Application of Raman spectroscopy in type 2 diabetes screening in blood using leucine and isoleucine amino-acids as biomarkers and in comparative anti-diabetic drugs efficacy studies

Raman Spectroscopy and Cystic Fibrosis Disease: An Alternative Potential Tool for Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Modulator Response Differentiation-A Pilot Study Based on Serum Samples

Cystic fibrosis (CF) is a genetic disorder that alters chloride transport in mucous membranes. Recent studies have demonstrated that treatment with modulators of the chloride channel reduces inflammatory markers, restoring, among others, the imbalance of lipids. In this study, we analyzed the serum samples of treated and non-treated patients with modulators with Raman spectroscopy. Nineteen (eight treated an eleven non-treated) patients were considered. The main difference between the two groups appeared in the 3020-2800 cm-1 range. A Voigt deconvolution fit was performed, and nine sub-bands were identified. To distinguish between treated and non-treated patients, the area ratio between the CH3 and CH2 vibration modes was calculated for each patient. The results were validated using statistical analyses. In particular, receiver operating characteristic (ROC) curves and Youden index (Y) were calculated (Area Under Curve (AUC): 0.977; Y: 3.30). An ROC curve represents the performance of the classification, illustrating the diagnostic ability of Raman spectroscopy. It was demonstrated that Raman spectroscopy is able to highlight peculiar differences between elexacaftor/tezacaftor/ivacaftor (ETI)-treated and non-treated patients, in relation with lipids biomarkers.

Raman spectroscopy analysis of plasma of diabetes patients with and without retinopathy, nephropathy, and neuropathy

Diabetes is now one of the major public health challenges, globally. Prolonged diabetes leads to various diabetic microvascular complications (DMCs) like retinopathy, nephropathy, and neuropathy. Multiple factors are likely to be involved in predisposing diabetic individuals to complications. Early detection or diagnosis is essential in developing strategies to reduce the risk factors and management costs of these diabetic complications. In this study, we employed Raman Spectroscopy (RS) to analyse the plasma samples of diabetes patients without and with DMCs along with the plasma samples of healthy subjects. Spectral comparisons revealed decrease in protein content in Diabetes group and further subsequent decrease in proteins in DMC groups when compared with control group, which corroborates with the fact that there exists increased secretion of proteins in urine and corresponding decreased protein content in their blood in case of diabetic individuals. Among all study groups, it was noted that 75% of control spectra show correct classification, while spectral misclassification is high amongst the subjects with Diabetes and DMCs. Interestingly, very few Diabetes and DMC plasma spectra are misclassified as control spectra. Findings demonstrate that 70% of the Diabetes subjects without complications can be correctly identified from diabetes with complications. Further, investigations could also attempt to explore the use of serum instead of plasma to reduce the spectral misclassifications as one of the abundant constituents namely clotting factors could be avoided. The outcome of RS study may be imminent for the early detection or diagnosis of DMCs.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Fabrication of surface-enhanced Raman spectroscopy substrates using silver nanoparticles produced by laser ablation in liquids

This research describes the use of surface-enhanced Raman spectroscopy (SERS) substrates based on colloidal silver nanoparticles (AgNPs) produced by laser ablation of silver granules in pure water that are inexpensive, easy to make, and chemically stable. Here, the effects of the laser power, pulse repetition frequency, and ablation duration on the Surface Plasmon Resonance peak of AgNPs solutions, were used to determine the optimal parameters. Also, the effects of the laser ablation time on both ablation efficiency and SERS enhancement were studied. The synthesized AgNPs were characterized by UV-Vis spectrophotometer, Scanning Electron Microscope (SEM), and Raman spectrometer. The Surface Plasmon Resonance peak of AgNP solutions was centered at 404 nm confirming their synthesis and they were noted to be spherical with 34 nm in diameter. Using Raman spectroscopy, they had main bands centered at 196 cm^-1 (O = Ag₂/Ag-N stretching vibrations), 568 cm^-1 (NH out of plane bending); 824 cm^-1 (symmetric deformation of the NO₂); 1060 cm^-1 (NH out of plane bending); 1312 cm^-1 (symmetric stretching of NO₂); 1538 cm^-1 (NH in-plane bending); and 2350 cm^-1 (N₂ vibrations). Their Raman spectral profiles remained constant within the first few days of storage at room temperature implying chemical stability. The Raman signals from blood were enhanced when mixed with AgNPs and this depended on colloidal AgNPs concentration. Using those generated by 12 h ablation time, an enhancement of 14.95 was achieved. Additionally, these substrates had an insignificant impact on the Raman profiles of samples of rat blood when mixed with them. The Raman peaks noted were attributed to CC stretching of glucose (932 cm^-1); CC stretching of Tryptophan (1064 cm^-1); CC stretching of β Carotene (1190 cm^-1); CH₂ wagging of proteins (1338 and 1410 cm^-1); carbonyl stretch for proteins (1650 cm^-1); CN vibrations for glycoproteins (2122 cm^-1). These SERS substrates can be applied to areas such as forensics to distinguish between human and other animal blood, monitoring of the efficacy of drugs, disease diagnostics such as diabetes, and pathogen detection. All this can be achieved by comparing the Raman spectra of the biological samples mixed with the synthesized SERS substrates for different samples. Thus, the results on the use of inexpensive, simple-to-prepare Raman substrates have the possibility of making surface-enhanced Raman spectroscopy available to laboratories with scarce resources in developing nations.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm^-1 and (1385-1520) cm^-1 bands. In the (1300-1360) cm^-1 band, lipids and tryptophan were identified; in the (1385-1520) cm^-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Establishing the relations of characteristic aroma precursors and volatile compounds for authenticating Tibetan pork

