Discrimination between ricin and sulphur mustard toxicity in vitro using Raman spectroscopy

Detection of serum alterations in polysubstance use patients by FT-Raman spectroscopy

Substance use disorders pose significant health risks and treatment challenges due to the diverse interactions between substances and their impact on physical and mental health. The chemical effects of multiple substance use on bodily fluids are not yet fully understood. Therefore, this study aimed to investigate the chemical changes induced by a combination of substances compared to a control group. Analysis of FT-Raman spectra revealed structural alterations in the amide III, I, and C = O functional groups of lipids in subjects treated with opioids, alcohol and cannabis (polysubstance group). These changes were evident in the form of peak shifts compared to the control group. Additionally, an imbalance in the amide-lipid ratio was observed, indicating perturbations in serum protein and lipid levels. Furthermore, a 2D plot of two-track two-dimensional correlation spectra (2T2D-COS) demonstrated a shift towards dominance of lipid vibrations in the polysubstance use groups, contrasting with the predominance of the amide fraction in the control group. This observation suggests distinct molecular changes induced by multiple substance use, potentially contributing to the pathophysiology of substance use disorders. Principal Component Analysis (PCA) was utilized to visualize the data structure and identify outliers. Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) was employed to classify the polysubstance use and control groups. The PLS-DA model demonstrated high classification accuracy, achieving 100.00 % in the training dataset and 94.74 % in the test dataset. Furthermore, receiver operating characteristic (ROC) analysis yielded perfect AUC values of 1.00 for both the training and test sets, underscoring the robustness of the classification model. This study highlights the quantitative and qualitative changes in serum protein and lipid levels induced by polysubstance use groups, as evidenced by FT-Raman spectroscopy. The findings underscore the importance of understanding the chemical effects of polysubstance use on bodily fluids for improved diagnosis and treatment of substance use disorders. Moreover, the successful classification of spectral data using machine learning techniques emphasizes the potential of these approaches in clinical applications for substance abuse monitoring and management.

The applicability of the SERS technique in food contamination testing - The detailed spectroscopic, chemometric, genetic, and comparative analysis of food-borne Cronobacter spp. strains

Microorganisms assigned as Cronobacter are Gram-negative, facultatively anaerobic, bacteria widely distributed in nature, home environments, and hospitals. They can also be detected in foods, milk powder, and powdered infant formula (PIF). Additionally, as an opportunistic pathogen, Cronobacter may cause serious infections, sometimes leading to the death of neonates and infants. Thus, it is essential to test food products for the presence of Cronobacter spp. The currently used standard described in ISO 22964:2017 is a laborious method that could be easily replaced by surface-enhanced Raman scattering (SERS). Here, we demonstrate that SERS allows the identification of food-borne bacteria belonging to Cronobacter spp. based on their SERS spectra. For this purpose, twenty-six Cronobacter strains from different food samples were analyzed. Additionally, it was shown that it is possible to differentiate them from other closely related pathogens such as Salmonella enterica subsp. enterica, Escherichia coli, or Enterobacter spp. The SERS results were supported by principal component analysis (PCA), as well as and sequencing of 16S rRNA, rpoB and fusA genes. Last but not least, it was demonstrated that the cells of Cronobacter sakazakii may be easily separated from PIF using an appropriate filter, microfluidic chip, and dielectrophoresis (DEP) technique.

Spectrochip-based Calibration Curve Modeling (CCM) for Rapid and Accurate Multiple Analytes Quantification in Urinalysis

Most urine test strips are intended to enable the general population to rapidly and easily diagnose potential renal disorders. It is semi-quantitative in nature, and although the procedure is straightforward, certain factors will affect the judgmental outcomes. This study describes rapid and accurate quantification of twelve urine test strip parameters: leukocytes, nitrite, urobilinogen, protein, pH, occult blood, specific gravity, ketone, bilirubin, glucose, microalbumin, and creatinine using a micro-electromechanical system (MEMS)-based spectrophotometer, known as a spectrochip. For each parameter, absorption spectra were measured three times independently at eight different concentration levels of diluted standard solutions, and the average spectral intensities were calculated to establish the calibration curve under the characteristic wavelength (λ c ). Then, regression analysis on the calibration curve was performed with GraphPad Prism software, which revealed that the coefficient of determination (R 2 ) of the modeled calibration curves was greater than 0.95. This result illustrates that the measurements exceed standard levels, confirming the importance of a spectrochip for routine multi-parameter urine analysis. Thus, it is possible to obtain the spectral signal strength for each parameter at its characteristic wavelength in order to compare directly with the calibration curves in the future, even in situations when sample concentration is unknown. Additionally, the use of large testing machines can be reduced in terms of cost, time, and space by adopting a micro urine testing platform based on spectrochip, which also improves operational convenience and effectively enables point-of-care (POC) testing in urinalysis.

Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak

Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.

