Prospects for in vivo Raman spectroscopy

Raman Characterization of Plastics: A DFT Study of Polystyrene

Plastic materials are ubiquitous and raise concerns about their impact on health and the environment. To address these concerns, it is crucial to characterize the structural, size, and textural properties of plastics throughout their lifecycle from production to degradation. Raman spectroscopy appears as a valuable tool for this purpose, offering speed, robustness, and sensitivity to nanoscale and amorphous particles. In order to be properly used for plastics, the Raman response of reference materials needs to be carefully assessed, with the literature on such assessments being scarce. This study addresses this gap by using theoretical calculations to generate ab initio spectra for polystyrene, a reference material. The aim is to explain the origins of the spectral peaks and their consistency across various compositions and structures using linear ordered polymeric and finite amorphous models. The CRYSTAL package is employed to obtain full Raman spectra based on a careful benchmark of computational settings. While some peaks are present across all spectra and can serve for calibration, others exhibit structure-dependent behavior, enabling polymer identification. We conclude that Raman spectroscopy is a well-suited technique for plastics characterization provided that a careful analysis of signal origin is conducted to fully interpret the spectra and deploy applications.

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.

The microarchitecture and chemical composition of the femur neck of senescent female rats after different physical training protocols

A sedentary lifestyle, coupled with a decrease in estrogen, impairs bone homeostasis, favoring to the development of osteopenia and osteoporosis, both recognized as risk factors for fractures. Here, we investigated the quality of the femur, particularly the femur neck region, and the ambulation performance of senescent rats subjected to three different physical training protocols during the periestropause period. Forty-eight female rats, 18 months of age, were subjected to a 120-day training period, three times a week. The rats were distributed into four groups: aerobic training (AT), strength training (ST), concurrent training (CT), or no training (NT). After the experimental period, at 21 months of age, ambulation performance and femur were analyzed using microtomography, Raman stereology, densitometry, and mechanical strength tests. The results demonstrated greater remodeling activity and improvement in resistance and bone microarchitecture in the femur neck of senescent female rats after undergoing physical training. Our verified higher intensities of bands related to collagen, phosphate, amide III, and amide I. Furthermore, the analysis of the secondary collagen structures indicated alterations in the collagen network due to the exercise, resulting in increased bone strength. Both AT and strength-based training proved beneficial, with AT showing greater adaptations in bone density and stiffness in the femur, while strength-based training greater adaptations in trabecular and cortical structure. These insights contribute to the understanding of the potential interventions for preventing osteopenia and osteoporosis, which are critical risk factors for fractures.

Context-Responsive Nanoparticle Derived from Synthetic Zwitterionic Ionizable Phospholipids in Targeted CRISPR/Cas9 Therapy for Basal-like Breast Cancer

The majority of triple negative breast cancers (TNBCs) are basal-like breast cancers (BLBCs), which tend to be more aggressive, proliferate rapidly, and have poor clinical outcomes. A key prognostic biomarker and regulator of BLBC is the Forkhead box C1 (FOXC1) transcription factor. However, because of its functional placement inside the cell nucleus and its structural similarity with other related proteins, targeting FOXC1 for therapeutic benefit, particularly for BLBC, continues to be difficult. We envision targeted nonviral delivery of CRISPR/Cas9 plasmid toward the efficacious knockdown of FOXC1. Keeping in mind the challenges associated with the use of CRISPR/Cas9 in vivo, including off-targeting modifications, and effective release of the cargo, a nanoparticle with context responsive properties can be designed for efficient targeted delivery of CRISPR/Cas9 plasmid. Consequently, we have designed, synthesized, and characterized a zwitterionic amino phospholipid-derived transfecting nanoparticle for delivery of CRISPR/Cas9. The construct becomes positively charged only at low pH, which encourages membrane instability and makes it easier for nanoparticles to exit endosomes. This has enabled effective in vitro and in vivo downregulation of protein expression and genome editing. Following this, we have used EpCAM aptamer to make the system targeted toward BLBC cell lines and to reduce its off-target toxicity. The in vivo efficacy, biodistribution, preliminary pharmacokinetics, and biosafety of the optimized targeted CRISPR nanoplatform is then validated in a rodent xenograft model. Overall, we have attempted to knockout the proto-oncogenic FOXC1 expression in BLBC cases by efficient delivery of CRISPR effectors via a context-responsive nanoparticle delivery system derived from a designer lipid derivative. We believe that the nonviral approach for in vitro and in vivo delivery of CRISPR/Cas9 targeted toward FOXC1, studied herein, will greatly emphasize the therapeutic regimen for BLBC.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in Tetramorium caespitum