Tibetan pork has been favored for its unique aromas, which originate from chemical reactions between characteristic precursors in cooking. The precursors (e.g., fatty acids, free amino acids, reducing sugars, and thiamine) of Tibetan pork ((semi-) free range) from different regions in China, comprising Tibet, Sichuan, Qinghai, and Yunnan, and commercial pork (indoor reared) were compared in this study. Tibetan pork was characterized by higher ω-3 polyunsaturated fatty acids (i.e., C18:3n3), higher essential (i.e., valine, leucine, and isoleucine), aromatic (i.e., phenylalanine), and sulfur-containing (i.e., methionine and cysteine) free amino acids, higher thiamine, and lower reducing sugars. Boiled Tibetan pork exhibited higher heptanal, 4-heptenal, and 4-pentylbenzaldehyde compared with commercial pork. The results from multivariate statistical analysis revealed that precursors combined with volatiles exhibited discriminating capability for characterizing Tibetan pork. The precursors in Tibetan pork exerted a certain effect on characteristic aroma generation, probably arising from promoting chemical reactions in cooking.

Development of a novel method for analysing N-acetyl-DL-leucine enantiomers in human fingernail by UPLC-ESI-MS/MS and the evaluation in diabetes mellitus

BACKGROUND

Recent research has been reported that N-acetyl-leucine content is significantly reduced in the saliva of diabetic patients, but no reports of detection in human nails have been found. This study aims to develop a novel method for the chiral separation of N-acetyl-DL-leucine (Ac-DL-Leu) in human fingernails to investigate the differences between healthy volunteers (HVs), prediabetes (PDs) and diabetic patients (DPs), and to verify its effectiveness in early warning of diabetes.

METHOD

Chiral resolution was performed using DBD-Apy pre-column derivatization on a C18 column (2.1 × 150 mm, 1.9 μm) at 40 ^oC, and detected by UPLC-ESI-MS/MS.

RESULTS

The resolution and the limit of detection (LOD) of Ac-DL-Leu were 1.75 and 1.50 fmol, respectively. The linear range of Ac-DL-Leu was 10-2000 fmol and the determination coefficient (R²) was above 0.9997. The recovery of Ac-DL-Leu in human nails was 96.92-105.69%. The contents of Ac-D-Leu and Ac-L-Leu were analyzed in 18 HVs, 13 PDs and 16 DPs fingernails. The results showed that their contents were significantly lower in DPs than in PDs and HVs (p < 0.0001).

CONCLUSIONS

A method for evaluating the effectiveness of Ac-DL-Leu enantiomers in human fingernails as a biomarker for diabetes was firstly developed.

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

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

Preliminary study for the application of Raman spectroscopy for the identification of Leishmania infected dogs

Raman spectroscopy is a rapid qualitative and quantitative technique that allows the simultaneous determination of several components in biological fluids. This methodology concerns an alternative technique to distinguish between non-healthy and healthy subjects. Leishmaniasis is a zoonosis of world interest, the most important agent is L. infantum. Dogs are the principal reservoirs affected by a broad spectrum of clinical features. During a clinical exam, blood samples were collected in tubes without anticoagulants, from twenty two dogs. One aliquot was used for serological test for Leishmaniasis, one aliquot was subjected to the Raman spectroscopic analysis. Animals were divided into two groups of equal subjects, Leishmania group (LG) constituted by infected dogs, and control group (CG) constituted by healthy dogs. The acquired spectra were different in the region 1200-1370 cm^-1, in which it is possible to distinguish the amide III vibration (~ 1300 cm^-1). In LG, an evident shift to the shortwave region is observed in spectral frequencies of the band centered at ~ 1250 cm^-1. Our results distinguished between LD group and CG. Further studies are necessary to exclude the effect of metabolic modification due to disease on the recorded spectra changes and to consolidate the achievability of Raman spectroscopy as rapid and less expensive diagnosis of Leishmaniasis.

