Advantages and limitations of Raman spectroscopy for molecular diagnostics: an update

Raman microscopy allows to follow internalization, subcellular accumulation and fate of iron oxide nanoparticles in cells

An important issue in the context of both potenial toxicity of iron oxide nanoparticles (IONP) and their medical applications is tracking of the internalization process of these nanomaterials into living cells, as well as their localization and fate within them. The typical methods used for this purpose are transmission electron microscopy, confocal fluorescence microscopy as well as light-scattering techniques including dark-field microscopy and flow cytometry. All the techniques mentioned have their advantages and disadvantages. Among the problems it is necessary to mention complicated sample preparation, difficult interpretation of experimental data requiring qualified and experienced personnel, different behavior of fluorescently labeled IONP comparing to those label-free or finally the lack of possibility of chemical composition characteristics of nanomaterials. The purpose of the present investigation was the assessment of the usefulness of Raman microscopy for the tracking of the internalization of IONP into cells, as well as the optimization of this process. Moreover, the study focused on identification of the potential differences in the cellular fate of superparamagnetic nanoparticles having magnetite and maghemite core. The Raman spectra of U87MG cells which internalized IONP presented additional bands which position depended on the used laser wavelength. They occurred at the wavenumber range 1700-2400 cm-1 for laser 488 nm and below the wavenumber of 800 cm-1 in case of laser 532 nm. The intensity of the mentioned Raman bands was higher for the green laser (532 nm) and their position, was independent and not characteristic on the primary core material of IONP (magnetite, maghemite). The obtained results showed that Raman microscopy is an excellent, non-destructive and objective technique that allows monitoring the process of internalization of IONP into cells and visualizing such nanoparticles and/or their metabolism products within them at low exposure levels. What is more, the process of tracking IONP using the technique may be further improved by using appropriate wavelength and power of the laser source.

Evaluation of Ilex guayusa and Piper marginatum Extract Cytotoxicity on Human Dental Pulp Mesenchymal Stem Cells

BACKGROUND

Amelogenesis imperfecta is a hereditary disorder affecting dental enamel. Among its phenotypes, hypocalcified AI is characterized by mineral deficiency, leading to tissue wear and, consequently, dental sensitivity. Excessive fluoride intake (through drinking water, fluoride supplements, toothpaste, or by ingesting products such as pesticides or insecticides) can lead to a condition known as dental fluorosis, which manifests as stains and teeth discoloration affecting their structure. Our recent studies have shown that extracts from Colombian native plants, Ilex guayusa and Piper marginatum, deposit mineral ions such as phosphate and orthophosphate into the dental enamel structure; however, it is unknown whether these extracts produce toxic effects on the dental pulp.

OBJECTIVE

To assess cytotoxicity effects on human dental pulp stem cells (hDPSCs) exposed to extracts isolated from I. guayusa and P. marginatum and, hence, their safety for clinical use.

METHODS

Raman spectroscopy, fluorescence microscopy, and flow cytometry techniques were employed. For Raman spectroscopy, hDPSCs were seeded onto nanobiochips designed to provide surface-enhanced Raman spectroscopy (SERS effect), which enhances their Raman signal by several orders of magnitude. After eight days in culture, I. guayusa and P. marginatum extracts at different concentrations (10, 50, and 100 ppm) were added. Raman measurements were performed at 0, 12, and 24 h following extract application. Fluorescence microscopy was conducted using an OLIMPUS fv1000 microscope, a live-dead assay was performed using a kit employing a BD FACS Canto TM II flow cytometer, and data analysis was determined using a FlowJo program.

RESULTS

The Raman spectroscopy results showed spectra consistent with viable cells. These findings were corroborated using fluorescence microscopy and flow cytometry techniques, confirming high cellular viability.

CONCLUSIONS

The analyzed extracts exhibited low cytotoxicity, suggesting that they could be safely applied on enamel for remineralization purposes. The use of nanobiochips for SERS effect improved the cell viability assessment.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Fluorescein-stained confocal laser endomicroscopy versus conventional frozen section for intraoperative histopathological assessment of intracranial tumors

BACKGROUND

The aim of this clinical trial was to compare Fluorescein-stained intraoperative confocal laser endomicroscopy (CLE) of intracranial lesions and evaluation by a neuropathologist with routine intraoperative frozen section (FS) assessment by neuropathology.

METHODS

In this phase II noninferiority, prospective, multicenter, nonrandomized, off-label clinical trial (EudraCT: 2019-004512-58), patients above the age of 18 years with any intracranial lesion scheduled for elective resection were included. The diagnostic accuracies of both CLE and FS referenced with the final histopathological diagnosis were statistically compared in a noninferiority analysis, representing the primary endpoint. Secondary endpoints included the safety of the technique and time expedited for CLE and FS.

RESULTS

A total of 210 patients were included by 3 participating sites between November 2020 and June 2022. Most common entities were high-grade gliomas (37.9%), metastases (24.1%), and meningiomas (22.7%). A total of 6 serious adverse events in 4 (2%) patients were recorded. For the primary endpoint, the diagnostic accuracy for CLE was inferior with 0.87 versus 0.91 for FS, resulting in a difference of 0.04 (95% confidence interval -0.10; 0.02; P = .367). The median time expedited until intraoperative diagnosis was 3 minutes for CLE and 27 minutes for FS, with a mean difference of 27.5 minutes (standard deviation 14.5; P < .001).

