Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review

Gestational diabetes mellitus (GDM) is associated with adverse maternal and foetal consequences, along with the subsequent risk of type 2 diabetes mellitus (T2DM) and several other diseases. Due to early risk stratification in the prevention of progression of GDM, improvements in biomarker determination for GDM diagnosis will enhance the optimization of both maternal and foetal health. Spectroscopy techniques are being used in an increasing number of applications in medicine for investigating biochemical pathways and the identification of key biomarkers associated with the pathogenesis of GDM. The significance of spectroscopy promises the molecular information without the need for special stains and dyes; therefore, it speeds up and simplifies the necessary ex vivo and in vivo analysis for interventions in healthcare. All the selected studies showed that spectroscopy techniques were effective in the identification of biomarkers through specific biofluids. Existing GDM prediction and diagnosis through spectroscopy techniques presented invariable findings. Further studies are required in larger, ethnically diverse populations. This systematic review provides the up-to-date state of research on biomarkers in GDM, which were identified via various spectroscopy techniques, and a discussion of the clinical significance of these biomarkers in the prediction, diagnosis, and management of GDM.

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

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

Investigation on the Cancer Invasion and Metastasis of Skin Squamous Cell Carcinoma by Raman Spectroscopy

Raman spectroscopy facilitates accurate and minimally invasive investigation on biomedical samples to reveal their molecular-level biological information. In this work, the cancer field effects of squamous cell carcinoma (SCC) tissues were illustrated by Raman microspectroscopy. Referenced with hematoxylin and eosin (H&E) stained microscopic images, the biochemical variations during SCC progress were meticulously described by the Raman spectral features in different pathological areas of two lesion types, including the biochemical changes in collagen, lipids, DNA, and other components of SCC diffusion and metastasis. The experimental results demonstrated that the intensities of the Raman peaks representing collagen (853, 936, and 1248 cm⁻¹) were decreased, whereas the intensities of peaks corresponding to DNA (720, 1327 cm⁻¹) and lipids (1305 cm⁻¹) were increased significantly in cancerous lesions, which testified that SCC originates from the epidermis and invades the dermis gradually. The achieved results not only described the molecular mechanism of skin carcinogenesis, but also provided vital reference data for in vivo skin cancer diagnosis using Raman spectroscopy.

Accuracy of Raman spectroscopy for differentiating skin cancer from normal tissue

BACKGROUND

Raman spectroscopy could be applied to distinguish tumor from normal tissues. This meta-analysis assessed the accuracy of Raman spectroscopy in differentiating skin cancer from normal tissue.

METHODS

PubMed, Embase, Cochrane Library, and CNKI were searched to identify suitable studies before Februray 4th, 2018. We estimated the pooled sensitivity, specificity, positive, and negative likelihood ratios, diagnostic odds ratio, and constructed summary receiver-operating characteristics curves to identify the accuracy of Raman spectroscopy in differentiating skin cancer from normal tissue.

RESULTS

A total of 12 studies with 2461 spectra were included. For basal cell skin cancer (BCC) ex vivo detection, the pooled sensitivity and specificity were 0.99 (95% confidence interval [CI] 0.97-0.99) and 0.96 (95% CI 0.95-0.97), respectively. The area under the curve (AUC) was 0.9837. For BCC in vivo detection, the pooled sensitivity and specificity were 0.69 (95% CI 0.61-0.76) and 0.85 (95% CI 0.82-0.87), respectively. The AUC was 0.9213. For melanoma (MM) ex vivo detection, the pooled sensitivity and specificity were 1.00 (95% CI 0.91-1.00) and 0.98 (95% CI 0.95-1.00), respectively. The AUC was 0.9914. For MM in vivo detection, the sensitivity (0.93) and the specificity (0.96) balanced relatively well. For squamous cell skin cancer (SCC) ex vivo detection, the pooled sensitivity and specificity were 0.96 (95% CI 0.81-1.00) and 1.00 (95% CI 0.92-1.00), respectively. For SCC in vivo detection, the sensitivity was 0.81 (95% CI 0.70-0.90) and the specificity was 0.89 (95% CI 0.86-0.91).