Raman Spectroscopy of Blood Serum for Essential Thrombocythemia Diagnosis: Correlation with Genetic Mutations and Optimization of Laser Wavelengths

Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the analysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended. Disease poses diagnostic challenges due to overlapping mutations with other neoplasms and the presence of triple-negative cases. This study explores the potential of Raman spectroscopy combined with machine learning for ET diagnosis. We assessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, patients having no mutations were also observed in our group, making diagnosis challenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum.

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.

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

In histopathology, it is highly crucial to have chemical and structural information about tissues. Additionally, the segmentation of zones within a tissue plays a vital role in investigating the functions of these regions for better diagnosis and treatment. The placenta plays a vital role in embryonic and fetal development and in diagnosing some diseases associated with its dysfunction. This study provides a label-free approach to obtain the images of mature mouse placenta together with the chemical differences between the tissue compartments using Raman spectroscopy. To generate the Raman images, spectra of placental tissue were collected using a custom-built optical setup. The pre-processed spectra were analyzed using statistical and machine learning methods to acquire the Raman maps. We found that the placental regions called decidua and the labyrinth zone are biochemically distinct from the junctional zone. A histologist performed a comparison and evaluation of the Raman map with histological images of the placental tissue, and they were found to agree. The results of this study show that Raman spectroscopy offers the possibility of label-free monitoring of the placental tissue from mature mice while simultaneously revealing crucial structural information about the zones.

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes

Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.

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.

Cancer diagnosis using label-free SERS-based exosome analysis

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.

Performance of Classification Models of Toxins Based on Raman Spectroscopy Using Machine Learning Algorithms

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky-Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

Quantification of skin penetration of caffeine and propylene glycol applied topically in a mixture by tailored multivariate curve resolution-alternating least squares of depth-resolved Raman spectra

The quantitative determination of topically applied substances in the skin is severely limited and represents a challenging task. The porcine skin ex vivo was topically treated with a gel containing caffeine (CF) and propylene glycol (PG), and depth-resolved Raman spectra were recorded with two confocal Raman microscopes. We applied a novel tailored multivariate curve resolution-alternating least squares method to the selected spectral regions (512-604 and 778-1148 cm-1 ) of gel-treated skin and quantitatively determined the concentrations of CF and PG in the stratum corneum (SC). The highest concentration of CF (181 mg/cm3 ) was found at the surface, while PG (384 mg/cm3 ) was found at 10% SC depth, indicating the formation of a reservoir at the superficial SC. The concentrations of CF and PG decreased monotonically and reached the detection limit at ≈60% and ≈80% SC depth, respectively, indicating that neither permeate the SC.

FT-Raman data analyzed by multivariate and machine learning as a new methods for detection spectroscopy marker of platinum-resistant women suffering from ovarian cancer

The phenomenon of platinum resistance is a very serious problem in the treatment of ovarian cancer. Unfortunately, no molecular, genetic marker that could be used in assigning women suffering from ovarian cancer to the platinum-resistant or platinum-sensitive group has been discovered so far. Therefore, in this study, for the first time, we used FT-Raman spectroscopy to determine chemical differences and chemical markers presented in serum, which could be used to differentiate platinum-resistant and platinum-sensitive women. The result obtained showed that in the serum collected from platinum-resistant women, a significant increase of chemical compounds was observed in comparison with the serum collected from platinum-sensitive woman. Moreover, a decrease in the ratio between amides vibrations and shifts of peaks, respectively, corresponding to C-C/C-N stretching vibrations from proteins, amide III, amide II, C = O and CH lipids vibrations suggested that in these compounds, structural changes occurred. The Principal Component Analysis (PCA) showed that using FT-Raman range, where the above-mentioned functional groups were present, it was possible to differentiate the serum collected from both analyzed groups. Moreover, C5.0 decision tree clearly showed that Raman shifts at 1224 cm-1 and 2713 cm-1 could be used as a marker of platinum resistance. Importantly, machine learning methods showed that the accuracy, sensitivity and specificity of the FT-Raman spectroscopy were from 95 to 100%.

Raman Multi-Omic Snapshot and Statistical Validation of Structural Differences between Herpes Simplex Type I and Epstein-Barr Viruses

Raman spectroscopy was applied to study the structural differences between herpes simplex virus Type I (HSV-1) and Epstein-Barr virus (EBV). Raman spectra were first collected with statistical validity on clusters of the respective virions and analyzed according to principal component analysis (PCA). Then, average spectra were computed and a machine-learning approach applied to deconvolute them into sub-band components in order to perform comparative analyses. The Raman results revealed marked structural differences between the two viral strains, which could mainly be traced back to the massive presence of carbohydrates in the glycoproteins of EBV virions. Clear differences could also be recorded for selected tyrosine and tryptophan Raman bands sensitive to pH at the virion/environment interface. According to the observed spectral differences, Raman signatures of known biomolecules were interpreted to link structural differences with the viral functions of the two strains. The present study confirms the unique ability of Raman spectroscopy for answering structural questions at the molecular level in virology and, despite the structural complexity of viral structures, its capacity to readily and reliably differentiate between different virus types and strains.