CONCLUSIONS

CLE allowed for a safe and time-effective intraoperative histological diagnosis with a diagnostic accuracy of 87% across all intracranial entities included. The technique achieved histological assessments in real time with a 10-fold reduction of processing time compared to FS, which may invariably impact surgical strategy on the fly.

Rapid and non-invasive detection of cystic echinococcosis in sheep based on serum fluorescence spectrum combined with machine learning algorithms

Cystic echinococcosis (CE) is a grievous zoonotic parasitic disease. Currently, the traditional technology of screening CE is laborious and expensive, developing an innovative technology is urgent. In this study, we combined serum fluorescence spectroscopy with machine learning algorithms to develop an innovative screening technique to diagnose CE in sheep. Serum fluorescence spectra of Echinococcus granulosus sensu stricto-infected group (n = 63) and uninfected E. granulosus s.s. group (n = 60) under excitation at 405 nm were recorded. The linear support vector machine (Linear SVM), Quadratic SVM, medium radial basis function (RBF) SVM, K-nearest neighbor (KNN), and principal component analysis-linear discriminant analysis (PCA-LDA) were used to analyze the spectra data. The results showed that Quadratic SVM had the great classification capacity, its sensitivity, specificity, and accuracy were 85.0%, 93.8%, and 88.9%, respectively. In short, serum fluorescence spectroscopy combined with Quadratic SVM algorithm has great potential in the innovative diagnosis of CE in sheep.

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy

In the fight against oral cancer, innovative methods like Raman spectroscopy and deep learning have become powerful tools, particularly in integral tasks encompassing tumor staging, lymph node staging, and histological grading. These aspects are essential for the development of effective treatment strategies and prognostic assessment. However, it is important to note that most research so far has focused on solutions to one of these problems and has not taken full advantage of the potential wealth of information in the data. To compensate for this shortfall, we conceived a method that combines Raman spectroscopy with deep learning for simultaneous processing of multiple classification tasks, including tumor staging, lymph node staging, and histological grading. To achieve this innovative approach, we collected 1750 Raman spectra from 70 tissue samples, including normal and cancerous tissue samples from 35 patients with oral cancer. In addition, we used a deep neural network architecture to design four distinct multi-task network (MTN) models for intelligent oral cancer diagnosis, named MTN-Alexnet, MTN-Googlenet, MTN-Resnet50, and MTN-Transformer. To determine their effectiveness, we compared these multitask models to each other and to single-task models and traditional machine learning methods. The preliminary experimental results show that our multi-task network model has good performance, among which MTN-Transformer performs best. Specifically, MTN-Transformer has an accuracy of 81.5%, a precision of 82.1%, a sensitivity of 80.2%, and an F1_score of 81.1% in terms of tumor staging. In the field of lymph node staging, the accuracy, precision, sensitivity, and F1_score of MTN-Transformer are 81.3%, 83.0%, 80.1%, and 81.5% respectively. Similarly, for the histological grading classification tasks, the accuracy was 83.0%, the precision 84.3%, the sensitivity 76.7%, and the F1_score 80.2%. This code is available at https://github.com/ISCLab-Bistu/MultiTask-OralRamanSystem.

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

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

Microfluidics-Based Nanobiosensors for Healthcare Monitoring

Efficient healthcare management demands prompt decision-making based on fast diagnostics tools, astute data analysis, and informatics analysis. The rapid detection of analytes at the point of care is ensured using microfluidics in synergy with nanotechnology and biotechnology. The nanobiosensors use nanotechnology for testing, rapid disease diagnosis, monitoring, and management. In essence, nanobiosensors detect biomolecules through bioreceptors by modulating the physicochemical signals generating an optical and electrical signal as an outcome of the binding of a biomolecule with the help of a transducer. The nanobiosensors are sensitive and selective and play a significant role in the early identification of diseases. This article reviews the detection method used with the microfluidics platform for nanobiosensors and illustrates the benefits of combining microfluidics and nanobiosensing techniques by various examples. The fundamental aspects, and their application are discussed to illustrate the advancement in the development of microfluidics-based nanobiosensors and the current trends of these nano-sized sensors for point-of-care diagnosis of various diseases and their function in healthcare monitoring.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

Lipid metabolism analysis in esophageal cancer and associated drug discovery

Esophageal cancer is an upper gastrointestinal malignancy with a bleak prognosis. It is still being explored in depth due to its complex molecular mechanisms of occurrence and development. Lipids play a crucial role in cells by participating in energy supply, biofilm formation, and signal transduction processes, and lipid metabolic reprogramming also constitutes a significant characteristic of malignant tumors. More and more studies have found esophageal cancer has obvious lipid metabolism abnormalities throughout its beginning, progress, and treatment resistance. The inhibition of tumor growth and the enhancement of antitumor therapy efficacy can be achieved through the regulation of lipid metabolism. Therefore, we reviewed and analyzed the research results and latest findings for lipid metabolism and associated analysis techniques in esophageal cancer, and comprehensively proved the value of lipid metabolic reprogramming in the evolution and treatment resistance of esophageal cancer, as well as its significance in exploring potential therapeutic targets and biomarkers.

Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods

Pathogenic microbes contribute to several major global diseases that kill millions of people every year. Bloodstream infections caused by these microbes are associated with high morbidity and mortality rates, which are among the most common causes of hospitalizations. The search for the "Holy Grail" in clinical diagnostic microbiology, a reliable, accurate, low cost, real-time, and easy-to-use diagnostic method, is one of the essential issues in clinical practice. These very critical conditions can be met by Raman tweezers in combination with advanced analysis methods. Here, we present a proof-of-concept study based on Raman tweezers combined with spectral mixture analysis that allows for the identification of microbial strains directly from human blood serum without user intervention, thus eliminating the influence of a data analyst.