CONCLUSION

This meta-analysis suggested that Raman spectroscopy could be an effective and accurate tool for differentiating BCC, MM, SCC from normal tissue, which would assist us in the diagnosis and treatment of skin cancer.

Active antioxidants in ex-vivo examination of burn wound healing by means of IR and Raman spectroscopies-Preliminary comparative research

Being a complex traumatic event, burn injury also affects other organ systems apart from the skin. Wounds undergo various pathological changes which are accompanied by alterations in the molecular environment. Information about molecules may be obtained with the use of Raman spectroscopy and Fourier-transform infrared spectroscopy, and when combined, both methods are a powerful tool for providing material characterization. Alterations in the molecular environment may lead to identifying objective markers of acute wound healing. In general, incubation of samples in solutions of l-ascorbic acid and 5% and 7% orthosilicic acid organizes the collagen structure, whereas the increased intensity of the Raman bands in the region of 1500-800cm^-1 reveals regeneration of the burn tissue. Since oxidative damage is one of the mechanisms responsible for local and distant pathophysiological events after burn, antioxidant therapy can prove to be beneficial in minimizing burn wounds, which was examined on the basis of human skin samples and chicken skin samples, the latter being subject to modification when heated to a temperature sufficient for the simulation of a burn incident.

Biomarkers for wound healing and their evaluation

A biological marker (biomarker) is a substance used as an indicator of biological state. Advances in genomics, proteomics and molecular pathology have generated many candidate biomarkers with potential clinical value. Research has identified several cellular events and mediators associated with wound healing that can serve as biomarkers. Macrophages, neutrophils, fibroblasts and platelets release cytokines molecules including TNF-α, interleukins (ILs) and growth factors, of which platelet-derived growth factor (PDGF) holds the greatest importance. As a result, various white cells and connective tissue cells release both matrix metalloproteinases (MMPs) and the tissue inhibitors of metalloproteinases (TIMPs). Studies have demonstrated that IL-1, IL-6, and MMPs, levels above normal, and an abnormally high MMP/TIMP ratio are often present in non-healing wounds. Clinical examination of wounds for these mediators could predict which wounds will heal and which will not, suggesting use of these chemicals as biomarkers of wound healing. There is also evidence that the application of growth factors like PDGF will alleviate the recuperating process of chronic, non-healing wounds. Finding a specific biomarker for wound healing status would be a breakthrough in this field and helping treat impaired wound healing.

Paraconsistent analysis network applied in the treatment of Raman spectroscopy data to support medical diagnosis of skin cancer

Paraconsistent logic (PL) is a type of non-classical logic that accepts contradiction as a fundamental concept and has produced valuable results in the analysis of uncertainties. In this work, algorithms based on a type of PL-paraconsistent annotated logic of two values (PAL2v)-are interconnected into a network of paraconsistent analysis (PANnet). PANnet was applied to a dataset comprising 146 Raman spectra of skin tissue biopsy fragments of which 30 spectra were determined to represent normal skin tissue (N), 96 were determined to represent tissue with basal cell carcinoma, and 19 were determined to be tissue with melanoma (MEL). In this database, paraconsistent analysis was able to correctly discriminate 136 out of a total of 145 fragments, obtaining a 93.793 % correct diagnostic accuracy. The application of PAL2v in the analysis of Raman spectroscopy signals produces better discrimination of cells than conventional statistical processes and presents a good graphical overview through its associated lattice structure. The technique of PAL2v-based data processing can be fundamental in the development of a computational tool dedicated to support the diagnosis of skin cancer using Raman spectroscopy.

