In vivo detection of human cutaneous beta‐carotene using computational optical clearing

The content of dermal beta‐carotene can be a good indicator showing the body health. Because, it is involved in production of vitamin A maintaining healthy skin and mucous membranes. Also, it reduces the risk of cardiovascular diseases and its antioxidant capacity prevents the formation of cancerous cells. In this work, we use Raman spectroscopy and a low‐cost diffuse reflectance spectroscopy (DRS) to detect the dermal beta‐carotene spectra. We apply computational optical clearing (OC) method to in vivo evaluation the concentration of this chromophore. The results show that Raman spectroscopy is a good tool for in vitro detection of carotenoids but is not able to clearly discriminate the individual carotenoids in skin tissue in vivo. The results also show that using OC enhances the ability of low‐cost diffuse reflection spectroscopy for in vivo detection of dermal beta‐carotene in humans. This method can be used as a low‐cost and portable device to screening the concentration of chromophores such as melanin and carotenoid molecules for oncological studies.     

Urine analysis using FTIR spectroscopy: A study on healthy adults and children

Urine spectra from 108 healthy volunteers are studied by attenuated total refraction-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral features are correlated with observable urine components. The variation of spectra within a healthy population is quantified and a library of reference spectra is constructed. Using the band assignments, these spectra are compared with both age-wise and gender-wise. Children show the least intensity variations compared to both adult groups. Young adults show the highest variation, particularly in the 1650 to 1400 cm⁻¹ and 1200 to 900 cm⁻¹ regions. These results indicate the importance of the size of the control group in comparative studies utilizing FTIR. Age-wise comparisons reveal that phosphate and sulfate excretion decreases with age, and that the variance of phosphate among individuals is higher with adults. As for gender-wise comparisons, females show a slightly higher citrate content at 1390 cm⁻¹ regardless of the age and they show a higher variance in the 1200 to 1000 cm⁻¹ region when compared to men.     

Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.     

Effect of hemolysis on Fourier transform infrared and Raman spectra of blood plasma

Hemolysis is a very common phenomenon and is referred as the release of intracellular components from red blood cells to the extracellular fluid. Hemolyzed samples are often rejected in clinics due to the interference of hemoglobin and intracellular components in laboratory measurements. Plasma and serum based vibrational spectroscopy studies are extensively applied to generate spectral biomarkers for various diseases. However, no studies have reported the effect of hemolysis in blood based vibrational spectroscopy studies. This study was undertaken to evaluate the effect of hemolysis on infrared and Raman spectra of blood plasma. In this study, prostate cancer plasma samples (n = 30) were divided into three groups (nonhemolyzed, mildly hemolyzed, and moderately hemolyzed) based on the degree of hemolysis and FTIR and Raman spectra were recorded using high throughput (HT)‐FTIR and HT‐Raman spectroscopy. Discrimination was observed between the infrared and Raman spectra of nonhemolyzed and hemolyzed plasma samples using principal component analysis. A classical least square fitting analysis showed differences in the weighting of pure components in nonhemolyzed and hemolyzed plasma samples. Therefore, it is worth to consider the changes in spectral features due to hemolysis when comparing the results within and between experiments.     

Raman Tweezers Spectroscopy of Live,Single Red and White Blood Cells

An optical trap has been combined with a Raman spectrometer to make high-resolution measurements of Raman spectra of optically-immobilized, single, live red (RBC) and white blood cells (WBC) under physiological conditions. Tightly-focused, near infrared wavelength light (1064 nm) is utilized for trapping of single cells and 785 nm light is used for Raman excitation at low levels of incident power (few mW). Raman spectra of RBC recorded using this high-sensitivity, dual-wavelength apparatus has enabled identification of several additional lines; the hitherto-unreported lines originate purely from hemoglobin molecules. Raman spectra of single granulocytes and lymphocytes are interpreted on the basis of standard protein and nucleic acid vibrational spectroscopy data. The richness of the measured spectrum illustrates that Raman studies of live cells in suspension are more informative than conventional micro-Raman studies where the cells are chemically bound to a glass cover slip.     

Label‐free diagnosis for colorectal cancer through coffee ring‐assisted surface‐enhanced Raman spectroscopy on blood serum

Surface‐enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle‐serum mixture formed coffee‐ring‐like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support‐vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.     