Use of high frequency electrorotation to identify cytoplasmic changes in cells non-disruptively

In this paper we demonstrate how the use of frequencies ranging from 50 kHz to 5 GHz in the analysis of cells by electrorotation can open the path to the identification of differences not detectable by conventional set-ups. Earlier works usually reported electrorotation devices operating below 20 MHz, limiting the response obtained to properties associated with the cell membrane. Those devices are thus unable to resolve the physiological properties in the cytoplasm. We used microwave-based technology to extend the frequency operation to 5 GHz. At high frequencies (from tens of MHz to GHz), the electromagnetic signal passes through the membrane and allows probing the cytoplasm. This enables several applications, such as cell classification, and viability analysis. Additionally, the use of conventional microfabrication techniques reduces the cost and complexity of analysis, compared to other non-invasive methods. We demonstrated the potential of this set-up by identifying two different populations of T-lymphocytes not distinguishable through visual assessment. We also assessed the effect of calcein on cell cytoplasmic properties and used it as a controlled experiment to demonstrate the possibility of this method to detect changes happening predominantly in the cytoplasm.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

Innovative Imaging Techniques: A Conceptual Exploration of Multi-Modal Raman Light Sheet Microscopy

Advances in imaging of microscopic structures are supported and complemented by adaptive visualization tools. These tools enable researchers to precisely capture and analyze complex three-dimensional structures of different kinds such as crystals, microchannels and electronic or biological material. In this contribution, we focus on 3D cell cultures. The new possibilities can play a particularly important role in biomedical research, especially here in the study of 3D cell cultures such as spheroids in the field of histology. By applying advanced imaging techniques, detailed information about the spatial arrangement and interactions between cells can be obtained. These insights help to gain a better understanding of cellular organization and function and have potential implications for the development of new therapies and drugs. In this context, this study presents a multi-modal light sheet microscope designed for the detection of elastic and inelastic light scattering, particularly Rayleigh scattering as well as the Stokes Raman effect and fluorescence for imaging purposes. By combining multiple modalities and stitching their individual results, three-dimensional objects are created combining complementary information for greater insight into spatial and molecular information. The individual components of the microscope are specifically selected to this end. Both Rayleigh and Stokes Raman scattering are inherent molecule properties and accordingly facilitate marker-free imaging. Consequently, altering influences on the sample by external factors are minimized. Furthermore, this article will give an outlook on possible future applications of the prototype microscope.

Molecular imaging of bacterial biofilms-a systematic review

The formation of bacterial biofilms in the human body and on medical devices is a serious human health concern. Infections related to bacterial biofilms are often chronic and difficult to treat. Detailed information on biofilm formation and composition over time is essential for a fundamental understanding of the underlying mechanisms of biofilm formation and its response to anti-biofilm therapy. However, information on the chemical composition, structural components of biofilms, and molecular interactions regarding metabolism- and communication pathways within the biofilm, such as uptake of administered drugs or inter-bacteria communication, remains elusive. Imaging these molecules and their distribution in the biofilm increases insight into biofilm development, growth, and response to environmental factors or drugs. This systematic review provides an overview of molecular imaging techniques used for bacterial biofilm imaging. The techniques included mass spectrometry-based techniques, fluorescence-labelling techniques, spectroscopic techniques, nuclear magnetic resonance spectroscopy (NMR), micro-computed tomography (µCT), and several multimodal approaches. Many molecules were imaged, such as proteins, lipids, metabolites, and quorum-sensing (QS) molecules, which are crucial in intercellular communication pathways. Advantages and disadvantages of each technique, including multimodal approaches, to study molecular processes in bacterial biofilms are discussed, and recommendations on which technique best suits specific research aims are provided.

Metabolomics in Acute Kidney Injury: The Clinical Perspective

BACKGROUND

Acute kidney injury (AKI) affects increasing numbers of hospitalized patients worldwide. The diagnosis of AKI is made too late in most individuals since it is still based on dynamic changes in serum creatinine. In recent years, new AKI biomarkers have been identified; however, none of these can reliably replace serum creatinine yet. Metabolomic profiling (metabolomics) allows the concomitant detection and quantification of large numbers of metabolites from biological specimens. The current article aims to summarize clinical studies on metabolomics in AKI diagnosis and risk prediction.

METHODS

The following databases were searched for references: PubMed, Web of Science, Cochrane Library, and Scopus, and the period lasted from 1940 until 2022. The following terms were utilized: 'AKI' OR 'Acute Kidney Injury' OR 'Acute Renal Failure' AND 'metabolomics' OR 'metabolic profiling' OR 'omics' AND 'risk' OR 'death' OR 'survival' OR 'dialysis' OR 'KRT' OR 'kidney replacement therapy' OR 'RRT' OR 'renal replacement therapy' OR 'recovery of kidney function' OR 'renal recovery' OR 'kidney recovery' OR 'outcome'. Studies on AKI risk prediction were only selected if metabolomic profiling allowed differentiation between subjects that fulfilled a risk category (death or KRT or recovery of kidney function) and those who did not. Experimental (animal-based) studies were not included.