Correlation between METAVIR scores and Raman spectroscopy in liver lesions induced by hepatitis C virus: a preliminary study

The viral hepatitis C is one of the most important causes of chronic hepatic illness worldwide, affecting around 3 % of the world population. Raman spectroscopy has been employed to distinguish normal from hepatic lesions through differences in the spectral features related to the METAVIR score system. This preliminary study evaluated 11 patients with diagnoses of chronic hepatitis C who underwent hepatic biopsies; the biopsies were submitted to near-infrared Raman spectroscopy using a dispersive spectrometer (830-nm wavelength, 300-mW laser power, and 20-s exposure time). The METAVIR was further scored, and the spectra were submitted to principal component analysis (PCA). The results show a good correlation between the Raman spectroscopy features and the stage of hepatic inflammation and fibrosis. PCA showed that samples with a higher degree of fibrosis presented a higher amount of protein features (collagen), whereas samples with a higher degree of inflammation presented higher features of hemoglobin, in accordance to the expected evolution of chronic hepatitis. Quinone was found to be an important biomarker in early hepatic lesions with a spectral feature at 1595 cm(-1). This study demonstrates that Raman spectroscopy may become an important tool for diagnosing liver disease.

Vibrational spectroscopy of biofluids for disease screening or diagnosis: translation from the laboratory to a clinical setting

There remains a need for objective and cost-effective approaches capable of diagnosing early-stage disease in point-of-care clinical settings. Given an increasingly ageing population resulting in a rising prevalence of chronic diseases, the need for screening to facilitate the personalising of therapies to prevent or slow down pathology development will increase. Such a tool needs to be robust but simple enough to be implemented into clinical practice. There is interest in extracting biomarkers from biofluids (e.g., plasma or serum); techniques based on vibrational spectroscopy provide an option. Sample preparation is minimal, techniques involved are relatively low-cost, and data frameworks are available. This review explores the evidence supporting the applicability of vibrational spectroscopy to generate spectral biomarkers of disease in biofluids. We extend the inter-disciplinary nature of this approach to hypothesise a microfluidic platform that could allow such measurements. With an appropriate lightsource, such engineering could revolutionize screening in the 21(st) century.

Biochemical and molecular aspects of spectral diagnosis in calcinosis cutis

Calcinosis cutis (CC) is a type of calcinosis wherein insoluble compounds or salts deposited on the skin. Clinical diagnosis of CC is usually achieved through time consuming histopathological or immunohistochemical procedures, but it can only be empirically identified by experienced practitioners. The use of advanced vibrational spectroscopy has been recently shown to have great potential as a diagnostic technique for various diseased tissues because it analyses the chemical composition of diseased tissue rather than its anatomy and predicts disease progression. This review article includes a summary of the application of Fourier transform infrared (FT-IR) and Raman spectroscopic or microspectroscopic analysis for the rapid diagnosis and identification of the chemical composition of skin calcified deposits in patients with various CC symptoms. Both advanced techniques not only can detect the types of insoluble salts such as calcium phosphate, calcium carbonate, and monosodium urate, and β-carotene in the calcified deposits of human skin tissue but also can directly differentiate the carbonate substitution in the apatite structure of the skin calcified deposits. In particular, the combination of both vibrational techniques may provide complementary information to simultaneously assess the intact components of the calcified deposits. In the future, both FT-IR and Raman vibrational microspectroscopic techniques will become available tools to support the standard test techniques currently used in some clinical diagnoses. Molecular spectroscopy technique is rapidly changing disease diagnosis and management.

Advances in the clinical application of Raman spectroscopy for cancer diagnostics

Light interacts with tissue in a number of ways including, elastic and inelastic scattering, reflection and absorption, leading to fluorescence and phosphorescence. These interactions can be used to measure abnormal changes in tissue. Initial optical biopsy systems have potential to be used as an adjunct to current investigative techniques to improve the targeting of blind biopsy. Future prospects with molecular-specific techniques may enable objective optical detection providing a real-time, highly sensitive and specific measurement of the histological state of the tissue. Raman spectroscopy has the potential to identify markers associated with malignant change and could be used as diagnostic tool for the early detection of precancerous and cancerous lesions in vivo. The clinical requirements for an objective, non-invasive, real-time probe for the accurate and repeatable measurement of pathological state of the tissue are overwhelming. This paper discusses some of the recent advances in the field.

Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis

OBJECTIVE

Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis.

BACKGROUND DATA

Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively.

METHODS

Skin biopsy fragments of ≈ 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues.

RESULTS

We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p<0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%.

