In this work, we report the use of fiber-optic Raman spectroscopy for the accurate and rapid diagnosis of gastric and esophageal cancers. Using a database of 14,000 spectra from 140 ex vivo tissue pieces of both tumor and normal tissue samples, we compare the random forest (RF) and our established Euclidean distance Raman spectroscopy (EDRS) model. The RF analysis achieves a sensitivity of 85.23% and an accuracy of 83.05% in diagnosing gastric tumors. The EDRS algorithm with improved diagnostic transparency further increases the sensitivity to 92.86% and accuracy to 89.29%. When these diagnostic protocols are extended to esophageal tumors, the RF and EDRS models achieve accuracies of 71.27% and 93.18%, respectively. Finally, we demonstrate that fewer than 20 spectra are sufficient to achieve good Raman diagnostic accuracy for both tumor tissues. This optimizes the balance between acquisition time and diagnostic performance. Our work, although conducted on ex vivo tissue models, offers valuable insights for in vivo in situ endoscopic Raman diagnosis of gastric and esophageal cancer lesions in the future. Our study provides a robust, rapid, and convenient method as a new paradigm in in vivo endoscopic medical diagnostics that integrates spectroscopic techniques and a Raman probe for detecting upper gastrointestinal malignancies.

Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors

PURPOSE

Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information for tumor identification and was investigated in comparison with RS for delineating brain tumors in situ.

METHODS

Raman spectra were acquired together with AF in situ within the solid tumor and at the tumor border during routine brain tumor surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, n = 10; metastases, n = 10; meningioma, n = 3). Tissue classification for tumor identification in situ was trained on ex vivo data (375 spectra; glioma/GBM patients, n = 20; metastases, n = 11; meningioma, n = 13; and epileptic hippocampi, n = 4).

RESULTS

Both in situ and ex vivo data showed that AF intensity in brain tumors was lower than that in border regions and normal brain tissue. Moreover, a positive correlation was observed between the AF intensity and the intensity of the Raman band corresponding to lipids at 1437 cm- 1, while a negative correlation was found with the intensity of the protein band at 1260 cm- 1. The classification of in situ AF and RS datasets matched the surgeon's evaluation of tissue type, with correct rates of 0.83 and 0.84, respectively. Similar correct rates were achieved in comparison to histopathology of tissue biopsies resected in selected measurement positions (AF: 0.80, RS: 0.83).

CONCLUSIONS

Spectroscopy was successfully integrated into existing neurosurgical workflows, and in situ spectroscopic data could be classified based on ex vivo data. RS confirmed its ability to detect brain tumors, while AF emerged as a competitive method for intraoperative tumor delineation.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

Validation of optimised intracranial spectroscopic probe for instantaneous in-situ monitoring and classification of traumatic brain injury

The development of an optical interface to directly distinguish the brain tissue's biochemistry is the next step in understanding traumatic brain injury (TBI) pathophysiology and the best and most appropriate treatment in cases where in-hospital intracranial access is required. Despite TBI being a globally leading cause of morbidity and mortality in patients under 40, there is still a lack of objective diagnostical tools. Further, given its pathophysiological complexity the majority of treatments provided are purely symptomatic without standardized therapeutic targets. Our tailor-engineered prototype of the intracranial Raman spectroscopy probe (Intra-RSP) is designed to bridge the gap and provide real-time spectroscopic insights to monitor TBI and its evolution as well as identify patient-specific molecular targets for timely intervention. Raman spectroscopy being rapid, label-free and non-destructive, renders it an ideal portable diagnostics tool. In combination with our in-house developed software, using machine learning algorithms for multivariate analysis, the Intra-RSP is shown to accurately differentiate simulated TBI conditions in rat brains from the healthy controls, directly from the brain surface as well as through the rat's skull. Using clinically pre-established methods of cranial entry, the Intra-RSP can be inserted into a 2-piece optimised cranial bolt with integrated focussing and correctly identify a sample in real-life conditions with an accuracy >80 %. To further validate the Intra-RSP's efficiency as a TBI monitoring device, rat brains mildly damaged from inflicted spinal cord injury were found to be correctly classified with 94.5 % accuracy. Through optimization and rigorous in-vivo validation, the Intra-RSP prototype is envisioned to seamlessly integrate into existing standards of neurological care, serving as a minimally invasive, in-situ neuromonitoring tool. This transformative approach has the potential to revolutionize the landscape of neurological care by providing clinicians with unprecedented insights into the nature of brain injuries and fostering targeted, timely and effective therapeutic interventions.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.