Antibiotic resistance genes (ARGs) are ancient but have become a modern critical threat to health. Gut microbiota, a dynamic reservoir for ARGs, transfer resistance between individuals. Surveillance of the antibiotic resistome in the gut during different host growth phases is critical to understanding the dynamics of the resistome in this ecosystem. Herein, we disentangled the ARG profiles and the dynamic mechanism of ARGs in the egg and adult phases of Tetramorium caespitum. Experimental results showed a remarkable difference in both gut microbiota and gut resistome with the development of T. caespitum. Meta-based metagenomic results of gut microbiota indicated the generalizability of gut antibiotic resistome dynamics during host development. By using Raman spectroscopy and metabolomics, the metabolic phenotype and metabolites indicated that the biotic phase significantly changed lipid metabolism as T. caespitum aged. Lipid metabolites were demonstrated as the main factor driving the enrichment of ARGs in T. caespitum. Cuminaldehyde, the antibacterial lipid metabolite that displayed a remarkable increase in the adult phase, was demonstrated to strongly induce ARG abundance. Our findings show that the gut resistome is host developmental stage-dependent and likely modulated by metabolites, offering novel insights into possible steps to reduce ARG dissemination in the soil food chain.

Human tissue-resident peritoneal macrophages reveal resistance towards oxidative cell stress induced by non-invasive physical plasma

In the context of multimodal treatments for abdominal cancer, including procedures such as cytoreductive surgery and intraperitoneal chemotherapy, recurrence rates remain high, and long-term survival benefits are uncertain due to post-operative complications. Notably, treatment-limiting side effects often arise from an uncontrolled activation of the immune system, particularly peritoneally localized macrophages, leading to massive cytokine secretion and phenotype changes. Exploring alternatives, an increasing number of studies investigated the potential of plasma-activated liquids (PAL) for adjuvant peritoneal cancer treatment, aiming to mitigate side effects, preserve healthy tissue, and reduce cytotoxicity towards non-cancer cells. To assess the non-toxicity of PAL, we isolated primary human macrophages from the peritoneum and subjected them to PAL exposure. Employing an extensive methodological spectrum, including flow cytometry, Raman microspectroscopy, and DigiWest protein analysis, we observed a pronounced resistance of macrophages towards PAL. This resistance was characterized by an upregulation of proliferation and anti-oxidative pathways, countering PAL-derived oxidative stress-induced cell death. The observed cellular effects of PAL treatment on human tissue-resident peritoneal macrophages unveil a potential avenue for PAL-derived immunomodulatory effects within the human peritoneal cavity. Our findings contribute to understanding the intricate interplay between PAL and macrophages, shedding light on the promising prospects for PAL in the adjuvant treatment of peritoneal cancer.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis

Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.

Raman microscopy reveals how cell inflammation activates glucose and lipid metabolism

Metabolism of endothelial cells (ECs) depends on the availability of the energy substrates. Since the endothelium is the first line of defence against inflammation in the cardiovascular system and its dysfunction can lead to the development of cardiovascular diseases, it is important to understand how glucose metabolism changes during inflammation. In this work, glucose uptake was studied in human microvascular endothelial cells (HMEC-1) in high glucose (HG), and additionally in an inflammatory state, using Raman imaging. HG state was induced by incubation of ECs with a deuterated glucose analogue, while the EC inflammation was caused by TNF-α pre-treatment. Spontaneous and stimulated Raman scattering spectroscopy provided comprehensive information on biochemical changes, including lipids and the extent of unsaturation induced by excess glucose in ECs., induced by excess glucose in ECs. In this work, we indicated spectroscopic markers of metabolic changes in ECs as a strong increase in the ratio of the intensity of lipids / (proteins + lipids) bands and an increase in the level of lipid unsaturation and mitochondrial changes. Inflamed ECs treated with HG, revealed enhanced glucose uptake, and intensified lipid production i.a. of unsaturated lipids. Additionally, increased cytochrome c signal in the mitochondrial region indicated higher mitochondrial activity and biogenesis. Raman spectroscopy is a powerful method for determining the metabolic markers of ED which will better inform understanding of disease onset, development, and treatment.

DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%-40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis.

Non-invasive biochemical analysis and comparison of atopic dermatitis and psoriasis skin using handheld confocal Raman spectroscopy

A handheld non-invasive confocal Raman system (CRS) was used to evaluate the differences in skin biochemicals between atopic dermatitis (AD) and psoriasis, which are inflammatory skin conditions. Raman spectral measurements in the fingerprint and high wavenumber region were acquired using a portable in-house CRS system with excitation lasers operating at 671 and 785 nm. It was deduced that relative amount of water decreases in the following sequence of skin: healthy, psoriasis and AD. Moreover, differential trends were observed for the subclasses of ceramides such that ceramide 3 is lower in the lesional AD and psoriasis skin as compared to healthy, while ceramide 2 showed a contrasting trend of decrease in lesional AD and increase in lesional psoriasis as opposed to healthy skin. Amount of cholesterol was significantly higher in lesional psoriasis as compared to lesional AD and healthy skin. These differences can aid in an objective classification of the skin conditions and in the formulation of new disease-specific topical treatments.

Performance matching of common pesticides in banana plantations on the surface of banana leaves at different growth stages

BACKGROUND

The effective deposition of pesticide droplets on a target leaf surface is critical for decreasing pesticide application rates. The wettability between the target leaf surface and the pesticide spray liquid should be investigated in depth, with the aim of enhancing the adhesion of pesticide solutions. The wetting and deposition behavior of pesticides on target leaves depends on the properties of the liquid and the physical and chemical properties of the leaves. The physical and chemical properties of leaves vary with growth stage. This study aims to investigate the wetting behavior of banana leaf surfaces at different stages.

RESULTS

The microstructures and chemical compositions of banana leaf surfaces at different stages were studied using modern methods. The surface structure of banana leaves exhibited a wide variety of characteristics at different growth stages, and the chemical composition changed marginally. The surface free energy (SFE) and polar and non-polar components of banana leaves at different growth stages were measured by examining the contact angles (CA) of different test solutions on the surface of banana leaves. Previous research has suggested that changes in the CA and SFE correlate with changes in leaf surface wettability. In general, the new upper leaves of banana trees are composed of polar components and exhibit hydrophobicity. Non-polar components become dominant as the leaf grows. The back surface of banana leaves was non-polar at all growth stages, with a trend that was opposite to that of the front surface. The critical surface tension of the banana leaf surface at different growth stages ranged from 7.83 to 24.22 mN m-1 , thus falling into the category of a low-energy surface.

CONCLUSION

The surface roughness and chemical characteristics of banana leaves affected the wettability of the leaf surface. Differences in the free energy and the polar and non-polar components of the leaf surface at were seen at different growth stages. This study provides a favorable reference for the rational control of pesticide spraying parameters and the enhancement of wetting and adhesion of the solution on banana leaf surfaces. © 2023 Society of Chemical Industry.

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.

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review

Estimating postmortem intervals (PMI) is crucial in forensic investigations, providing insights into criminal cases and determining the time of death. PMI estimation relies on expert experience and a combination of thanatological data and environmental factors but is prone to errors. The lack of reliable methods for assessing PMI in bones and soft tissues necessitates a better understanding of bone decomposition. Several research groups have shown promise in PMI estimation in skeletal remains but lack valid data for forensic cases. Current methods are costly, time-consuming, and unreliable for PMIs over 5 years. Raman spectroscopy (RS) can potentially estimate PMI by studying chemical modifications in bones and teeth correlated with burial time. This review summarizes RS applications, highlighting its potential as an innovative, nondestructive, and fast technique for PMI estimation in forensic medicine.