CONCLUSIONS

This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

Advanced endoscopic imaging in Barrett's oesophagus

Barrett's oesophagus is a metaplastic condition with an inherent risk of progression to adenocarcinoma. It is essential to identify dysplastic changes within Barrett's oesophagus in order to individualise surveillance strategies and establish which patients warrant endoscopic treatment. There is a trend towards endoscopic resection of focal high-grade dysplasia followed by whole segment ablation. However, endoscopic identification of dysplastic lesions is unreliable and subjective making targeted therapy extremely difficult. In addition, the current practice of taking random quadrantic biopsies may miss dysplastic disease and intramucosal adenocarcinoma. Several advanced endoscopic imaging techniques have been described and tested in clinical trials in an effort to improve the detection of early lesions, although none are routinely used in clinical practice. In this article we will review these techniques and discuss their potential for clinical implementation. We will also discuss the potential benefits of multimodal imaging and highlight several newer techniques which have shown early promise for in vivo diagnosis.

Vibrational spectroscopy: a tool being developed for the noninvasive monitoring of wound healing

Wound care and management accounted for over 1.8 million hospital discharges in 2009. The complex nature of wound physiology involves hundreds of overlapping processes that we have only begun to understand over the past three decades. The management of wounds remains a significant challenge for inexperienced clinicians. The ensuing inflammatory response ultimately dictates the pace of wound healing and tissue regeneration. Consequently, the eventual timing of wound closure or definitive coverage is often subjective. Some wounds fail to close, or dehisce, despite the use and application of novel wound-specific treatment modalities. An understanding of the molecular environment of acute and chronic wounds throughout the wound-healing process can provide valuable insight into the mechanisms associated with the patient's outcome. Pathologic alterations of wounds are accompanied by fundamental changes in the molecular environment that can be analyzed by vibrational spectroscopy. Vibrational spectroscopy, specifically Raman and Fourier transform infrared spectroscopy, offers the capability to accurately detect and identify the various molecules that compose the extracellular matrix during wound healing in their native state. The identified changes might provide the objective markers of wound healing, which can then be integrated with clinical characteristics to guide the management of wounds.

Raman Spectroscopy for Clinical Oncology

Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.

Raman spectroscopy: a potential tool for early objective diagnosis of neoplasia in the oesophagus

There is a profound clinical need for a diagnostic tool that will enable clinicians to identify early neoplastic change in the oesophagus. Raman Spectroscopy (RS) has demonstrated the potential to provide non-invasive, rapid, objective diagnosis of endoscopically invisible precancerous oesophageal dysplasia in vitro. RS analyses biological material to identify highly specific biochemical information that can be used to influence clinical care. Raman spectroscopic mapping could provide automated assessment of tissue biopsies to aid histopathological diagnosis in vitro. Furthermore, the recent development of fibre-optic Raman probes has enabled endoscopic assessment of oesophageal mucosa in vivo. Accurate identification of dysplasia will enable targeted endoscopic resection of early lesions preventing the development of oesophageal cancer. This review summarises the development of Raman systems for use as laboratory based analytical adjuncts and endoscopic diagnostic tools in the distal oesophagus.

Evaluation of Raman probe for oesophageal cancer diagnostics

Early detection of (pre-)cancerous changes improves prognosis, therefore in the UK patients at high risk of developing gastrointestinal cancers are enrolled on endoscopic surveillance programmes or the Bowel Cancer Screening Programme. The current gold standard technique for the detection of pre-cancerous changes in the gastrointestinal tract is histopathological analysis of biopsy tissue collected at endoscopy. This relies upon subjective assessment of morphological changes within the excised tissue samples and poor targeting of pre-malignant lesions. Raman spectroscopy offers a number of potential advantages for in vivo assessment of tissue at endoscopy. The performance of a custom built Raman probe as a biopsy targeting tool has been evaluated using excised biopsy material. Multivariate classification models have been used to demonstrate the likely ability of a miniature, confocal, fibre optic Raman probe to be used as an optical biopsy tool at endoscopy to provide spectral information in clinically practicable timescales. This technique could facilitate improved targeting of excisional biopsy with associated clinical benefits.