Raman Spectroscopy for Instant Bladder Tumor Diagnosis: System Development and In Vivo Proof-Of-Principle Study in Accordance with the European Medical Device Regulation (MDR2017/745)

This work reports on an in vivo Raman-based endoscopy system, invaScope, enabling Raman measurements of healthy and tumor bladder tissue during an endoscopic procedure in the operating theatre. The presented study outlines the progression from the initial concept (validated through previously performed ex vivo studies) to the approval and implementation of a clinical investigational device according to the requirement within the framework of the European Medical Device Regulation (MDR2017/745). The study's primary objective was to employ the invaScope Raman system within the bladder, capturing in vivo spectroscopic Raman data followed by standard histo- and cytopathological examinations of urological tissue (considered the gold standard). The collected data were analyzed and correlated with histopathological findings post-procedure. Additionally, the study aimed to assess the feasibility of using diagnostic equipment, probes, and software for application in a clinical setting, evaluating usability aspects that are important during surgical procedures. This research represents a pivotal step toward advancing Raman spectroscopy for routine clinical use in characterizing bladder lesions.

High-Throughput Raman Spectroscopy by Horizontally Shifted Collection Fibers

Optical fiber probe-based Raman spectroscopy systems are widely used for in situ measurements ranging from material characterization to biomedical applications. However, small Raman cross sections necessitate the use of high-power lasers or long exposure times that limit Raman's larger application to multiple research fields. This limitation can be overcome by collecting more Raman photons through additional collection fibers with taller detectors. This system configuration requires replacement of the detector and modification of the spectrograph to incorporate larger optical components, making it a costly and cumbersome option. In probe-based Raman systems, a typical detector image shows stacked collection fibers on the vertical axis and Raman spectra on the horizontal axis. While the vertical pixels are fully packed with multiple collection fibers, horizontal pixels have broad silent regions due to the narrow bandwidth of Raman peaks, potentially wasting valuable detector pixels. Here, we propose a new approach utilizing horizontally shifted collection fibers rather than vertically stacked ones. We designed and fabricated a novel collection fiber bundle that has horizontally shifted optical fibers in two vertical lines at the spectrograph entrance. This custom-made fiber bundle was incorporated into the imaging spectrograph to provide multiple horizontally shifted spectra on the detector. Through deconvolution, the original spectra can be recovered with an improved detection limit from greater photon collection. We demonstrate an enhanced limit of detection on various bioanalytes, such as glucose, urea, and lactate. Further, we applied the probe to measure tissue Raman spectra and successfully decomposed them into basis spectra, demonstrating the potential application of high-throughput in vivo tissue diagnosis. Our approach provides a simple, cost-effective, and universal method to increase the throughput without modifying existing Raman spectrometers.

Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy

Given the pivotal role of neuronal populations in various biological processes, assessing their collective output is crucial for understanding the nervous system's complex functions. Building on our prior development of a spiral scanning mechanism for the rapid acquisition of Raman spectra from single cells and incorporating machine learning for label-free evaluation of cell states, we investigated whether the Paint Raman Express Spectroscopy System (PRESS) can assess neuronal activities. We tested this hypothesis by examining the chemical responses of glutamatergic neurons as individual neurons and autonomic neuron ganglia as neuronal populations derived from human-induced pluripotent stem cells. The PRESS successfully acquired Raman spectra from both individual neurons and ganglia within a few seconds, achieving a signal-to-noise ratio sufficient for detailed analysis. To evaluate the ligand responsiveness of the induced neurons and ganglia, the Raman spectra were subjected to principal component and partial least squares discriminant analyses. The PRESS detected neuronal activity in response to glutamate and nicotine, which were absent in the absence of calcium. Additionally, the PRESS induced dose-dependent neuronal activity changes. These findings underscore the capability of the PRESS to assess individual neuronal activity and elucidate neuronal population dynamics and pharmacological responses, heralding new opportunities for drug discovery and regenerative medicine advancement.

Label-Free Fiber-Optic Raman Spectroscopy for Intravascular Coronary Atherosclerosis and Plaque Detection

The rupture of atherosclerotic plaques remains one of the leading causes of morbidity and mortality worldwide. The plaques have certain pathological characteristics including a fibrous cap, inflammation, and extensive lipid deposition in a lipid core. Various invasive and noninvasive imaging techniques can interrogate structural aspects of atheroma; however, the composition of the lipid core in coronary atherosclerosis and plaques cannot be accurately detected. Fiber-optic Raman spectroscopy has the capability of in vivo rapid and accurate biomarker detection as an emerging omics technology. Previous studies demonstrated that an intravascular Raman spectroscopic technique may assess and manage the therapeutic and medication strategies intraoperatively. The Raman spectral information identified plaque depositions consisting of lipids, triglycerides, and cholesterol esters as the major components by comparing normal region and early plaque formation region with histology. By focusing on the composition of plaques, we could identify the subgroups of plaques accurately and rapidly by Raman spectroscopy. Collectively, this fiber-optic Raman spectroscopy opens up new opportunities for coronary atherosclerosis and plaque detection, which would assist optimal surgical strategy and instant postoperative decision-making. In this paper, we will review the advancement of label-free fiber-optic Raman probe spectroscopy and its applications of coronary atherosclerosis and atherosclerotic plaque detection.