Deep Learning Assisted Surface-Enhanced Raman Spectroscopy (SERS) for Rapid and Direct Nucleic Acid Amplification and Detection: Toward Enhanced Molecular Diagnostics

Surface-enhanced Raman scattering (SERS) has evolved into a robust analytical technique capable of detecting a variety of biomolecules despite challenges in securing a reliable Raman signal. Conventional SERS-based nucleic acid detection relies on hybridization assays, but reproducibility and signal strength issues have hindered research on directly amplifying nucleic acids on SERS surfaces. This study introduces a deep learning assisted ZnO-Au-SERS-based direct amplification (ZADA) system for rapid, sensitive molecular diagnostics. The system employs a SERS substrate fabricated by depositing gold on uniformly grown ZnO nanorods. These nanorods create hot spots for the amplification of the target nucleic acids directly on the SERS surface, eliminating the need for postamplification hybridization and Raman reporters. The limit of detection of the ZADA system was superior to those of the conventional amplification methods. Clinical validation of the ZADA system with coronavirus disease 2019 (COVID-19) samples from human patients yielded a sensitivity and specificity of 92.31% and 81.25%, respectively. The integration of a deep learning program further enhanced sensitivity and specificity to 100% and reduced SERS analysis time, showcasing the potential of the ZADA system for rapid, label-free disease diagnosis via direct nucleic acid amplification and detection within 20 min.

Enhancing phytochemical parameters in broccoli through vacuum impregnation and their prediction with comparative ANN and RSM models

Amidst increasing demand for nutritious foods, the quest for effective methods to enhance health-promoting attributes has intensified. Vacuum impregnation (VI) is a promising technique to augment produce properties while minimizing impacts on biochemical attributes. In light of broccoli's growing popularity driven by its nutritional benefits, this study explores the impact of VI using ascorbic acid and calcium chloride as impregnation agents on enhancing its phytochemical properties. Response surface methodology (RSM) was used for optimization of the vacuum impregnation process with Vacuum pressure (0.6, 0.4, 0.2 bar), vacuum time (3, 7, 11 min), restoration time (5, 10, 15 min), and concentrations (0.5, 1.0, 1.5%) as independent parameters. The influence of these process parameters on six targeted responses viz. total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), total chlorophyll content (TCC), free radical scavenging activity (FRSA), and carotenoid content (CC) were analysed. Levenberg-Marquardt back propagated neural network (LMB-ANN) was used to model the impregnation process. Multiple response optimization of the vacuum impregnation process indicated an optimum condition of 0.2 bar vacuum pressure, 11 min of vacuum time, 12 min of restoration time, and 1.5% concentration of solution for vacuum impregnation of broccoli. The values of TPC, TFC, AAC, TCC, FRSA, and CC obtained at optimized conditions were 291.20 mg GAE/100 g, 11.29 mg QE/100 g, 350.81 mg/100 g, 1.21 mg/100 g, 79.77 mg, and 8.51 mg, respectively. The prediction models obtained through ANN was found suitable for predicting the responses with less standard errors and higher R2 value as compared to RSM models. Instrumental characterization (FTIR, XRD and SEM analysis) of untreated and treated samples were done to see the effect of impregnation on microstructural and morphological changes in broccoli. The results showed enhancement in the TPC, TFC, AAC, TCC, FRSA, and CC values of broccoli florets with impregnation. The FTIR and XRD analysis also supported the results.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

BACKGROUND AND OBJECTIVE

Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca.

METHODS

Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELİSA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study.

RESULTS

In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm^-1 was observed and, the Raman intensity of amide III, II, I, and CH₂ proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm^-1, as well as between 2700 and 3000 cm^-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm^-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm.

CONCLUSIONS

The obtained results suggest, that Raman shifts at 1302 and 1306 cm^-1 could be spectroscopic markers of gastric cancer.

Increased levels of nerve growth factor accompany oxidative load in recurrent pregnancy loss. Machine learning applied to FT-Raman spectra study