RESULTS

In total, eight studies were identified. Six studies were related to the diagnosis of AKI; two studies were performed on metabolic analysis in AKI risk (death) prediction. Metabolomics studies in AKI already helped to identify new biomarkers for AKI diagnosis. The data on metabolomics for AKI risk prediction (death, KRT, recovery of kidney function), however, are very limited.

CONCLUSIONS

Both the heterogenous etiology and the high degree of pathogenetic complexity of AKI most likely require integrated approaches such as metabolomics and/or additional types of '-omics' studies to improve clinical outcomes in AKI.

Analytical Techniques for Single-Cell Biochemical Assays of Lipids

Lipids are essential cellular components forming membranes, serving as energy reserves, and acting as chemical messengers. Dysfunction in lipid metabolism and signaling is associated with a wide range of diseases including cancer and autoimmunity. Heterogeneity in cell behavior including lipid signaling is increasingly recognized as a driver of disease and drug resistance. This diversity in cellular responses as well as the roles of lipids in health and disease drive the need to quantify lipids within single cells. Single-cell lipid assays are challenging due to the small size of cells (∼1 pL) and the large numbers of lipid species present at concentrations spanning orders of magnitude. A growing number of methodologies enable assay of large numbers of lipid analytes, perform high-resolution spatial measurements, or permit highly sensitive lipid assays in single cells. Covered in this review are mass spectrometry, Raman imaging, and fluorescence-based assays including microscopy and microseparations.

Metabolomics in Acute Kidney Injury: The Experimental Perspective

Acute kidney injury (AKI) affects increasing numbers of in-hospital patients in Central Europe and the USA, the prognosis remains poor. Although substantial progress has been achieved in the identification of molecular/cellular processes that induce and perpetuate AKI, more integrated pathophysiological perspectives are missing. Metabolomics enables the identification of low-molecular-weight (< 1.5 kD) substances from biological specimens such as certain types of fluid or tissue. The aim of the article was to review the literature on metabolic profiling in experimental AKI and to answer the question if metabolomics allows the integration of distinct pathophysiological events such as tubulopathy and microvasculopathy in ischemic and toxic AKI. The following databases were searched for references: PubMed, Web of Science, Cochrane Library, Scopus. The period lasted from 1940 until 2022. The following terms were utilized: "acute kidney injury" OR "acute renal failure" OR "AKI" AND "metabolomics" OR "metabolic profiling" OR "omics" AND "ischemic" OR "toxic" OR "drug-induced" OR "sepsis" OR "LPS" OR "cisplatin" OR "cardiorenal" OR "CRS" AND "mouse" OR "mice" OR "murine" OR "rats" OR "rat". Additional search terms were "cardiac surgery", "cardiopulmonary bypass", "pig", "dog", and "swine". In total, 13 studies were identified. Five studies were related to ischemic, seven studies to toxic (lipopolysaccharide (LPS), cisplatin), and one study to heat shock-associated AKI. Only one study, related to cisplatin-induced AKI, was performed as a targeted analysis. The majority of the studies identified multiple metabolic deteriorations upon ischemia/the administration of LPS or cisplatin (e.g., amino acid, glucose, lipid metabolism). Particularly, abnormalities in the lipid homeostasis were shown under almost all experimental conditions. LPS-induced AKI most likely depends on the alterations in the tryptophan metabolism. Metabolomics studies provide a deeper understanding of pathophysiological links between distinct processes that are responsible for functional impairment/structural damage in ischemic or toxic or other types of AKI.

Comprehensive Approach to Assessment of Liver Viability During Normothermic Machine Perfusion

Liver transplantation is the most effective treatment of advanced liver disease, and the use of extended criteria donor organs has broadened the source of available livers. Although normothermic machine perfusion (NMP) has become a useful tool in liver transplantation, there are no consistent criteria that can be used to evaluate the viability of livers during NMP. This review summarizes the criteria, indicators, and methods used to evaluate liver viability during NMP. The shape, appearance, and hemodynamics of the liver can be analyzed at a macroscopic level, while markers of liver injury, indicators of liver and bile duct function, and other relevant indicators can be evaluated by biochemical analysis. The liver can also be assessed by tissue biopsy at the microscopic level. Novel methods for assessment of liver viability are introduced. The limitations of evaluating liver viability during NMP are discussed and suggestions for future clinical practice are provided.

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to predict the delay to sample examination. We achieved 90% accuracy for binary classification of the serum samples in the groups "without delay" versus "delayed". Spectra recorded on the first day could be distinguished clearly from all subsequent measurements. Distinguishing between spectra taken in the range from the second to the last day seems to be possible as well, but currently, with an accuracy of approximately 70% only. Importantly, filtering out the fluorescent background significantly reduces the precision of detection.

Recent Advances in Biomolecular Detection Based on Aptamers and Nanoparticles

The fast, accurate detection of biomolecules, ranging from nucleic acids and small molecules to proteins and cellular secretions, plays an essential role in various biomedical applications. These include disease diagnostics and prognostics, environmental monitoring, public health, and food safety. Aptamer recognition (DNA or RNA) has gained extensive attention for biomolecular detection due to its high selectivity, affinity, reproducibility, and robustness. Concurrently, biosensing with nanoparticles has been widely used for its high carrier capacity, stability and feasibility of incorporating optical and catalytic activity, and enhanced diffusivity. Biosensors based on aptamers and nanoparticles utilize the combination of their advantages and have become a promising technology for detecting of a wide variety of biomolecules with high sensitivity, reliability, specificity, and detection speed. Via various sensing mechanisms, target biomolecules have been quantified in terms of optical (e.g., colorimetric and fluorometric), magnetic, and electrical signals. In this review, we summarize the recent advances in and compare different aptamer-nanoparticle-based biosensors by nanoparticle types and detection mechanisms. We also share our views on the highlights and challenges of the different nanoparticle-aptamer-based biosensors.