Spatially Offset Raman Spectroscopy toward In Vivo Assessment of the Adipose Tissue in Cardiometabolic Pathologies

Spatially offset Raman spectroscopy (SORS) enhanced the capabilities of Raman spectroscopy for the depth-resolved analysis of biological and diffusely scattering samples. This technique offers selective probing of subsurface layers, providing molecular insights without invasive procedures. While SORS has found application in biomedical research, up to now, studies have focused mainly on the detection of mineralization of bones and tissues. Herein, for the first time, SORS is used to assess the soft, organic tissue beneath the skin's surface. In this study, we demonstrate the diagnostic utility of a hand-held SORS device for evaluating the chemical composition of the adipose tissue. We compared perigonadal white adipose tissue (gWAT) in a murine model of atherosclerosis, heart failure, and high-fat diet (HFD) induced obesity. Our results reveal distinct chemical differences in gWAT between HFD-fed and control mice, showcasing the potential of SORS for intravital adipose tissue phenotype characterization. Furthermore, our findings underscore the effectiveness of SORS as a valuable tool for noninvasive assessment of the adipose tissue composition, holding potential diagnostic significance for metabolic disorders.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Automated classification pipeline for real-time in vivo examination of colorectal tissue using Raman spectroscopy

Colorectal cancer is the third most common malignancy worldwide and one of the leading causes of death in oncological patients with its diagnosis typically involving confirmation by tissue biopsy. In vivo Raman spectroscopy, an experimental diagnostic method less invasive than a biopsy, has shown great potential to discriminate between normal and cancerous tissue. However, the complex and often manual processing of Raman spectra along with the absence of a suitable instant classifier are the main obstacles to its adoption in clinical practice. This study aims to address these issues by developing a real-time automated classification pipeline coupled with a user-friendly application tailored for non-spectroscopists. First, in addition to routine colonoscopy, 377 subjects underwent in vivo acquisitions of Raman spectra of healthy tissue, adenomatous polyps, or cancerous tissue, which were conducted using a custom-made microprobe. The spectra were then loaded into the pipeline and pre-processed in several steps, including standard normal variate transformation and finite impulse response filtration. The quality of the pre-processed spectral data was checked based on their signal-to-noise ratio before the suitable spectra were decomposed and classified using a combination of principal component analysis and a support vector machine, respectively. After five-fold cross-validation, the developed classifier exhibited 100% sensitivity toward adenocarcinoma and adenomatous polyps. The overall accuracy was 96.9% and 79.2% for adenocarcinoma and adenomatous polyps respectively. In addition, an application with a graphical user interface was developed to facilitate the use of our data pipeline by medical professionals in a clinical environment. Overall, the combination of supervised and unsupervised machine learning with algorithmic pre-processing of in vivo Raman spectra appears to be a viable way of reducing the relatively large number of biopsies currently needed to definitively diagnose colorectal cancer.

Optical fiber biosensors toward in vivo detection

This review takes stock of the various optical fiber-based biosensors that could be used for in vivo applications. We discuss the characteristics that biosensors must have to be suitable for such applications and the corresponding transduction modes. In particular, we focus on optical fiber biosensors based on fluorescence, evanescent wave, plasmonics, interferometry, and Raman phenomenon. The operational principles, implemented solutions, and performances are described and debated. The different sensing configurations, such as the side- and tip-based fiber biosensors, are illustrated, and their adaptation for in vivo measurements is discussed. The required implementation of multiplexed biosensing on optical fibers is shown. In particular, the use of multi-fiber assemblies, one of the most optimal configurations for multiplexed detection, is discussed. Different possibilities for multiple localized functionalizations on optical fibers are presented. A final section is devoted to the practical in vivo use of fiber-based biosensors, covering regulatory, sterilization, and packaging aspects. Finally, the trends and required improvements in this promising and emerging field are analyzed and discussed.