The presented article is focused on developing and validating an efficient, credible, minimally invasive technique based on spectral signatures of blood serum samples in patients with diagnosed recurrent pregnancy loss (RPL) versus healthy individuals who were followed at the Gynecology department. A total of 120 participants, RPL disease (n = 60) and healthy individuals (n = 60), participated in the study. First, we investigated the effect of circulating nerve growth factor (NGF) in RPL and healthy groups. To show NGF's effect, we measured the level of oxidative loads such as Total Antioxidant Level (TAS), Total Oxidant Level (TOS), and Oxidative Stress Index (OSI) with Beckman Coulter AU system and biochemical assays. We find a correlation between oxidative load and NGF level. Oxidative load mainly causes structural changes in the blood. Therefore, we obtained Raman measurements of the participant's serum. Then we selected two Raman regions, 800 and 1800 cm^-1, and between 2700 cm^-1 and 3000 cm^-1, to see chemical changes. We noted that Raman spectra obtained for RPL and healthy women differed. The findings confirm that the imbalance between reactive oxygen species and antioxidants has important implications for the pathogenesis of RPL and that NGF levels accompany the level of oxidative load in the RPL state. Biomolecular structure and composition were determined using Raman spectroscopy and machine learning methods, and the correlation of these parameters was studied alongside machine learning technologies to advance toward clinical translation. Here we determined and validated the development of instrumentation for the Analysis of RPL patients' serum that can differentiate from control individuals with an accuracy of 100% using the Raman region corresponding to structural changes. Furthermore, this study found a correlation between traditional biochemical parameters and Raman data. This suggests that Raman spectroscopy is a sensitive tool for detecting biochemical changes in serum caused by RPL or other diseases.

Raman spectroscopy combined with deep learning for rapid detection of melanoma at the single cell level

Melanoma is an aggressive and metastatic skin cancer caused by genetic mutations in melanocytes, and its incidence is increasing year by year. Understanding the gene mutation information of melanoma cases is very important for its precise treatment. The current diagnostic methods for melanoma include radiological, pharmacological, histological, cytological and molecular techniques, but the gold standard for diagnosis is still pathological biopsy, which is time consuming and destructive. Raman spectroscopy is a rapid, sensitive and nondestructive detection method. In this study, a total of 20,000 Surface-enhanced Raman scattering (SERS) spectra of melanocytes and melanoma cells were collected using a positively charged gold nanoparticles planar solid SERS substrate, and a classification network system based on convolutional neural networks (CNN) was constructed to achieve the classification of melanocytes and melanoma cells, wild-type and mutant melanoma cells and their drug resistance. Among them, the classification accuracy of melanocytes and melanoma cells was over 98%. Raman spectral differences between melanocytes and melanoma cells were analyzed and compared, and the response of cells to antitumor drugs were also evaluated. The results showed that Raman spectroscopy provided a basis for the medication of melanoma, and SERS spectra combined with CNN classification model realized classification of melanoma, which is of great significance for rapid diagnosis and identification of melanoma.

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.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

A methodology to evaluate different histological preparations of soft tissues: Intervertebral disc tissues study

A tissue preparation method will inevitably alter the tissue content. This study aims to evaluate how different common sample preparation methods will affect the tissue morphology, biomechanical properties, and chemical composition of samples. The study focuses on intervertebral disc (IVD) tissue; however, it can be applied to other soft tissues. Raman spectroscopy synchronized with nanoindentation instrumentation was employed to investigate the compositional changes of IVD, specifically, nucleus pulposus (NP) and annulus fibrosus (AF), together with their biomechanical properties of IVD. These properties were examined through the following histological specimen types: fresh cryosection (control), fixed cryosection, and paraffin-embedded. The IVD tissue could be located using an optical microscope under three different preparation methods. Paraffin-embedded samples showed the most explicit details where the lamellae structure of AF could be identified. In terms of biomechanical properties, there was no significant difference between the fresh and fixed cryosection (p > 0.05). In contrast, the fresh cryosection and paraffin-embedded samples showed a significant difference (p < 0.05). It was also found that the tissue preparations affected the chemical content of the tissues and structure of the tissue, which are expected to contribute to biomechanical properties changes. Fresh cryosection and fixed cryosection samples are more promising to work with for biomechanical assessment in histological tissues. The findings fill essential gaps in the literature by providing valuable insight into the characteristics of IVD at the microscale. This study can also become a reference for a better approach to assessing the mechanical properties and chemical content of soft tissues at the microscale.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

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.