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.

Instantaneous Inactivation of Herpes Simplex Virus by Silicon Nitride Bioceramics

Hydrolytic reactions taking place at the surface of a silicon nitride (Si3N4) bioceramic were found to induce instantaneous inactivation of Human herpesvirus 1 (HHV-1, also known as Herpes simplex virus 1 or HSV-1). Si3N4 is a non-oxide ceramic compound with strong antibacterial and antiviral properties that has been proven safe for human cells. HSV-1 is a double-stranded DNA virus that infects a variety of host tissues through a lytic and latent cycle. Real-time reverse transcription (RT)-polymerase chain reaction (PCR) tests of HSV-1 DNA after instantaneous contact with Si3N4 showed that ammonia and its nitrogen radical byproducts, produced upon Si3N4 hydrolysis, directly reacted with viral proteins and fragmented the virus DNA, irreversibly damaging its structure. A comparison carried out upon testing HSV-1 against ZrO2 particles under identical experimental conditions showed a significantly weaker (but not null) antiviral effect, which was attributed to oxygen radical influence. The results of this study extend the effectiveness of Si3N4's antiviral properties beyond their previously proven efficacy against a large variety of single-stranded enveloped and non-enveloped RNA viruses. Possible applications include the development of antiviral creams or gels and oral rinses to exploit an extremely efficient, localized, and instantaneous viral reduction by means of a safe and more effective alternative to conventional antiviral creams. Upon incorporating a minor fraction of micrometric Si3N4 particles into polymeric matrices, antiherpetic devices could be fabricated, which would effectively impede viral reactivation and enable high local effectiveness for extended periods of time.

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Label-Free Quantification of Microscopic Alignment in Engineered Tissue Scaffolds by Polarized Raman Spectroscopy

Monitoring of extracellular matrix (ECM) microstructure is essential in studying structure-associated cellular processes, improving cellular function, and for ensuring sufficient mechanical integrity in engineered tissues. This paper describes a novel method to study the microscale alignment of the matrix in engineered tissue scaffolds (ETS) that are usually composed of a variety of biomacromolecules derived by cells. First, a trained loading function was derived from Raman spectra of highly aligned native tissue via principal component analysis (PCA), where prominent changes associated with specific Raman bands (e.g., 1444, 1465, 1605, 1627-1660, and 1665-1689 cm-1) were detected with respect to the polarization angle. These changes were mainly caused by the aligned matrix of many compounds within the tissue relative to the laser polarization, including proteins, lipids, and carbohydrates. Hence this trained function was applied to quantify the alignment within ETS of various matrix components derived by cells. Furthermore, a simple metric called Amplitude Alignment Metric (AAM) was derived to correlate the orientation dependence of polarized Raman spectra of ETS to the degree of matrix alignment. It was found that the AAM was significantly higher in anisotropic ETS than isotropic ones. The PRS method revealed a lower p-value for distinguishing the alignment between these two types of ETS as compared to the microscopic method for detecting fluorescent-labeled protein matrices at a similar microscopic scale. These results indicate that the anisotropy of a complex matrix in engineered tissue can be assessed at the microscopic scale using a PRS-based simple metric, which is superior to the traditional microscopic method. This PRS-based method can serve as a complementary tool for the design and assessment of engineered tissues that mimic the native matrix organizational microstructures.

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.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Chemical changes in childhood obesity blood as a marker of the disease. A Raman-based machine learning study

Obesity in children is a global problem, leading to different medical conditions that may contribute to metabolic syndrome and increase the risk of diabetes, dyslipidemia, hypertension, and cardiovascular diseases in future health. Metabolic disorders are the results of the body's chemical process. The changes in the chemical compositions could be determined by Raman spectroscopy. Therefore, in this study, we measured blood collected from children with obesity to show chemical changes caused by obesity disease. Moreover, we will also show characteristic Raman peaks/regions, which could be used as a marker of obesity, not other metabolic syndromes. Children with obesity had higher glucose levels, proteins, and lipids than the control ones. Furthermore, it was noticed that the ratio between CO and C-H is 0.23 in control patients and 0.31 in children with obesity, as well as the ratio between amide II and amide I was 0.72 in control and 1.15 in obesity, which suggests an imbalance in these two fractions in childhood obesity. PCA with discrimination analyses showed that the accuracy, selectivity, and specificity of Raman spectroscopy in differentiation between childhood obesity and healthy children was between 93% and 100%. There is an increased risk of metabolic changes in childhood obesity with higher glucose levels, lipids, and proteins in children with obesity. Also, there were differences in the ratio between proteins and lipids functional groups and glucose, amide II, and amide I vibrations as a marker of obesity. The results of the study offer valuable insights into potential alterations in protein structure and lipid composition in children with obesity, emphasizing the importance of considering metabolic changes beyond traditional anthropometric, measurements.