New insights into the effects of UV light on individual Nosema bombycis spores, as determined using single-cell optical approaches

Nosema bombycis (Nb) is a pathogen causing pebrine in sericulture. Ultraviolet (UV) light exposure is a common physical disinfection method, but the mechanisms underlying UV-based disinfection have only been studied at the population level. In this study, changes in and germination of UV-irradiated spores were observed using Raman tweezers and phase-contrast imaging to evaluate the effects of UV radiation on Nb spores at the single-cell level. We found that irradiation caused the complete leakage of trehalose from individual spores. We also found that more spores leaked as the UV dose increased. There was no significant loss of intracellular biomacromolecules and no marked changes in the peaks associated with protein secondary structures. Low-dose radiation promoted spore germination and high-dose radiation decreased the germination rate, while the germination time did not undergo significant alterations. These results suggest that UV radiation disrupts the permeability of the inner membrane and alters the spore wall, thereby affecting the ability of the spore to sense and respond to extracellular stimuli, which further triggers germination and reduces or stops spore germination. This study provides new insights into the molecular mechanisms underlying conventional disinfection measures on microsporidian spores.

Characterization of individual spores of two biological insecticides, Bacillus thuringiensis and Lysinibacillus sphaericus, in response to glutaraldehyde using single-cell optical approaches

Bacillus thuringiensis (Bt) and Lysinibacillus sphaericus (Ls) are the most widely used microbial insecticides. Both encounter unfavorable environmental factors and pesticides in the field. Here, the responses of Bt and Ls spores to glutaraldehyde were characterized using Raman spectroscopy and differential interference contrast imaging at the single-cell level. Bt spores were more sensitive to glutaraldehyde than Ls spores under prolonged exposure: <1.0% of Bt spores were viable after 10 min of 0.5% (v/v) glutaraldehyde treatment, compared to ~ 20% of Ls spores. The Raman spectra of glutaraldehyde-treated Bt and Ls spores were almost identical to those of untreated spores; however, the germination process of individual spores was significantly altered. The time to onset of germination, the period of rapid Ca2+-2,6-pyridinedicarboxylic acid (CaDPA) release, and the period of cortex hydrolysis of treated Bt spores were significantly longer than those of untreated spores, with dodecylamine germination being particularly affected. Similarly, the germination of treated Ls spores was significantly prolonged, although the prolongation was less than that of Bt spores. Although the interiors of Bt and Ls spores were undamaged and CaDPA did not leak, proteins and structures involved in spore germination could be severely damaged, resulting in slower and significantly prolonged germination. This study provides insights into the impact of glutaraldehyde on bacterial spores at the single cell level and the variability in spore response to glutaraldehyde across species and populations.

Nanoprinted microstructure-assisted light incoupling into high-numerical aperture multimode fibers

The coupling of light into optical fibers is limited by the numerical aperture (NA). Here, we show that large-area polymer axial-symmetric microstructures printed on silica multimode fibers improve their incoupling performance by two to three orders of magnitude beyond the numerical aperture limit. A ray-optical mathematical model describing the impact of the grating-assisted light coupling complements the experimental investigation. This study clearly demonstrates the improvement of incoupling performance by nanoprinting microstructures on fibers, opening new horizons, to the best of our knowledge, for multimode fiber applications in life sciences, quantum technologies, and "lab-on-fiber" devices.

Optics miniaturization strategy for demanding Raman spectroscopy applications

Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA's Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm-1 resolution within the 400-4000 cm-1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.

In vivo imaging using surface enhanced spatially offset raman spectroscopy (SESORS): balancing sampling frequency to improve overall image acquisition

In the field of optical imaging, the ability to image tumors at depth with high selectivity and specificity remains a challenge. Surface enhanced resonance Raman scattering (SERRS) nanoparticles (NPs) can be employed as image contrast agents to specifically target cells in vivo; however, this technique typically requires time-intensive point-by-point acquisition of Raman spectra. Here, we combine the use of "spatially offset Raman spectroscopy" (SORS) with that of SERRS in a technique known as "surface enhanced spatially offset resonance Raman spectroscopy" (SESORRS) to image deep-seated tumors in vivo. Additionally, by accounting for the laser spot size, we report an experimental approach for detecting both the bulk tumor, subsequent delineation of tumor margins at high speed, and the identification of a deeper secondary region of interest with fewer measurements than are typically applied. To enhance light collection efficiency, four modifications were made to a previously described custom-built SORS system. Specifically, the following parameters were increased: (i) the numerical aperture (NA) of the lens, from 0.2 to 0.34; (ii) the working distance of the probe, from 9 mm to 40 mm; (iii) the NA of the fiber, from 0.2 to 0.34; and (iv) the fiber diameter, from 100 μm to 400 μm. To calculate the sampling frequency, which refers to the number of data point spectra obtained for each image, we considered the laser spot size of the elliptical beam (6 × 4 mm). Using SERRS contrast agents, we performed in vivo SESORRS imaging on a GL261-Luc mouse model of glioblastoma at four distinct sampling frequencies: par-sampling frequency (12 data points collected), and over-frequency sampling by factors of 2 (35 data points collected), 5 (176 data points collected), and 10 (651 data points collected). In comparison to the previously reported SORS system, the modified SORS instrument showed a 300% improvement in signal-to-noise ratios (SNR). The results demonstrate the ability to acquire distinct Raman spectra from deep-seated glioblastomas in mice through the skull using a low power density (6.5 mW/mm2) and 30-times shorter integration times than a previous report (0.5 s versus 15 s). The ability to map the whole head of the mouse and determine a specific region of interest using as few as 12 spectra (6 s total acquisition time) is achieved. Subsequent use of a higher sampling frequency demonstrates it is possible to delineate the tumor margins in the region of interest with greater certainty. In addition, SESORRS images indicate the emergence of a secondary tumor region deeper within the brain in agreement with MRI and H&E staining. In comparison to traditional Raman imaging approaches, this approach enables improvements in the detection of deep-seated tumors in vivo through depths of several millimeters due to improvements in SNR, spectral resolution, and depth acquisition. This approach offers an opportunity to navigate larger areas of tissues in shorter time frames than previously reported, identify regions of interest, and then image the same area with greater resolution using a higher sampling frequency. Moreover, using a SESORRS approach, we demonstrate that it is possible to detect secondary, deeper-seated lesions through the intact skull.