Rapid diagnosis of diabetes based on ResNet and Raman spectroscopy

Diabetes mellitus is a global public health problem, and the epidemic situation in China is particularly serious. The prevalence of the disease has been increasing in recent years, and the number of patients is the highest in the world. Diabetes has become another chronic non-communicable disease that seriously endangers the health of our people after cardiovascular and cerebrovascular diseases and tumors. In this study, urine sample data were collected from 37 diabetic patients and 37 healthy volunteers using Raman spectroscopy. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and a polynomial Savitzky-Golay smoothing algorithm. After extracting features using principal component analysis (PCA) dimensionality reduction algorithm, ResNet, support vector machine (SVM) and linear discriminant analysis (LDA) classification models were selected to classify and identify diabetic patients and healthy controls. The results show that ResNet has the best discrimination effect, and the average accuracy, recall and F1-score can reach 84.28%, 86.20% and 84.02% respectively after five cross-validations, and the area under the subject working characteristic (ROC) curve is 0.93. The experimental results show that the model established in this paper is simple to operate, highly accurate and has good reference value for rapid screening of diabetes.

Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy

OBJECTIVES

Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens.

MATERIALS AND METHODS

Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression.

RESULTS

PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%.

CONCLUSION

PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Single-Molecule Surface-Enhanced Raman Spectroscopy

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to detect single molecules in a non-invasive, label-free manner with high-throughput. SM-SERS can detect chemical information of single molecules without statistical averaging and has wide application in chemical analysis, nanoelectronics, biochemical sensing, etc. Recently, a series of unprecedented advances have been realized in science and application by SM-SERS, which has attracted the interest of various fields. In this review, we first elucidate the key concepts of SM-SERS, including enhancement factor (EF), spectral fluctuation, and experimental evidence of single-molecule events. Next, we systematically discuss advanced implementations of SM-SERS, including substrates with ultra-high EF and reproducibility, strategies to improve the probability of molecules being localized in hotspots, and nonmetallic and hybrid substrates. Then, several examples for the application of SM-SERS are proposed, including catalysis, nanoelectronics, and sensing. Finally, we summarize the challenges and future of SM-SERS. We hope this literature review will inspire the interest of researchers in more fields.

Surface-enhanced Raman spectroscopic analysis of the centrifugally filtered blood serum samples of the hepatitis C patients

BACKGROUND

Previously Raman spectroscopy technique is a use to analyze non-invasive disease related to body fluids.

OBJECTIVES

For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed.

METHOD

Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis.

RESULTS

PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99 % accuracy.

Cell Type-Specific Anti-Adhesion Properties of Peritoneal Cell Treatment with Plasma-Activated Media (PAM)

Postoperative abdominal adhesions are responsible for serious clinical disorders. Administration of plasma-activated media (PAM) to cell type-specific modulated proliferation and protein biosynthesis is a promising therapeutic strategy to prevent pathological cell responses in the context of wound healing disorders. We analyzed PAM as a therapeutic option based on cell type-specific anti-adhesive responses. Primary human peritoneal fibroblasts and mesothelial cells were isolated, characterized and exposed to different PAM dosages. Cell type-specific PAM effects on different cell components were identified by contact- and marker-independent Raman imaging, followed by thorough validation by specific molecular biological methods. The investigation revealed cell type-specific molecular responses after PAM treatment, including significant cell growth retardation in peritoneal fibroblasts due to transient DNA damage, cell cycle arrest and apoptosis. We identified a therapeutic dose window wherein specifically pro-adhesive peritoneal fibroblasts were targeted, whereas peritoneal mesothelial cells retained their anti-adhesive potential of epithelial wound closure. Finally, we demonstrate that PAM treatment of peritoneal fibroblasts reduced the expression and secretion of pro-adhesive cytokines and extracellular matrix proteins. Altogether, we provide insights into biochemical PAM mechanisms which lead to cell type-specific pro-therapeutic cell responses. This may open the door for the prevention of pro-adhesive clinical disorders.

Raman spectroscopy combined with comprehensive gas chromatography for label-free characterization of plasma-derived extracellular vesicle subpopulations

Raman spectroscopy together with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOFMS) was employed to characterize exomere- (<50 nm) and exosome-sized (50-80 nm) EVs isolated from human plasma by the novel on-line immunoaffinity chromatography - asymmetric flow field-flow fractionation method. CD9⁺, CD63⁺, and CD81⁺ EVs were selected to represent general EV subpopulations secreted into plasma, while CD61+EVs represented the specific EV subset derived from platelets. Raman spectroscopy could distinguish EVs from non-EV particles, including apolipoprotein B-100-containing lipoproteins, signifying its potential in EV purity assessment. Moreover, platelet-derived (CD61⁺) EVs of both exomere and exosome sizes were discriminated from other EV subpopulations due to different biochemical compositions. Further investigations demonstrated composition differences between exomere- and exosome-sized EVs, confirming the applicability of Raman spectroscopy in distinguishing EVs, not only from different origins but also sizes. In addition, fatty acids that act as building blocks for lipids and membranes in EVs were studied by GCxGC-TOF-MS. The results achieved highlighted differences in EV fatty acid compositions in both esterified (membrane lipids) and non-esterified (free fatty acids) fractions, indicating possible differences in membrane structures, biological functions, and roles in cell-to-cell communications of EV subpopulations.