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 microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra

Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm^-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.

Eggshell Membrane as a Biomaterial for Bone Regeneration

The physicochemical features of the avian eggshell membrane play an essential role in the process of calcium carbonate deposition during shell mineralization, giving rise to a porous mineralized tissue with remarkable mechanical properties and biological functions. The membrane could be useful by itself or as a bi-dimensional scaffold to build future bone-regenerative materials. This review focuses on the biological, physical, and mechanical properties of the eggshell membrane that could be useful for that purpose. Due to its low cost and wide availability as a waste byproduct of the egg processing industry, repurposing the eggshell membrane for bone bio-material manufacturing fulfills the principles of a circular economy. In addition, eggshell membrane particles have has the potential to be used as bio-ink for 3D printing of tailored implantable scaffolds. Herein, a literature review was conducted to ascertain the degree to which the properties of the eggshell membrane satisfy the requirements for the development of bone scaffolds. In principle, it is biocompatible and non-cytotoxic, and induces proliferation and differentiation of different cell types. Moreover, when implanted in animal models, it elicits a mild inflammatory response and displays characteristics of stability and biodegradability. Furthermore, the eggshell membrane possesses a mechanical viscoelastic behavior comparable to other collagen-based systems. Overall, the biological, physical, and mechanical features of the eggshell membrane, which can be further tuned and improved, make this natural polymer suitable as a basic component for developing new bone graft materials.

Spectroscopic signature of ZnO NP-induced cell death modalities assessed by non-negative PCA

Selective cytotoxicity of ZnO nanoparticles among different cell types and cancer and non-cancerous cells has been demonstrated earlier. In the view of anticancer potential of ZnO nanoparticles and their presence in numerous industrial products, it is of great importance to carefully evaluate their effects and mechanisms of action in both cancerous and healthy cells. In this paper, the effects of ZnO nanoparticles on cancerous HeLa and non-cancerous MRC-5 cells are investigated by studying the changes in the vibrational properties of the cells using Raman spectroscopy. Both types of cells were incubated with ZnO nanoparticles of average size 40 nm in the doses from the range 10-40 µg/ml for the period of 48 h, after which Raman spectra were collected. Raman modes' intensity ratios I₁₆₅₉/I₁₄₄₄, I₂₈₅₅/I₂₉₃₃ and I₁₃₃₇/I₁₃₀₅ were determined as spectral markers of the cytotoxic effect of ZnO in both cell types. Non-negative principal component analysis was used instead of standard one for analysis and detection of spectral features characteristic for nanoparticle-treated cells. The first several non-negative loading vectors obtained in this analysis coincided remarkably well with the Raman spectra of particular biomolecules, showing increase of lipid and decrease of nucleic acids and protein content. Our study pointed out that Raman spectral markers of lipid unsaturation, especially I₁₂₇₀/I₁₃₀₀, are relevant for tracing the cytotoxic effect of ZnO nanoparticles on both cancerous and non-cancerous cells. The change of these spectral markers is correlated to the dose of applied nanoparticles and to the degree of cellular damage. Furthermore, great similarity of spectral features of increasing lipids to spectral features of phosphatidylserine, one of the main apoptotic markers, was recognized in treated cells. Finally, the results strongly indicated that the degree of lipid saturation, presented in the cells, plays an important role in the interaction of cells with nanoparticles.

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Probing Individual Particles Generated at the Freshwater-Seawater Interface through Combined Raman, Photothermal Infrared, and X-ray Spectroscopic Characterization

Sea spray aerosol (SSA) is one of the largest global sources of atmospheric aerosol, but little is known about SSA generated in coastal regions with salinity gradients near estuaries and river outflows. SSA particles are chemically complex with substantial particle-to-particle variability due to changes in water temperature, salinity, and biological activity. In previous studies, the ability to resolve the aerosol composition to the level of individual particles has proven necessary for the accurate parameterization of the direct and indirect aerosol effects; therefore, measurements of individual SSA particles are needed for the characterization of this large source of atmospheric aerosol. An integrated analytical measurement approach is required to probe the chemical composition of individual SSA particles. By combining complementary vibrational microspectroscopic (Raman and optical photothermal infrared, O-PTIR) measurements with elemental information from computer-controlled scanning electron microscopy with energy-dispersive X-ray analysis (CCSEM-EDX), we gained unique insights into the individual particle chemical composition and morphology. Herein, we analyzed particles from four experiments on laboratory-based SSA production using coastal seawater collected in January 2018 from the Gulf of Maine. Individual salt particles were enriched in organics compared to that in natural seawater, both with and without added microalgal filtrate, with greater enrichment observed for smaller particle sizes, as evidenced by higher carbon/sodium ratios. Functional group analysis was carried out using the Raman and infrared spectra collected from individual SSA particles. Additionally, the Raman spectra were compared with a library of Raman spectra consisting of marine-derived organic compounds. Saccharides, followed by fatty acids, were the dominant components of the organic coatings surrounding the salt cores of these particles. This combined Raman, infrared, and X-ray spectroscopic approach will enable further understanding of the factors determining the individual particle composition, which is important for understanding the impacts of SSA produced within estuaries and river outflows, as well as areas of snow and ice melt.