Vibrational spectroscopy and multiphoton microscopy for label-free visualization of nervous system degeneration and regeneration

Neurological disorders, including spinal cord injury, peripheral nerve injury, traumatic brain injury, and neurodegenerative diseases, pose significant challenges in terms of diagnosis, treatment, and understanding the underlying pathophysiological processes. Label-free multiphoton microscopy techniques, such as coherent Raman scattering, two-photon excited autofluorescence, and second and third harmonic generation microscopy, have emerged as powerful tools for visualizing nervous tissue with high resolution and without the need for exogenous labels. Coherent Raman scattering processes as well as third harmonic generation enable label-free visualization of myelin sheaths, while their combination with two-photon excited autofluorescence and second harmonic generation allows for a more comprehensive tissue visualization. They have shown promise in assessing the efficacy of therapeutic interventions and may have future applications in clinical diagnostics. In addition to multiphoton microscopy, vibrational spectroscopy methods such as infrared and Raman spectroscopy offer insights into the molecular signatures of injured nervous tissues and hold potential as diagnostic markers. This review summarizes the application of these label-free optical techniques in preclinical models and illustrates their potential in the diagnosis and treatment of neurological disorders with a special focus on injury, degeneration, and regeneration. Furthermore, it addresses current advancements and challenges for bridging the gap between research findings and their practical applications in a clinical setting.

Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer

Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

The accuracy of Fiber-Optic Raman Spectroscopy in the detection and diagnosis of head and neck neoplasm in vivo: a systematic review and meta-analysis

Purpose

The aim of this article was to review and collectively assess the published studies of fiber-optic Raman spectroscopy (RS) of the in vivo detection and diagnosis of head and neck carcinomas, and to derive a consensus average of the accuracy, sensitivity and specificity.

Methods

The authors searched four databases, including Ovid-Medline, Ovid-Embase, Cochrane Library, and the China National Knowledge Infrastructure (CNKI), up to February 2023 for all published studies that assessed the diagnostic accuracy of fiber-optic RS in the in vivo detection of head and neck carcinomas. Nonqualifying studies were screened out in accordance with the specified exclusion criteria, and relevant information about the diagnostic performance of fiber-optic RS was excluded. Publication bias was estimated by Deeks' funnel plot asymmetry test. A random effects model was adopted to calculate the pooled sensitivity, specificity and diagnostic odds ratio (DOR). Additionally, the authors conducted a summary receiver operating characteristic (SROC) curve analysis and threshold analysis, reporting the area under the curve (AUC) to evaluate the overall performance of fiber-optic RS in vivo.

Results

Ten studies (including 16 groups of data) were included in this article, and a total of 5365 in vivo Raman spectra (cancer = 1,746; normal = 3,619) were acquired from 877 patients. The pooled sensitivity and specificity of fiber-optic RS of head and neck carcinomas were 0.88 and 0.94, respectively. SROC curves were generated to estimate the overall diagnostic accuracy, and the AUC was 0.96 (95% CI [0.94-0.97]). No significant publication bias was found in this meta-analysis by Deeks' funnel plot asymmetry test. The heterogeneity of these studies was significant; the Q test values of the sensitivity and specificity were 106.23 (P = 0.00) and 64.21 (P = 0.00), respectively, and the I2 index of the sensitivity and specificity were 85.88 (95% CI [79.99-91.77]) and 76.64 (95% CI [65.45-87.83]), respectively.