Raman Spectroscopy as a Neuromonitoring Tool in Traumatic Brain Injury: A Systematic Review and Clinical Perspectives

Traumatic brain injury (TBI) is a significant global health problem, for which no disease-modifying therapeutics are currently available to improve survival and outcomes. Current neuromonitoring modalities are unable to reflect the complex and changing pathophysiological processes of the acute changes that occur after TBI. Raman spectroscopy (RS) is a powerful, label-free, optical tool which can provide detailed biochemical data in vivo. A systematic review of the literature is presented of available evidence for the use of RS in TBI. Seven research studies met the inclusion/exclusion criteria with all studies being performed in pre-clinical models. None of the studies reported the in vivo application of RS, with spectral acquisition performed ex vivo and one performed in vitro. Four further studies were included that related to the use of RS in analogous brain injury models, and a further five utilised RS in ex vivo biofluid studies for diagnosis or monitoring of TBI. RS is identified as a potential means to identify injury severity and metabolic dysfunction which may hold translational value. In relation to the available evidence, the translational potentials and barriers are discussed. This systematic review supports the further translational development of RS in TBI to fully ascertain its potential for enhancing patient care.

Raman Spectroscopy: In Vivo Application for Bone Evaluation in Oral Reconstructive (Regenerative) Surgery

The aim of this study was to evaluate the quality of the bone, revealing the different phases for calcified tissues independent of the medical history of the patient in relation to periodontitis by means of in vivo Raman spectroscopy. Raman spectroscopy measurements were performed in vivo during surgery and then ex vivo for the harvested bone samples for the whole group of patients (ten patients). The specific peaks for the Raman spectrum were traced for reference compounds (e.g., calcium phosphates) and bone samples. The variation in the intensity of the spectrum in relation to the specific bone constituents’ concentrations reflects the bone quality and can be strongly related with patient medical status (before dental surgery and after a healing period). Moreover, bone sample fluorescence is related to collagen content, enabling a complete evaluation of bone quality including a “quasi-quantification” of the healing process similar to the bone augmentation procedure. A complete evaluation of the processed spectra offers quantitative/qualitative information on the condition of the bone tissue. We conclude that Raman spectroscopy can be considered a viable investigation method for an in vivo and quick bone quality assessment during oral and periodontal surgery.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Simple Summary

Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology.

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Automated non-invasive identification of pelvic autonomic nerves with a handheld Raman spectrometer and potential application to nerve-sparing colorectal surgery: a preliminary study in surgical specimens

Background

Although minimally invasive surgery for colorectal cancer, whether performed as standard laparoscopic or robotic surgery, has been established as an oncologically safe procedure, postoperative urinary dysfunction and sexual dysfunction remain matters of concern, even when so-called nerve-sparing surgery is performed. We have hypothesized that Raman spectroscopy can be used intraoperatively as a non-invasive label-free means of objective identification of the pelvic nerves, and we conducted a preliminary study by applying a newly developed handheld Raman spectrometer to surgical specimens.

Methods

Samples of nervous tissue, colon cancer tissue, and tissues from surrounding pelvic organs were obtained from 25 patients undergoing colectomy. Raman spectra were obtained by irradiation with the Progeny™ Raman spectrometer. We looked for characteristic Raman shifts to distinguish nervous tissue from cancer tissue. To improve discrimination between nervous tissue and other tissues, the spectral data were subjected to principal component analysis.

Results

We detected characteristic differences in the spectra at 1,309 cm^-1, 1,442 cm^-1, and 1,658 cm^-1. A significant difference was detected at 1,442 cm^-1, and accuracy of the modality for identification of nervous tissue was 75%. The addition of principle component analysis (4 components) yielded 100% sensitivity, 85% specificity, and 90%, notably increasing accuracy from 75% to 90% in discriminating between nervous tissue and cancer tissue.