Specific intracellular signature of SARS-CoV-2 infection using confocal Raman microscopy

SARS-CoV-2 infection remains spread worldwide and requires a better understanding of virus-host interactions. Here, we analyzed biochemical modifications due to SARS-CoV-2 infection in cells by confocal Raman microscopy. Obtained results were compared with the infection with another RNA virus, the measles virus. Our results have demonstrated a virus-specific Raman molecular signature, reflecting intracellular modification during each infection. Advanced data analysis has been used to distinguish non-infected versus infected cells for two RNA viruses. Further, classification between non-infected and SARS-CoV-2 and measles virus-infected cells yielded an accuracy of 98.9 and 97.2 respectively, with a significant increase of the essential amino-acid tryptophan in SARS-CoV-2-infected cells. These results present proof of concept for the application of Raman spectroscopy to study virus-host interaction and to identify factors that contribute to the efficient SARS-CoV-2 infection and may thus provide novel insights on viral pathogenesis, targets of therapeutic intervention and development of new COVID-19 biomarkers.

Data-Driven Identification of Biomarkers for In Situ Monitoring of Drug Treatment in Bladder Cancer Organoids

Three-dimensional (3D) organoid culture recapitulating patient-specific histopathological and molecular diversity offers great promise for precision medicine in cancer. In this study, we established label-free imaging procedures, including Raman microspectroscopy (RMS) and fluorescence lifetime imaging microscopy (FLIM), for in situ cellular analysis and metabolic monitoring of drug treatment efficacy. Primary tumor and urine specimens were utilized to generate bladder cancer organoids, which were further treated with various concentrations of pharmaceutical agents relevant for the treatment of bladder cancer (i.e., cisplatin, venetoclax). Direct cellular response upon drug treatment was monitored by RMS. Raman spectra of treated and untreated bladder cancer organoids were compared using multivariate data analysis to monitor the impact of drugs on subcellular structures such as nuclei and mitochondria based on shifts and intensity changes of specific molecular vibrations. The effects of different drugs on cell metabolism were assessed by the local autofluorophore environment of NADH and FAD, determined by multiexponential fitting of lifetime decays. Data-driven neural network and data validation analyses (k-means clustering) were performed to retrieve additional and non-biased biomarkers for the classification of drug-specific responsiveness. Together, FLIM and RMS allowed for non-invasive and molecular-sensitive monitoring of tumor-drug interactions, providing the potential to determine and optimize patient-specific treatment efficacy.

Correlation between endometriomas volume and Raman spectra. Attempting to use Raman spectroscopy in the diagnosis of endometrioma

The formation of the uterus lining, i.e. the endometrium, outside the uterus (ex. in the abdominal cavity,ovaries,or anywhere in the body) is called endometriosis. The presence of endometrial tissue present in the ovaries, thickens after menstruation, leading to menstrual-like bleeding and to the formation of chocolate cyst (Endometrioma) because of the accumulation of old, brown blood in the ovary. It is still unknown, what triggers the development ofendometrioma. However,it leads to excessive bleeding during menstrual periods or abnormal bleeding between periods and infertility. Endometriosis is often first diagnosed in those who seek medical attention for infertility. Therefore, new markers of endometrioma as well as new methods of its diagnosis are sought. In this study we used Raman spectra of serum collected from 50 healthy women and 50 women suffering from endometriosis. The obtained Raman data were used in multivariateanalysis to determine the Raman range, which can be used for endometriomadiagnostics. Partial Least Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) showed, that it is possible to distinguish between the serum collected from healthy and un-healthy women using the Raman range between 800 cm^-1 and 1800 cm^-1 and between 2956 cm^-1 and 2840 cm^-1, while the first range corresponds to the fingerprint region and the second one to lipids vibrations. Consequently, the Pearson correlation test showeda significantpositive correlation betweenvaluesoflipidintensity in Raman spectra and volume of endometriomas. Summarizing, Raman spectroscopy can be a helpful tool in endometrioma diagnosis and the lipid vibrations are candidates for being a spectroscopic marker of the disease being studied.