Conclusion

Fiber-optic RS was demonstrated to be a reliable technique for the in vivo detection of head and neck carcinoma with high accuracy. However, considering the high heterogeneity of these studies, more clinical studies are needed to reduce the heterogeneity, and further confirm the utility of fiber-optic Raman spectroscopy in vivo.

Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature

The process and mechanism of biomineralization and relevant physicochemical properties of mineral crystals are remarkably sophisticated multidisciplinary fields that include biology, chemistry, physics, and materials science. The components of the organic matter, structural construction of minerals, and related mechanical interaction, etc., could help to reveal the unique nature of the special mineralization process. Herein, the paper provides an overview of the biomineralization process from the perspective of molecular vibrational spectroscopy, including the physicochemical properties of biomineralized tissues, from physiological to applied mineralization. These physicochemical characteristics closely to the hierarchical mineralization process include biological crystal defects, chemical bonding, atomic doping, structural changes, and content changes in organic matter, along with the interface between biocrystals and organic matter as well as the specific mechanical effects for hardness and toughness. Based on those observations, the special physiological properties of mineralization for enamel and bone, as well as the possible mechanism of pathological mineralization and calcification such as atherosclerosis, tumor micro mineralization, and urolithiasis are also reviewed and discussed. Indeed, the clearly defined physicochemical properties of mineral crystals could pave the way for studies on the mechanisms and applications.

Two-layer reconstruction of Raman spectra in diffusive media based on an analytical model in the time domain

We derive and validate an analytical model that describes the migration of Raman scattered photons in two-layer diffusive media, based on the diffusion equation in the time domain. The model is derived under a heuristic approximation that background optical properties are identical on the excitation and Raman emission wavelengths. Methods for the reconstruction of two-layer Raman spectra have been developed, tested in computer simulations and validated on tissue-mimicking phantom measurements data. Effects of different parameters were studied in simulations, showing that the thickness of the top layer and number of detected photon counts have the most significant impact on the reconstruction. The concept of quantitative, mathematically rigorous reconstruction using the proposed model was finally proven on experimental measurements, by successfully separating the spectra of silicone and calcium carbonate (calcite) layers, showing the potential for further development and eventual application in clinical diagnostics.

Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy

We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Impact of e-cigarette liquid on porcine lung tissue-Ex vivo confocal Raman micro-spectroscopy study

Ex vivo porcine lung immersed in e-liquid was investigated in-depth using confocal Raman micro-spectroscopy to assess the e-liquid influence on the lung. It was found that lung-related Raman band intensities at 1002, 1548, 1618 and 1655 cm-1 increased after first and second treatments except the surface, which was attributed to the well-known optical clearing (OC) effect due to alveoli filling with e-liquid resulting in light scattering reduction. The autofluorescence enhancement was explained by oxidative stress induced in lung during exposure to e-liquid. Moreover, e-liquid induced collagen dehydration was revealed by the I937 /I926 Raman band intensity ratio change. The effect was enhanced after the second treatment of the same lung tissue that indicates the possibility of multi-step OC treatment. We hypothesize that the nicotine-flavour-free e-liquids containing glycerol and propylene glycol could potentially be used in clinical protocols as OC agent for enhanced in-depth Raman-guided bronchoscopy.

Development of a two-beveled-fiber polarized fiber-optic Raman probe coupled with a ball lens for in vivo superficial epithelial Raman measurements in endoscopy

We report on the development of a two-beveled-fiber polarized (TBFP) fiber-optic Raman probe coupled with a ball lens for in vivo superficial epithelial Raman measurements in endoscopy. The two-beveled fibers positioned symmetrically along a ball lens, in synergy with paired parallel-polarized polarizers integrated between the fibers and the ball lens, maximize the Raman signal excitation and collection from the superficial epithelium where gastrointestinal (GI) precancer arises. Monte Carlo (MC) simulations and two-layer tissue phantom experiments show that the probe developed detects ∼90% of the Raman signal from the superficial epithelium. The suitability of the probe developed for rapid (<3 s) superficial epithelial Raman measurements is demonstrated on fresh swine esophagus, stomach, and colon tissues, followed by their differentiation with high accuracies (92.1% for esophagus [sensitivity: 89.3%, specificity: 93.2%], 94.1% for stomach [sensitivity: 86.2%, specificity: 97.2%], and 94.1% for colon [sensitivity: 93.2%, specificity: 94.7%]). The presented results suggest the great potential of the developed probe for enhancing in vivo superficial epithelial Raman measurements in endoscopy.