Conclusions

Raman spectroscopy holds promise for non-invasive intraoperative recognition of nervous tissue. We expect the modality to become a powerful clinical tool, compensating for the lack of tactile feedback intrinsic to minimally invasive colectomy and thus thwarting the risk of postoperative urinary and/or sexual dysfunction.

Raman spectroscopy biochemical characterisation of bladder cancer cisplatin resistance regulated by FDFT1: a review

Bladder cancer is the fourth most common malignancy in males. It can present across the whole continuum of severity, from mild through well-differentiated disease to extremely malignant tumours with poor survival rates. As with other vital organ malignancies, proper clinical management involves accurate diagnosis and staging. Chemotherapy consisting of a cisplatin-based regimen is the mainstay in the management of muscle-invasive bladder cancers. Control via cisplatin-based chemotherapy is threatened by the development of chemoresistance. Intracellular cholesterol biosynthesis in bladder cancer cells is considered a contributory factor in determining the chemotherapy response. Farnesyl-diphosphate farnesyltransferase 1 (FDFT1), one of the main regulatory components in cholesterol biosynthesis, may play a role in determining sensitivity towards chemotherapy compounds in bladder cancer. FDFT1-associated molecular identification might serve as an alternative or appendage strategy for early prediction of potentially chemoresistant muscle-invasive bladder cancer tissues. This can be accomplished using Raman spectroscopy. Developments in the instrumentation have led to it becoming one of the most convenient forms of analysis, and there is a highly realistic chance that it will become an effective tool in the pathology lab. Chemosensitive bladder cancer tissues tend to have a higher lipid content, more protein genes and more cholesterol metabolites. These are believed to be associated with resistance towards bladder cancer chemotherapy. Herein, Raman peak assignments have been tabulated as an aid to indicating metabolic changes in bladder cancer tissues that are potentially correlated with FDFT1 expression.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

Raman spectroscopy is a powerful tool in molecular paleobiology: An analytical response to Alleon et al. (https://doi.org/10.1002/bies.202000295)

A recent article argued that signals from conventional Raman spectroscopy of organic materials are overwhelmed by edge filter and fluorescence artefacts. The article targeted a subset of Raman spectroscopic investigations of fossil and modern organisms and has implications for the utility of conventional Raman spectroscopy in comparative tissue analytics. The inferences were based on circular reasoning centered around the unconventional analysis of spectra from just two samples, one modern, and one fossil. We validated the disputed signals with in situ Fourier-Transform Infrared (FT-IR) Spectroscopy and through replication with different lasers, filters, and operators in independent laboratories. Our Raman system employs a holographic notch filter which is not affected by edge filter or other artefacts. Multiple lines of evidence confirm that conventional Raman spectra of fossils contain biologically and geologically meaningful information. Statistical analyses of large Raman and FT-IR spectral data sets reveal patterns in fossil composition and yield valuable insights into the history of life.

Vibrational Imaging Techniques for the Characterization of Hard Dental Tissues: From Bench-Top to Chair-Side

Currently, various analytical techniques, including scanning electron microscopy, X-Ray diffraction, microcomputed tomography, and energy dispersive X-ray spectroscopy, are available to study the structural or elemental features of hard dental tissues. In contrast to these approaches, Raman Microspectroscopy (RMS) has the great advantage of simultaneously providing, at the same time and on the same sample, a morpho-chemical correlation between the microscopic information from the visual analysis of the sample and its chemical and macromolecular composition. Hence, RMS represents an innovative and non-invasive technique to study both inorganic and organic teeth components in vitro. The aim of this narrative review is to shed new light on the applicative potential of Raman Microspectroscopy in the dental field. Specific Raman markers representative of sound and pathological hard dental tissues will be discussed, and the future diagnostic application of this technique will be outlined. The objective and detailed information provided by this technique in terms of the structure and chemical/macromolecular components of sound and pathological hard dental tissues could be useful for improving knowledge of several dental pathologies. Scientific articles regarding RMS studies of human hard dental tissues were retrieved from the principal databases by following specific inclusion and exclusion criteria.

Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning

Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high-cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97-0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95-0.98) using a miniaturized spectral range (896-1039 cm^-1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.