Noninvasive assessments of skin glycated proteins by fluorescence and Raman techniques in diabetics and nondiabetics

Diabetes is a complex metabolic disease and has chronic complications. It has been considered a serious public health problem. The aim of the current study was to evaluate skin glycated proteins through fluorescence and Raman techniques. One hundred subjects were invited to participate in the study. Six volunteers did not attend due to exclusion criteria or a change of mind about participating. Therefore, 94 volunteers were grouped according to age range (20‐80 years), health condition (nondiabetic, with insulin resistance [IR] and/or diabetic) and Fitzpatrick skin type (I‐VI). The fluorescence spectrometer and the portable Raman spectroscopy system were used to measure glycated proteins from the skin. There was elevated skin autofluorescence in healthy middle‐aged and elderly subjects, as well as in patients with IR and/or diabetes. Regarding Raman spectroscopy, changes in the skin hydration state, degradation of type I collagen and greater glycation were related for diabetes and chronological aging. Weak and positive correlation between the skin autofluorescence and the Raman peaks ratio (855/876) related to the glycated proteins was also found. Raman spectroscopy shows several bands for spectral analyses, complementing the fluorescence data. Therefore, this study contributes to understanding of the optical of human skin for noninvasive diabetes screening.      

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

The present paper studies the applicability of a portable cost‐effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near‐infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals.      

Combining near-infrared-excited autofluorescence and Raman spectroscopy improves in vivo diagnosis of gastric cancer

This study aims to evaluate the diagnostic utility of the combined near-infrared (NIR) autofluorescence (AF) and Raman spectroscopy for improving in vivo detection of gastric cancer at clinical gastroscopy. A rapid Raman endoscopic technique was employed for in vivo spectroscopic measurements of normal (n=1098) and cancer (n=140) gastric tissues from 81 gastric patients. The composite NIR AF and Raman spectra in the range of 800-1800 cm(-1) were analyzed using principal component analysis (PCA) and linear discriminant (LDA) to extract diagnostic information associated with distinctive spectroscopic processes of gastric malignancies. High quality in vivo composite NIR AF and Raman spectra can routinely be acquired from the gastric within 0.5s. The integrated intensity over the range of 800-1800 cm(-1) established the diagnostic implications (p=1.6E-14) of the change of NIR AF intensity associated with neoplastic transformation. PCA-LDA diagnostic modeling on the in vivo tissue NIR AF and Raman spectra acquired yielded a diagnostic accuracy of 92.2% (sensitivity of 97.9% and specificity of 91.5%) for identifying gastric cancer from normal tissue. The integration area under the receiver operating characteristic (ROC) curve using the combined NIR AF and Raman spectroscopy was 0.985, which is superior to either the Raman spectroscopy or NIR AF spectroscopy alone. This work demonstrates that the complementary Raman and NIR AF spectroscopy techniques can be integrated together for improving the in vivo diagnosis and detection of gastric cancer at endoscopy.     

Transcranial oximetry using fast near infrared spectroscopy can detect failure of collateral blood supply in humans

We tested the hypothesis that transcranial oximetry by fast scanning near infrared spectroscopy can detect local desaturation of hemoglobin in arterial vessels of cerebral circulation with impaired blood supply. A total of 74 near infrared spectroscopy recordings were taken from the intact skull of humans. Perfusion of the hemisphere under the detector was assessed in one of four groups: (1) healthy volunteer; (2) patient, unaffected side; (3) patient, affected side with intact collateral blood supply; (4) patient, affected side, impaired collateral blood supply. Transcranial saturation was 0.90+/-0.01 (all values reported as mean+/-S.E.) in healthy volunteers (n=24), 0.92+/-0.008 in the unaffected hemisphere of patients (n=23), 0.92+/-0.001 in the affected side if collateral supply with blood was intact (n=16). There was no statistical significance between these groups. Saturation in affected hemispheres with impaired collateral blood supply (n=9) was 0.81+/-0.028, which was significantly different from all other groups (P<0.05, one way-ANOVA). We conclude, that transcranial pulse oximetry can detect local hypoxia if collateral blood supply fails.     

Nuclear magnetic resonance studies of blood plasma and urine from subjects with chronic renal failure: identification of trimethylamine-N-oxide

We have used ¹H-, ¹³C- and ¹⁴N-NMR spectroscopy to investigate the constituents of plasma and urine in 16 patients with chromic renal failure (CRF). Resonances not previously observed in spectra of plasma from healthy volunteers were seen in CRF plasma, including those for trimethylamine-N-oxide (TMAO) and dimethylamine (DMA). A possible analogy with the plasma of elasmobranch fishes, in which TMAO stabilizes proteins in the presence of very high urea concentrations, is noted. The intensity of the TMAO resonance for CRF subjects was correlated with the plasma concentration of urea (R = 0.55) and creatinine (R = 0.74), suggesting that the presence of TMAO is closely related to the degree of renal failure. When normal subjects ate a meal of TMAO-containing fish, TMAO appeared rapidly in the plasma and in the urine. Thus TMAO is efficiently cleared by the healthy kidney. Differences in the interaction of lactate with plasma proteins were detected by NMR, suggesting that uraemia impairs their transport roles.     