A micro-Raman spectroscopy study of inflammatory condition of human cervix: Probing of Tissues and blood plasma samples

The present study explores the application of the micro-Raman spectroscopy technique to discriminate normal and cervicitis condition from cervical malignancy by analyzing the Raman signatures of tissues and plasma samples of the same subjects. The Raman peaks from tissue samples at 1026 cm^-1,1298 cm^-1 and 1243 cm^-1 are attributed to glycogen, fatty acids and collagen and are found to be reliable signatures capable of identifying cervicitis and normal condition from cervical cancer. The Raman signatures from plasma samples belonging to carbohydrates (578 cm^-1), lipids (1059 cm^-1) and nucleic acids (1077 cm^-1,1341 cm^-1 and 1357 cm^-1) are quite useful to classify various stages of cervix at par with tissue based diagnosis. The PCA-SVM based classification of the spectral data indicates the potential of Raman spectroscopy based liquid biopsy to rule out false diagnosis of cervicitis as cervical malignancy.

Identification of polycystic ovary syndrome from blood serum using hormone levels via Raman spectroscopy and multivariate analysis

Polycystic ovarian syndrome (PCOS) is a disease, which causes infertility in women. The factors for the development of the disease are still not well understood and diagnostic methods need to be improved. Therefore, in this study, Raman spectroscopy as a potential diagnostic tool, was investigated and spectra of blood serum were collected from PCOS and healthy women. The obtained spectra showed distinct changes in intensities as well as shift of peaks for the blood serum collected from PCOS compared to healthy individuals. Partial Last Square (PLS) analysis and Principal Component Analysis (PCA) allowed to determine that Raman shifts of amides (1500 - 1700 cm^-1) and CH₂, CH₃ lipid groups (2700 - 3000 cm^-1), could be thus used as potential PCOS markers. Furthermore, the Pearson correlation test showed a strong correlation between hormones (lutropin (LH), prolactin (PRL), follicle-stimulating (FSH), dehydroepiandrosterone (DHEAS), thyroid-stimulating (TSH), Estradiol) and lipids, as well as between hormones and protein functional groups in PCOS women, compared to the control. These results show, that the lipid and protein balance could be potentially applied as a helpful PCOS marker in Raman spectra.

Differential of cholangiocarcinoma disease using Raman spectroscopy combined with multivariate analysis

Cholangiocarcinoma (CCA) is a type of cancer, which 5-year survival is lower than 20 %, and which is detected mostly in advanced stage of the disease. Unfortunately, there are no diagnostic tools, which could show changes in the body indicating the development of the disease. Therefore, in this study, we investigate Raman spectroscopy as a promising analytical tool in medical diagnostics and as a method, which would allow to distinguish between healthy patients and patients suffering from cholangiocarcinoma. The obtained Raman spectra showed, that lower intensities of peaks corresponding to amino acids and proteins, as well as higher intensities of peaks originating from lipids vibrations were observed in healthy individuals in comparison with cancer patients. Moreover, Partial Last Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) of Raman spectra indicate that the ranges between 800 cm^-1 and 1800 cm^-1, 3477 cm^-1 -3322 cm^-1 and 1394 cm^-1 -1297 cm^-1 allow to distinguish cancer patients from healthy ones. The obtained results showed, that Raman spectroscopy is a good candidate, to become in future one of the diagnostic tools of Cholangiocarcinoma.

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.

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embedded (FFPE) and frozen specimens were obtained using a confocal Raman spectrometer and analyzed with machine learning and deep learning algorithms. We discovered characteristic Raman shifts in the GCTB specimens. They were assigned to phenylalanine and tyrosine. Based on the spectroscopic data, classification algorithms including support vector machine, k-nearest neighbors and long short-term memory (LSTM) were successfully applied to discriminate GCTB from adjacent normal tissues of both the FFPE and frozen specimens, with the accuracy ranging from 82.8% to 94.5%. Importantly, our LSTM algorithm showed the best performance in the discrimination of the frozen specimens, with a sensitivity and specificity of 93.9% and 95.1% respectively, and the AUC was 0.97. The results of our study suggest that confocal Raman spectroscopy accomplished by the LSTM network could non-destructively evaluate a tumor margin by its inherent biochemical specificity which may allow intraoperative assessment of the adequacy of tumor clearance.

Raman Research on Bleomycin-Induced DNA Strand Breaks and Repair Processes in Living Cells

Even several thousands of DNA lesions are induced in one cell within one day. DNA damage may lead to mutations, formation of chromosomal aberrations, or cellular death. A particularly cytotoxic type of DNA damage is single- and double-strand breaks (SSBs and DSBs, respectively). In this work, we followed DNA conformational transitions induced by the disruption of DNA backbone. Conformational changes of chromatin in living cells were induced by a bleomycin (BLM), an anticancer drug, which generates SSBs and DSBs. Raman micro-spectroscopy enabled to observe chemical changes at the level of single cell and to collect hyperspectral images of molecular structure and composition with sub-micrometer resolution. We applied multivariate data analysis methods to extract key information from registered data, particularly to probe DNA conformational changes. Applied methodology enabled to track conformational transition from B-DNA to A-DNA upon cellular response to BLM treatment. Additionally, increased expression of proteins within the cell nucleus resulting from the activation of repair processes was demonstrated. The ongoing DNA repair process under the BLM action was also confirmed with confocal laser scanning fluorescent microscopy.