Identification of Healthy Tissue from Malignant Tissue in Surgical Margin Using Raman Spectroscopy in Oral Cancer Surgeries

Accurate identification of tissue types in surgical margins is essential for ensuring the complete removal of cancerous cells and minimizing the risk of recurrence. The objective of this study was to explore the clinical utility of Raman spectroscopy for the detection of oral squamous cell carcinoma (OSCC) in both tumor and healthy tissues obtained from surgical resection specimens during surgery. This study enrolled a total of 64 patients diagnosed with OSCC. Among the participants, approximately 50% of the cases were classified as the most advanced stage, referred to as T4. Raman experiments were conducted on cryopreserved tissue samples collected from patients diagnosed with OSCC. Prominent spectral regions containing key oral biomarkers were analyzed using the partial least squares-support vector machine (PLS-SVM) method, which is a powerful multivariate analysis technique for discriminant analysis. This approach effectively differentiated OSCC tissue from non-OSCC tissue, achieving a sensitivity of 95.7% and a specificity of 93.3% with 94.7% accuracy. In the current study, Raman analysis of fresh tissue samples showed that OSCC tissues contained significantly higher levels of nucleic acids, proteins, and several amino acids compared to the adjacent healthy tissues. In addition to differentiating between OSCC and non-OSCC tissues, we have also explored the potential of Raman spectroscopy in classifying different stages of OSCC. Specifically, we have investigated the classification of T1, T2, T3, and T4 stages based on their Raman spectra. These findings emphasize the importance of considering both stage and subsite factors in the application of Raman spectroscopy for OSCC analysis. Future work will focus on expanding our tissue sample collection to better comprehend how different subsites influence the Raman spectra of OSCC at various stages, aiming to improve diagnostic accuracy and aid in identifying tumor-free margins during surgical interventions.

Serum-based Raman spectroscopic diagnosis of blast-induced brain injury in a rat model

The diagnosis of blast-induced traumatic brain injury (bTBI) is of paramount importance for early care and clinical therapy. Therefore, the rapid diagnosis of bTBI is vital to the treatment and prognosis in clinic. In this paper, we reported a new strategy for label-free bTBI diagnosis through serum-based Raman spectroscopy. The Raman spectral characteristics of serum in rat were investigated at 3 h, 24 h, 48 h and 72 h after mild and moderate bTBIs. It has been demonstrated that both the position and intensity of Raman characteristic peaks exhibited apparent differences in the range of 800-3000cm^-1 compared with control group. It could be inferred that the content, structure and interaction of biomolecules in the serum were changed after blast exposure, which might help to understand the neurological syndromes caused by bTBI. Furthermore, the control group, mild and moderate bTBIs at different times (a total of 9 groups) were automatically classified by combining principal component analysis and four machine learning algorithms (quadratic discriminant analysis, support vector machine, k-nearest neighbor, neural network). The highest classification accuracy, sensitivity and precision were up to 95.4%, 95.9% and 95.7%. It is suggested that this method has great potential for high-sensitive, rapid, and label-free diagnosis of bTBI.

Performance assessment of probe-based Raman spectroscopy systems for biomedical analysis

We present a methodology for evaluating the performance of probe-based Raman spectroscopy systems for biomedical analysis. This procedure uses a biological standard sample and data analysis approach to circumvent many of the issues related to accurately measuring and comparing the signal quality of Raman spectra between systems. Dairy milk is selected as the biological standard due to its similarity to tissue spectral properties and because its homogeneity eliminates the dependence of probe orientation on the measured spectrum. A spectral dataset is first collected from milk for each system configuration, followed by a model-based correction step to remove photobleaching artifacts and accurately calculate SNR. Results demonstrate that the proposed strategy, unlike current methods, produces an experimental SNR that agrees with the theoretical value. Four preconfigured imaging spectrographs that share similar manufacturer specifications were compared, showing that their capabilities to detect biological Raman spectra widely differ in terms of throughput and stray light rejection. While the methodology is used to compare spectrographs in this case, it can be adapted for other purposes, such as optimizing the design of a custom-built Raman spectrometer, evaluating inter-probe variability, or examining how altering system subcomponents affects signal quality.

Advanced Cardiac Imaging in the Assessment of Aortic Stenosis

Aortic stenosis is the most common form of valve disease in the Western world and a major healthcare burden. Although echocardiography remains the central modality for the diagnosis and assessment of aortic stenosis, recently, advanced cardiac imaging with cardiovascular magnetic resonance, computed tomography, and positron emission tomography have provided invaluable pathological insights that may guide the personalized management of the disease. In this review, we discuss applications of these novel non-invasive imaging modalities for establishing the diagnosis, monitoring disease progression, and eventually planning the invasive treatment of aortic stenosis.

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.