Automatic classification of Candida species using Raman spectroscopy and machine learning

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.

Point-of-care community drug checking technologies: an insider look at the scientific principles and practical considerations

Drug checking is increasingly being explored outside of festivals and events to be an ongoing service within communities, frequently integrated within responses to illicit drug overdose. The choice of instrumentation is a common question, and the demands on these chemical analytical instruments can be challenging as illicit substances may be more complex and include highly potent ingredients at trace levels. The answer remains nuanced as the instruments themselves are not directly comparable nor are the local demands on the service, meaning implementation factors heavily influence the assessment and effectiveness of instruments. In this perspective, we provide a technical but accessible introduction to the background of a few common drug checking methods aimed at current and potential drug checking service providers. We discuss the following tools that have been used as part of the Vancouver Island Drug Checking Project in Victoria, Canada: immunoassay test strips, attenuated total reflection IR-absorption spectroscopy, Raman spectroscopy from powder samples, surface-enhanced Raman scattering in a solution of colloidal gold nanoparticles, and gas chromatography-mass spectrometry. Using four different drug mixtures received and tested at the service, we illustrate the strengths, limitations, and capabilities of such instruments, and expose the scientific theory to give further insight into their analytical results. Each case study provides a walk-through-style analysis for a practical comparison between data from several different instruments acquired on the same sample. Ideally, a single instrument would be able to achieve all of the objectives of drug checking. However, there is no clear instrument that ticks every box; low cost, portable, rapid, easy-to-use and provides highly sensitive identification and accurate quantification. Multi-instrument approaches to drug checking may be required to effectively respond to increasingly complex and highly potent substances demanding trace level detection and the potential for quantification.

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.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

Reflective FT-NIR and SERS studies of HER-II breast cancer biomarker using plasmonic-active nanostructured thin film immobilized oriented antibody

We describe the fabrication of plasmonic-active nanostructured thin film substrate as a label-free surface-enhanced Raman scattering (SERS)-based biosensor immobilized covalently with monoclonal HER-II antibody (mAb) to detect overexpressed HER-II as a biomarker in breast cancer serum (BCS). Oriented conjugation of mAb via hydrazone linkage to provide higher mAb accessibility was characterized by UV-vis and reflective Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The interaction of BCS with mAb was studied by FT-NIR and nonresonant SERS at 637 nm. The results showed detection of glycoprotein content at different laser powers including a rise in amino acid and glycan content with varying results at higher power. With nonresonant SERS we observed nonlinear behavior of peak intensity. Analysis of variance was implemented to determine the effect of laser power which was found not to be a contributing factor. However, at the nanoscale, factors including the heating effect and aggregation of molecules can contribute to the nonlinearity of peak intensity.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

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

Cancer metabolic features allow discrimination of tumor from white blood cells by label-free multimodal optical imaging

Circulating tumor cells (CTCs) are tumor cells that have penetrated the circulatory system preserving tumor properties and heterogeneity. Detection and characterization of CTCs has high potential clinical values and many technologies have been developed for CTC identification. These approaches remain challenged by the extraordinary rarity of CTCs and the difficulty of efficiently distinguishing cancer from the much larger number of white blood cells in the bloodstream. Consequently, there is still a need for efficient and rapid methods to capture the broad spectrum of tumor cells circulating in the blood. Herein, we exploit the peculiarities of cancer metabolism for discriminating cancer from WBCs. Using deuterated glucose and Raman microscopy we show that a) the known ability of cancer cells to take up glucose at greatly increased rates compared to non-cancer cells results in the lipid generation and accumulation into lipid droplets and, b) by contrast, leukocytes do not appear to generate visible LDs. The difference in LD abundance is such that it provides a reliable parameter for distinguishing cancer from blood cells. For LD sensitive detections in a cell at rates suitable for screening purposes, we test a polarization-sensitive digital holographic imaging (PSDHI) technique that detects the birefringent properties of the LDs. By using polarization-sensitive digital holographic imaging, cancer cells (prostate cancer, PC3 and hepatocarcinoma cells, HepG2) can be rapidly discriminated from leukocytes with reliability close to 100%. The combined Raman and PSDHI microscopy platform lays the foundations for the future development of a new label-free, simple and universally applicable cancer cells' isolation method.

Label-Free Characterization of Macrophage Polarization Using Raman Spectroscopy

Macrophages are important cells of the innate immune system that play many different roles in host defense, a fact that is reflected by their polarization into many distinct subtypes. Depending on their function and phenotype, macrophages can be grossly classified into classically activated macrophages (pro-inflammatory M1 cells), alternatively activated macrophages (anti-inflammatory M2 cells), and non-activated cells (resting M0 cells). A fast, label-free and non-destructive characterization of macrophage phenotypes could be of importance for studying the contribution of the various subtypes to numerous pathologies. In this work, single cell Raman spectroscopic imaging was applied to visualize the characteristic phenotype as well as to discriminate between different human macrophage phenotypes without any label and in a non-destructive manner. Macrophages were derived by differentiation of peripheral blood monocytes of human healthy donors and differently treated to yield M0, M1 and M2 phenotypes, as confirmed by marker analysis using flow cytometry and fluorescence imaging. Raman images of chemically fixed cells of those three macrophage phenotypes were processed using chemometric methods of unmixing (N-FINDR) and discrimination (PCA-LDA). The discrimination models were validated using leave-one donor-out cross-validation. The results show that Raman imaging is able to discriminate between pro- and anti-inflammatory macrophage phenotypes with high accuracy in a non-invasive, non-destructive and label-free manner. The spectral differences observed can be explained by the biochemical characteristics of the different phenotypes.