A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum

This research innovatively combines FT-IR, Raman spectroscopy and their first-derivative spectroscopy to develop a rapid diagnosis method for renal cell carcinoma (RCC). After measuring the Raman spectra and FT-IR spectra of 45 cases of control subjects and 28 cases of RCC, the first derivative of the infrared spectra and the Raman spectra were calculated respectively. Principal component analysis (PCA) was used to extract the features of the infrared spectra, first-derivative infrared spectra, Raman spectra and first-derivative Raman spectra. Then the four feature matrices were merged as fused spectral feature matrices. The fused matrices were used as the input of AlexNet and MCNN. The fused spectral feature matrices were used as the input of AlexNet and MCNN. The adjusted AlexNet model performed better, and the classification accuracy of the fused spectral data is 93%. Compared with the classification results of infrared spectra (74%), Raman spectra (75%) and the fusion of infrared and Raman spectra (79%) combined with the adjusted AlexNet model, the classification result of the fusion of infrared spectra, Raman spectra and their first-derivative was significantly improved. The experimental results show that infrared spectroscopy, Raman spectroscopy and their first-derivative fusion technology combined with deep learning algorithms has great potential in the diagnosis of RCC.

Surface-enhanced Raman spectroscopic analysis of centrifugally filtered HBV serum samples

BACKGROUND

Raman spectroscopy is an effective tool for detecting and discriminating centrifugally filtered hepatitis B virus serum and centrifugally filtered  control serum.

OBJECTIVES

The purpose of current study is to separate high molecular weight fractions from low molecular weight fractions present hepatitis B serum to increase the disease diagnostic ability of surface enhanced Raman spectroscopy (SERS).

METHODS

Clinically diagnosed centrifugally filtered serum samples of hepatitis B patients are subjected for surface enhanced Raman spectroscopy (SERS) in comparison with centrifugally filtered serum samples of healthy individuals by using silver nanoparticles (Ag-NPs) as SERS substrates. Some SERS spectral features are solely observed in centrifugally filtered serum samples of hepatitis B and some SERS spectral are solely observed in centrifugally filtered serum samples of healthy individuals. The diagnostic ability of SERS is further enhanced with different statistical techniques like principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and partial least square regression analysis (PLSR) have applied.

RESULTS

The disease biomarkers of hepatitis B are more pronounced after their centrifugation as compared with uncentrifuged form. Statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) clearly differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. Furthermore, partial least square regression analysis (PLSR) has been applied for predicting unknown viral load of centrifugally filtered serum sample of hepatitis B.

CONCLUSION

SERS technique along with chemometric tools have successfully differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. The centrifugal filtration process has increased the differentiation accuracy of PLS-DA in terms of percentage 98% and regression accuracy of PLSR regression analysis in terms of RMSEP (0.30 IU/mL) of this diagnostic method as compared with that of uncentrifuged method.

The Specific Changes of Urine Raman Spectra Can Serve as Novel Diagnostic Tools for Disease Characteristics in Patients with Crohn’s Disease

Purpose

Crohn’s disease (CD) is a chronic recurrent intestinal inflammatory disease that requires repeated invasive examinations. Convenient and noninvasive diagnostic tools for CD are lacking. Surface-enhanced Raman spectroscopy (SERS) can rapidly provide specific metabolite information in various samples. Our previous study has showed urine Raman spectrum can distinguish CD patients from healthy controls noninvasively. In this study, we further investigated the value of urine Raman spectra on identifying the disease characterizations in patients with CD.

Patients and Methods

Urine samples were analyzed by SERS to acquire specific changes of the spectra from 100 active CD (aCD) patients and 88 inactive CD (iCD) patients. The accuracy of classifier models yielded by SERS was assessed by principal component analysis and support vector machine (PCA-SVM) to investigate spectral differences and disease characterizations.

Results

Given a panel of 16 specific Raman spectra, the classifier model was established to predict disease activity between patients with aCD and iCD and achieved higher efficacy than fecal calprotectin (AUC value, 0.864 vs 0.596, P=0.02). After leave-one-patient-out cross-validation, the classifier model still obtained 75.5% of accuracy. The correlation analysis showed it had negative correlation with endoscopic results (r=−0.616, P<0.0001). We further established the classifier model in identifying disease location to discriminate colonic-type from ileal-type CD with 63.6% of accuracy with the significantly increased intensity of 1643 cm⁻¹ band, and the model to predict the spectra changes of before and after treatment in tumor necrosis factor inhibitor responders with 91.2% of accuracy with a panel of 11 specific spectra. The metabolic changes of amino acids, proteins, lipids, and other compounds in urine levels were noted by SERS in patients with CD.