卵巢癌患者血清的拉曼光谱研究初探

目的:探究拉曼光谱技术应用于卵巢癌研究的可能性。方法:收集卵巢癌患者血清及健康人血清各20例,用激光共聚焦显微拉曼光谱仪进行检测。结果:两组血清的平均拉曼光谱形态和谱峰基本相似,但在约1010、1158、1283、1520、1646、2307和2661cm-17个拉曼频移附近,卵巢癌患者血清的拉曼光谱谱峰强度明显低于健康对照组,而在其余大部分波段,卵巢癌患者血清的拉曼光谱强度均高于健康对照组。结论:拉曼光谱技术可以初步区分卵巢癌及健康人血清,值得进一步研究和探讨其临床应用价值。     

Combined in vivo confocal Raman spectroscopy and confocal microscopy of human skin

In vivo confocal Raman spectroscopy is a noninvasive optical method to obtain detailed information about the molecular composition of the skin with high spatial resolution. In vivo confocal scanning laser microscopy is an imaging modality that provides optical sections of the skin without physically dissecting the tissue. A combination of both techniques in a single instrument is described. This combination allows the skin morphology to be visualized and (subsurface) structures in the skin to be targeted for Raman measurements. Novel results are presented that show detailed in vivo concentration profiles of water and of natural moisturizing factor for the stratum corneum that are directly related to the skin architecture by in vivo cross-sectional images of the skin. Targeting of skin structures is demonstrated by recording in vivo Raman spectra of sweat ducts and sebaceous glands in situ. In vivo measurements on dermal capillaries yielded high-quality Raman spectra of blood in a completely noninvasive manner. From the results of this exploratory study we conclude that the technique presented has great potential for fundamental skin research, pharmacology (percutaneous transport), clinical dermatology, and cosmetic research, as well as for noninvasive analysis of blood analytes, including glucose.     

Multispectral near infrared absorption imaging for histology of skin cancer

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800‐1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900‐1500 nm) was used for homogenous wide‐field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal‐cell carcinoma, squamous‐cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.      

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra

Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes – the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (≥90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.     

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.     

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10⁻¹⁶ ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.     

Assessing the effect of prolonged use of desloratadine on adipose Brillouin shift and composition in rats

Antihistamines, which are commonly used to treat allergic reactions, are known for their side effects, which contribute to weight gain. It is hypothesized that simultaneous Brillouin elastography and Raman spectroscopy can be used to detect changes in adipose tissue associated with a prolonged intake of desloratadine, a commonly used second generation antihistamine. White and brown adipose tissue samples were excised from adult rats following 16 weeks of daily administration of desloratadine. It was found that the prolonged intake of desloratadine leads to an increase in Brillouin shift in both adipose tissue types. Raman spectra indicate that antihistamine use reduces protein-to-lipid ratio in brown adipose tissue but not white adipose tissue, indicating the effect on adipose tissue is location-dependent.     

New insights into hidradenitis suppurativa diagnosis via salivary infrared biosignatures: A pilot study

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease which can lead to a prolonged physical disability. HS diagnosis is exclusively clinical with the absence of biomarkers. Our study aims at assessing the HS-diagnostic potential of infrared spectroscopy from saliva, as a biofluid reflecting the body's pathophysiological state. Infrared spectra from 127 patients (57 HS and 70 non-HS) were processed by multivariate methods: principal component analysis coupled with Kruskal–Wallis or Mann–Whitney tests to identify discriminant spectral wavenumbers and linear discriminant analysis to evaluate the performances of HS-diagnostic approach. Infrared features, mainly in the 1300 cm⁻¹-1600 cm⁻¹ region, were identified as discriminant for HS and prediction models revealed diagnostic performances of about 80%. Tobacco and obesity, two main HS risk factors, do not seem to alter the infrared diagnosis. This pilot study shows the potential of salivary “liquid biopsy” associated to vibrational spectroscopy to develop a personalized medical approach for HS patients' management.