Highly Excretable Gold Supraclusters for Translatable In Vivo Raman Imaging of Tumors

Raman spectroscopy provides excellent specificity for in vivo preclinical imaging through a readout of fingerprint-like spectra. To achieve sufficient sensitivity for in vivo Raman imaging, metallic gold nanoparticles larger than 10 nm were employed to amplify Raman signals via surface-enhanced Raman scattering (SERS). However, the inability to excrete such large gold nanoparticles has restricted the translation of Raman imaging. Here we present Raman-active metallic gold supraclusters that are biodegradable and excretable as nanoclusters. Although the small size of the gold nanocluster building blocks compromises the electromagnetic field enhancement effect, the supraclusters exhibit bright and prominent Raman scattering comparable to that of large gold nanoparticle-based SERS nanotags due to high loading of NIR-resonant Raman dyes and much suppressed fluorescence background by metallic supraclusters. The bright Raman scattering of the supraclusters was pH-responsive, and we successfully performed in vivo Raman imaging of acidic tumors in mice. Furthermore, in contrast to large gold nanoparticles that remain in the liver and spleen over 4 months, the supraclusters dissociated into small nanoclusters, and 73% of the administered dose to mice was excreted during the same period. The highly excretable Raman supraclusters demonstrated here offer great potential for clinical applications of in vivo Raman imaging.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Comment on "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes"

This paper comments recent findings about Raman spectroscopy application for in vivo noninvasive diabetes detection, published in the Journal of Biophotonics by E. Guevara et al. (J. Biophotonics 2022, 15, e202200055). The proposed results may be not entirely correct due to possible overestimation of classification models and absence of additional information regarding age of tested volunteers.

Brillouin-Raman micro-spectroscopy and machine learning techniques to classify osteoarthritic lesions in the human articular cartilage

In this study, Brillouin and Raman micro-Spectroscopy (BRamS) and Machine Learning were used to set-up a new diagnostic tool for Osteoarthritis (OA), potentially extendible to other musculoskeletal diseases. OA is a degenerative pathology, causing the onset of chronic pain due to cartilage disruption. Despite this, it is often diagnosed late and the radiological assessment during the routine examination may fail to recognize the threshold beyond which pharmacological treatment is no longer sufficient and prosthetic replacement is required. Here, femoral head resections of OA-affected patients were analyzed by BRamS, looking for distinctive mechanical and chemical markers of the progressive degeneration degree, and the result was compared to standard assignment via histological staining. The procedure was optimized for diagnostic prediction by using a machine learning algorithm and reducing the time required for measurements, paving the way for possible future in vivo characterization of the articular surface through endoscopic probes during arthroscopy.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

Investigations on Grating-Enhanced Waveguides for Wide-Angle Light Couplings

As a universal physical scheme, effective light couplings to waveguides favor numerous applications. However, the low coupling efficiency at wide angles prohibits this fundamental functionality and thus lowers the performance levels of photonic systems. As previously found, the transmission gratings patterned on waveguide facets could significantly improve the large-angle-inputted efficiency to the order of 10-1. Here, we continue this study with a focus on a common scenario, i.e., a grating-modified waveguide excited by the Gaussian beam. A simplified 2D theoretical model is firstly introduced, proving that the efficiency lineshape could be well flattened by elaborately arranged diffractive gratings. For demonstration, subsequent explorations for proper grating geometries were conducted, and four structural configurations were selected for later full-wave numerical simulations. The last comparison studies showcase that the analytical method approximates the finite element method-based modelings. Both methods highlight grating-empowered coupling efficiencies, being 2.5 bigger than the counterparts of the previously reported seven-ring structure. All in all, our research provides instructions to simulate grating effects on the waveguide's light-gathering abilities. Together with algorithm-designed coupling structures, it would be of great interest to further benefit real applications, such as bioanalytical instrumentation and quantum photon probes.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification

In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.

In vivo spectroscopy: optical fiber probes for clinical applications

INTRODUCTION

Fiber optic probe based in-vivo spectroscopy techniques are fast and highly objective methods for intraoperative diagnoses and minimally invasive surgical interventions for all procedures where endoscopic observations are carried out for cancers of different types. The Raman spectral features provide molecular fingerprint-type information and can reveal the subjects' pathological state in label-free manner, making endoscopy multiplexed fiber optic probe-based devices with the potential for translation from bench to bedside for routine applications.

AREAS COVERED

This review provides a general overview of different fiber-optic probes for in-vivo measurements with emphasis on Raman spectroscopy for biomedical application. Various aspects such as fiber-optic probe, radiation source, detector, and spectrometer for extracting optimum spectral features have also been discussed.

EXPERT OPINION

: Optical spectroscopy-based fiber probe systems with "Chip-on-Tip" technology, combined with machine learning, can in the near future, become a complimentary diagnostic tool to magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound, etc. Hyperspectral imaging and fluorescence-based devices are in the advanced stage of technology readiness level (TRL), and with advances in lasers and miniature spectroscopy systems, probe-based Raman devices are also coming up.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.