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.

Extracellular Vesicles in Veterinary Medicine

Extracellular vesicles (EVs) are cell-derived membrane-bound vesicles involved in many physiological and pathological processes not only in humans but also in all the organisms of the eukaryotic and prokaryotic kingdoms. EV shedding constitutes a fundamental universal mechanism of intra-kingdom and inter-kingdom intercellular communication. A tremendous increase of interest in EVs has therefore grown in the last decades, mainly in humans, but progressively also in animals, parasites, and bacteria. With the present review, we aim to summarize the current status of the EV research on domestic and wild animals, analyzing the content of scientific literature, including approximately 220 papers published between 1984 and 2021. Critical aspects evidenced through the veterinarian EV literature are discussed. Then, specific subsections describe details regarding EVs in physiology and pathophysiology, as biomarkers, and in therapy and vaccines. Further, the wide area of research related to animal milk-derived EVs is also presented in brief. The numerous studies on EVs related to parasites and parasitic diseases are excluded, deserving further specific attention. The literature shows that EVs are becoming increasingly addressed in veterinary studies and standardization in protocols and procedures is mandatory, as in human research, to maximize the knowledge and the possibility to exploit these naturally produced nanoparticles.

Three-dimensional imaging of biological cells using surface plasmon coupled emission

SIGNIFICANCE

Biological cell imaging has become one of the most crucial research interests because of its applications in biomedical and microbiology studies. However, three-dimensional (3D) imaging of biological cells is critically challenging and often involves prohibitively expensive and complex equipment. Therefore, a low-cost imaging technique with a simpler optical arrangement is immensely needed.

AIM

The proposed approach will provide an accurate cell image at a low cost without needing any microscope or extensive processing of the collected data, often used in conventional imaging techniques.

APPROACH

We propose that patterns of surface plasmon coupled emission (SPCE) features from a fluorescently labeled biological cell can be used to image the cell. An imaging methodology has been developed and theoretically demonstrated to create 3D images of cells from the detected SPCE patterns. The 3D images created from the different SPCE properties at the far-field closely match the actual cell structures.

RESULTS

The developed technique has been applied to different regular and irregular cell shapes. In each case, the calculated root-mean-square error (RMSE) of the created images from the cell structures remains within a few percentages. Our work recreates the base of a circular-shaped cell with an RMSE of ≲1.4 % . In addition, the images of irregular-shaped cell bases have an RMSE of ≲2.8 % . Finally, we obtained a 3D image with an RMSE of ≲6.5 % for a random cellular structure.

CONCLUSIONS

Despite being in its initial stage of development, the proposed technique shows promising results considering its simplicity and the nominal cost it would require.

Label-free laser spectroscopy for respiratory virus detection: A review

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning

The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR-MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data.

Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM)

Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F₂-score (F₂), Matthews correlation coefficient (MCC) and test effectiveness (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta$$\end{document}δ). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F₂; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F₂. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.

Label-Free Sensing with Metal Nanostructure-Based Surface-Enhanced Raman Spectroscopy for Cancer Diagnosis

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful analytical technique for the detection of small analytes with great potential for medical diagnostic applications. Its high sensitivity and excellent molecular specificity, which stems from the unique fingerprint of molecular species, have been applied toward the detection of different types of cancer. The noninvasive and rapid detection offered by SERS highlights its applicability for point-of-care (PoC) deployment for cancer diagnosis, screening, and staging, as well as for predicting tumor recurrence and treatment monitoring. This review provides an overview of the progress in label-free (direct) SERS-based chemical detection for cancer diagnosis with the main focus on the advances in the design and preparation of SERS substrates on the basis of metal nanoparticle structures formed via bottom-up strategies. It begins by introducing a synopsis of the working principles of SERS, including key chemometric approaches for spectroscopic data analysis. Then it introduces the advances of label-free sensing with SERS in cancer diagnosis using biofluids (blood, urine, saliva, sweat) and breath as the detection media. In the end, an outlook of the advances and challenges in cancer diagnosis via SERS is provided.

New Conceptional Study of a Portable Highly Sensitive Photometric Raman Sensor

Quality control and reaction monitoring are necessary to ensure the consistency of any kind of mixing or reaction process. In this context, a novel portable high-sensitivity sensor prototype based on the Raman effect is presented in this study. The elongated probe head is designed for (but not limited to) monitoring high temperature batch processes for quality assurance. Thanks to the highly sensitive special detectors, concentration differences of up to 50 mmol/L can currently be detected, which facilitates compliance with high product quality standards. In addition, seamless reaction tracking is possible. Small individual adjustments through simple, intuitive mechanical components provide a high degree of flexibility in reaction selection by the end user. Specially developed software automates the evaluation process and gives the user visual signals about the current status and progress of the batch as well as an emergency stop if temperature limits could damage individual components. By using all the individual components developed, the problem of the limited integration times of previous spectrometric measuring instruments could be reduced. The prototype was validated using concentration measurements of various substances that occur as standard in batch processes. In addition, this article provides an outlook on the fact that Raman measurements can also be carried out successfully and reliably in explosive environments in the future with the prototype presented.