Conclusion

The specific changes of urine Raman spectra can reflect changes in urine metabolism. It has the potential value on being the promising diagnostic tool for disease characterizations in CD patients by a convenient and noninvasive way.

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.

Non-invasive diagnosis of Crohn's disease based on SERS combined with PCA-SVM

Crohn's disease (CD) is an idiopathic chronic inflammatory bowel disease without a cure. Most of the CD patients are firstly diagnosed by invasive endoscopy, and clinical and pathological examinations are further required to confirm the diagnosis. Hence, the development of a non-invasive, rapid and accurate diagnosis method for CD patients is essential. In this study, urine samples from 95 CD patients (including 58 active CD (aCD) patients and 37 inactive CD (iCD) patients) and 48 healthy controls (HC) were investigated by surface-enhanced Raman spectroscopy (SERS). The statistical analysis of the three groups (i.e., CD/HC, aCD/HC and iCD/HC) was performed on the measured data. Principal component analysis (PCA)-support vector machine (SVM) and PCA-linear discriminant analysis (LDA) were then employed to establish classification models to distinguish between patients and HC. For the average SERS spectra of patients and HC, the Raman peaks belonging to lipids, proteins and nucleic acids were stronger in patients than those in HC. It showed that the classification accuracy of CD/HC based on PCA-SVM was higher than that of PCA-LDA (82.5% vs. 69.9%). And the classification accuracy of aCD/HC based on PCA-SVM was higher than that of iCD/HC (86.8% vs. 76.5%). The classification model we established distinguished between aCD and HC with 86.2% sensitivity and 87.5% specificity. It indicates that the metabolic change of patients could be identified by measuring urine with SERS, and aCD and HC could be distinguished more effectively. Our findings are helpful for clinicians to diagnose CD patients and monitor the progress and recurrence of the disease.

Surface-enhanced Raman spectroscopy for identification of food processing bacteria

Food processing bacteria play important role in providing flavors, ingredients and other beneficial characteristics to the food but at the same time some bacteria are responsible for food spoilage. Therefore, quick and reliable identification of these food processing bacteria is very necessary for the differentiation between different species which may help in the development of more useful food processing methodologies. In this study, analysis of different bacterial species (Lactobacillus fermentum, Fructobacillus fructosus, Pediococcus pentosaceus and Halalkalicoccus jeotgali) was performed with our in-house developed Ag NPs-based surface-enhanced Raman spectroscopy (SERS) method. The SERS spectral data was analyzed by multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Bacterial species were differentiated on the basis of SERS spectral features and potential of SERS was compared with the Raman spectroscopy (RS). SERS along with PCA and PLS-DA was found to be an efficient technique for identification and differentiation of food processing bacterial species. Differentiation with accuracy of 99.5% and sensitivity of 99.7% was depicted by PLS-DA model using leave one out cross validation.

Surface-enhanced Raman spectroscopy for analysis of PCR products of viral RNA of hepatitis C patients

In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

Culture of rat mesenchymal stem cells on PHBV-PCL scaffolds: analysis of conditioned culture medium by FT-Raman spectroscopy

Mesenchymal stem cells (MSCs) have great potential for application in cell therapy and tissue engineering procedures because of their plasticity and capacity to differentiate into different cell types. Given the widespread use of MSCs, it is necessary to better understand some properties related to osteogenic differentiation, particularly those linked to biomaterials used in tissue engineering. The aim of this study was to develop an analysis method using FT-Raman spectroscopy for the identification and quantification of biochemical components present in conditioned culture media derived from MSCs with or without induction of osteogenic differentiation. All experiments were performed between passages 3 and 5. For this analysis, MSCs were cultured on scaffolds composed of bioresorbable poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) and poly(ε-caprolactone) (PCL) polymers. MSCs (GIBCO®) were inoculated onto the pure polymers and 75:25 PHBV/PCL blend (dense and porous samples). The plate itself was used as control. The cells were maintained in DMEM (with low glucose) containing GlutaMAX® and 10% FBS at 37oC with 5% CO2 for 21 days. The conditioned culture media were collected and analyzed to probe for functional groups, as well as possible molecular variations associated with cell differentiation and metabolism. The method permitted to identify functional groups of specific molecules in the conditioned medium such as cholesterol, phosphatidylinositol, triglycerides, beta-subunit polypeptides, amide regions and hydrogen bonds of proteins, in addition to DNA expression. In the present study, FT-Raman spectroscopy exhibited limited resolution since different molecules can express similar or even the same stretching vibrations, a fact that makes analysis difficult. There were no variations in the readings between the samples studied. In conclusion, FT-Raman spectroscopy did not meet expectations under the conditions studied.