Raman and fluorescence micro-spectroscopy applied for the monitoring of sunitinib-loaded porous silicon nanocontainers in cardiac cells

Nanomaterials are a central pillar in modern medicine. They are thought to optimize drug delivery, enhance therapeutic efficacy, and reduce side-effects. To foster this technology, analytical methods are needed to validate not only the localization and distribution of these nanomaterials, but also their compatibility with cells, drugs, and drug release. In the present work, we assessed nanoparticles based on porous silicon (pSiNPs) loaded with the clinically used tyrosine kinase inhibitor sunitinib for their effectiveness of drug delivery, release, and toxicity in colon cancer cells (HCT 116 cells) and cardiac myoblast cells (H9c2) using Raman micro-spectroscopy, high-resolution fluorescence microscopy, along with biological methods for toxicological effects. We produced pSiNPs with a size of about 100 nm by grinding mesoporous silicon layers. pSiNPs allowed an effective loading of sunitinib due to their high porosity. Photoluminescence properties of the nanoparticles within the visible spectrum allowed the visualization of their uptake in cardiac cells. Raman micro-spectroscopy allowed not only the detection of the uptake and distribution of pSiNPs within the cells via a characteristic silicon Raman band at about 518–520 cm⁻¹, but also the localization of the drug based on its characteristic molecular fingerprints. Cytotoxicity studies by Western blot analyses of apoptotic marker proteins such as caspase-3, and the detection of apoptosis by subG1-positive cell fractions in HCT 116 and MTT analyses in H9c2 cells, suggest a sustained release of sunitinib from pSiNPs and delayed cytotoxicity of sunitinib in HCT 116 cells. The analyses in cardiac cells revealed that pSiNPs are well tolerated and that they may even protect from toxic effects in these cells to some extent. Analyses of the integrity of mitochondrial networks as an early indicator for apoptotic cellular effects seem to validate these observations. Our study suggests pSiNPs-based nanocontainers for efficient and safe drug delivery and Raman micro-spectroscopy as a reliable method for their detection and monitoring. Thus, the herein presented nanocontainers and analytical methods have the potential to allow an efficient advancement of nanoparticles for targeted and sustained intracellular drug release that is of need, e.g., in chronic diseases and for the prevention of cardiac toxicity.

Miniaturized Raman Instruments for SERS-Based Point-of-Care Testing on Respiratory Viruses

As surface-enhanced Raman scattering (SERS) has been used to diagnose several respiratory viruses (e.g., influenza A virus subtypes such as H1N1 and the new coronavirus SARS-CoV-2), SERS is gaining popularity as a method for diagnosing viruses at the point-of-care. Although the prior and quick diagnosis of respiratory viruses is critical in the outbreak of infectious disease, ELISA, PCR, and RT-PCR have been used to detect respiratory viruses for pandemic control that are limited for point-of-care testing. SERS provides quantitative data with high specificity and sensitivity in a real-time, label-free, and multiplex manner recognizing molecular fingerprints. Recently, the design of Raman spectroscopy system was simplified from a complicated design to a small and easily accessible form that enables point-of-care testing. We review the optical design (e.g., laser wavelength/power and detectors) of commercialized and customized handheld Raman instruments. As respiratory viruses have prominent risk on the pandemic, we review the applications of handheld Raman devices for detecting respiratory viruses. By instrumentation and commercialization advancements, the advent of the portable SERS device creates a fast, accurate, practical, and cost-effective analytical method for virus detection, and would continue to attract more attention in point-of-care testing.

Classification of tissue biopsies by Raman spectroscopy guided by quantitative phase imaging and its application to bladder cancer

We present a multimodal label-free optical measurement approach for analyzing sliced tissue biopsies by a unique combination of quantitative phase imaging and localized Raman spectroscopy. First, label-free quantitative phase imaging of the entire unstained tissue slice is performed using automated scanning. Then, pixel-wise segmentation of the tissue layers is performed by a kernelled structural support vector machine based on Haralick texture features, which are extracted from the quantitative phase profile, and used to find the best locations for performing the label-free localized Raman measurements. We use this multimodal label-free measurement approach for segmenting the urothelium in benign and malignant bladder cancer tissues by quantitative phase imaging, followed by location-guided Raman spectroscopy measurements. We then use sparse multinomial logistic regression (SMLR) on the Raman spectroscopy measurements to classify the tissue types, demonstrating that the prior segmentation of the urothelium done by label-free quantitative phase imaging improves the Raman spectra classification accuracy from 85.7% to 94.7%.

Long-Life and pH-Stable SnO2-Coated Au Nanoparticles for SHINERS

Shell-isolated nanoparticles (SHINs) with a 37 nm gold core and an 11 nm tin dioxide (SnO₂) coating exhibited long-life Raman enhancement for 3 months and a wide pH stability of pH 2-13 in comparison with conventional SiO₂-coated SHINs. Herein, Au-SnO₂ is demonstrated as a more durable SHIN for use in the technique Shell-Isolated Nanoparticles for Enhanced Raman Spectroscopy (SHINERS).

Future of Male Infertility Evaluation and Treatment: Brief Review of Emerging Technology

Over the past few decades, there have been significant advances in male infertility, particularly in the development of novel diagnostic tools. Unfortunately, there remains a substantial number of patients that remain infertile despite these improvements. In this review, we take heed of the emerging technologies that will shape the future of male infertility diagnosis, evaluation and treatment. Improvement in computer-assisted semen analyses and portability allow males to obtain basic semen parameters from the comfort of their home. Additionally, breakthrough ultrasound technology allows for preoperative prediction of potential areas of spermatogenesis within the testes, high-resolution optics permits better visualization during microdissection testicular sperm extraction (mTESE), and artificial intelligence improves sperm selection and identification.

Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example

Surface-enhanced Raman spectroscopy (SERS) is an emerging technology that combines Raman spectroscopy and nanotechnology with great potential. This technology can accurately characterize molecular adsorption behavior and molecular structure. Moreover, it can provide rapid and sensitive detection of molecules and trace substances. In practical application, SERS has the advantages of portability, no need for sample pretreatment, rapid analysis, high sensitivity, and ‘fingerprint’ recognition. Thus, it has great potential in food safety detection. Alcoholic beverages have a long history of production in the world. Currently, a variety of popular products have been developed. With the continuous development of the alcoholic beverage industry, simple, on-site, and sensitive detection methods are necessary. In this paper, the basic principle, development history, and research progress of SERS are summarized. In view of the chemical composition, the beneficial and toxic components of alcoholic beverages and the practical application of SERS in alcoholic beverage analysis are reviewed. The feasibility and future development of SERS are also summarized and prospected. This review provides data and reference for the future development of SERS technology and its application in food analysis.

Micro-Raman spectroscopy study of blood samples from myocardial infarction patients

Acute myocardial infarction (MI) is found to be a major causative factor for global mortality and morbidity. This situation demands necessity of developing efficient and rapid diagnostic tools to detect acute MI. Raman spectroscopy is a non-destructive optical diagnostic technique, which has high potential in probing biochemical changes in clinical samples during initiation and progress of diseases. In this work, blood was taken as the sample to examine inflammation in acute MI patients using Raman spectroscopy. Ratio of Raman peak intensities that corresponds to phenylalanine (1000 cm^-1) and tyrosine (825 cm^-1) can facilitate indirect information about tetrahydrobiopterin (BH4) availability, which can indicate inflammatory status in patients. This ratio obtained was higher for MI patients in comparison with control subjects. The decrease in phenylalanine and tyrosine ratio (Phe-Tyr ratio) is attributed to the prognosis of standard of care (medications like antiplatelets including aspirin, statin and revascularisation) leading to inflammation reduction. Phe-Tyr ratio estimated from the Raman spectra of blood can be exploited as a reliable method to probe inflammation due to MI. The method is highly objective, require only microliters of sample and minimal sample preparation, signifying its clinical utility.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Ex vivo fluorescence confocal microscopy: chances and changes in the analysis of breast tissue

Background

Rapid histologic diagnosis of frozen sections is essential for a variety of surgical procedures. Frozen sections however, require specialized lab equipment, are prone to freezing artifacts and are not applicable to all types of tissue. Adipose tissue is especially difficult to process in frozen sections. Although these limitations are well known, no alternative method for microscopic tissue analysis that might replace frozen sections could be established. Our objective was to evaluate whether tissue imaging based on ex vivo fluorescent confocal microscopy (FCM) is applicable for rapid microscopic assessment of breast tumors specimens with abundant adipose tissue.

Methods

We evaluated 17 tissue samples from mastectomy specimens, rich in adipose tissue, submitted to the department of pathology at the Medical University of Vienna. We conducted our study on the FCM VivaScope® 2500M-G4 (Mavig GmbH, Munich, Germany; Caliber I.D.; Rochester NY, USA).

Results

When comparing FCM to frozen sections, we found a very similar overall processing time for FCM images and frozen sections respectively. Image quality was mostly superior to frozen sections (especially for adipose tissue and nuclear detail) but inferior to H&E stained FFPE sections. Limitations of the technology were uneven coloring, invisibility of ink applied for marking tissue margins and distortion artifacts if too much pressure is applied to the tissue.

Conclusion

FCM has the potential to expand the application and usefulness of rapid tissue analysis as speed is comparable and quality exceeds that of frozen sections especially in tissues rich in adipose cells such as breast specimen.

Hyaluronic acid detection and relative quantification by mass spectrometry imaging in human skin tissues

Hyaluronic acid (HA) is a major component of the skin, contributing to tissue hydration and biomechanical properties. As HA content in the skin decreases with age, formulas containing HA are widely used in cosmetics and HA injections in aesthetic procedures to reduce the signs of aging. To prove the beneficial effects of these treatments, efficient quantification of HA levels in the skin is necessary, but remains difficult. A new analytical method has been developed based on matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to quantify HA content in cross sections of human skin explants. A standardized and reproducible chemical entity (3 dimeric motifs or 6-mer) quantifiable by MALDI-MSI was produced by enzymatic hydrolysis using a specific hyaluronidase (H1136) in HA solution. This enzymatic digestion was carried out on skin sections before laser desorption, enabling the detection of HA. Histological coloration allowed us to localize the epidermis and the dermis on skin sections and, by comparison with the MALDI molecular image, to calculate the relative HA concentrations in these tissue areas. Skin explants were treated topically using a formula containing HA or its placebo, and the HA distribution profiles were compared with those obtained from untreated explants. A significant increase in HA was shown in each skin layer following topical application of the formula containing HA versus placebo and untreated samples (average of 126±40% and 92±40%, respectively). The MALDI-MSI technique enabled the quantification and localization of all HA macromolecules (endogenous and exogenous) on skin sections and could be useful for determining the efficacy of new cosmetic products designed to fight the signs of aging.