Analysis of amino acids content in the plant components of the antidiabetic herbal mixture by GC-MS

Medicinal plants and their combinations due to the wide range of biologically active substances can influence on various links of the pathogenetic mechanism of development of DM type 2 and its complications. One of such combinations is an antidiabetic herbal mixture (Urticae folia, Rosae fructus, Myrtilli folia, Menthae folia and Taraxaci radices) with established hypoglycaemic, hypolipidemic, antioxidant, hepatoprotective, pancreatoprotective activity in previous pharmacological studies in vivo and in vitro and defined phytochemical composition. Thus, the aim of this study was to identify and establish the content of amino acids in the plant components of antidiabetic herbal mixture. The amino acids were separated by GC-MS method with pre-column derivatization. The calibration curves of twenty CRS of amino acids were linear (R2 &gt; 0.98) over the range of 1–100 µg/mL, the LODs and the LOQs were in the range of 0.01–0.07 µg/mL and 0.02–0.20 µg/mL, respectively. The results of analysis showed that the predominant essential amino acid was L-proline in Taraxaci radices, Urticae folia, Rosae fructus and Menthae folia, its total content was 101.46 mg/g, 25.31 mg/g, 23.04 mg/g and 19.30 mg/g, respectively. In addition, it was established total content of essential amino acid – L-leucine that can stimulate insulin secretion in β-cells of the pancreas. Its total content was 58.51 mg/g in Taraxaci radices, 9.58 mg/g in Myrtilli folia, 4.68 mg/g in Rosae fructus, 2.99 mg/g in Urticae folia and 0.79 mg/g in Menthae folia. Chromatographic examination also revealed L-phenylalanine, an essential amino acid important for antidiabetic therapy that can increase insulin secretion, stimulate proliferation and neogenesis of β-cells of the pancreas and reduce insulin resistance. Its total content was 13.42 mg/g in Myrtilli folia, 2.23 mg/g in Rosae fructus, 1.478 mg/g in Urticae folia, 1.46 mg/g in Taraxaci radices and 0.52 mg/g in Menthae folia. This phytochemical study shows, which plant material forms the amino acid composition and content in the finished herbal mixture and due to which biologically active substances the antidiabetic activity of this phytocomposition is manifested.

Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy

We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.

Vibrational Spectroscopy for Detection of Diabetes: A Review

Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimer's disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over four million deaths each year. Current methods for detecting diabetes typically involve testing a person's glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individual's levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

Investigation of squalene-doxorubicin distribution and interactions within single cancer cell using Raman microspectroscopy

Intracellular distribution of doxorubicin (DOX) and its squalenoylated (SQ-DOX) nanoparticles (NPs) form in murine lung carcinoma M109 and human breast carcinoma MDA-MB-231 cells was investigated by Raman microspectroscopy. Pharmacological data showed that DOX induced higher cytotoxic effect than SQ-DOX NPs. Raman data were obtained using single-point measurements and imaging on the whole cell areas. These data showed that after DOX treatment at 1 μM, the spectral features of DOX were not detected in the M109 cell cytoplasm and nucleus. However, the intracellular distribution of SQ-DOX NPs was higher than DOX in the same conditions. In addition, SQ-DOX NPs were localized into both cell cytoplasm and nucleus. After 5 μM treatment, Raman bands of DOX at 1211 and 1241 cm^-1 were detected in the nucleus. Moreover, the intensity ratio of these bands decreased, indicating DOX intercalation into DNA. However, after treatment with SQ-DOX NPs, the intensity of these Raman bands increased. Interestingly, with SQ-DOX NPs, the intensity of 1210/1241 cm^-1 ratio was higher suggesting a lower fraction of intercalated DOX in DNA and higher amount of non-hydrolyzed SQ-DOX. Raman imaging data confirm this subcellular localization of these drugs in both M109 and MDA-MB-231 cells. These finding brings new insights to the cellular characterization of anticancer drugs at the molecular level, particularly in the field of nanomedicine.

Hypoglycemic Effect of Hydrophobic BCAA Peptides Is Associated with Altered PI3K/Akt Protein Expression

The hypoglycemic activities of the hydrophobic branched-chain amino acid (BCAA) peptides from seabuckthorn seed protein were preliminarily characterized in type 2 diabetic db/db mice. Four novel BCAA peptides (18.27 ± 0.26% (w/w): Leu/Ile-Pro-Glu-Asp-Pro, Asp-Leu/Ile-Val-Gly-Glu, Leu/Ile-Pro, and Leu/Ile-Pro-Leu/Ile) were identified in seabuckthorn seed protein. The protein content in seabuckthorn seed protein hydrolysate, obtained using 80% ethanol, was 78.8 ± 1.4% (w/w). Animal experiments revealed that oral administration of BCAA peptides (all four) significantly reversed the diabetic symptoms. Compared to the db/db group (control), body weight and insulin resistance were ameliorated after treatment with BCAA peptides (0.5, 1.0, 2.0 mg/(g d)). Also, the treatment remarkably reduced the fasting blood glucose (FBG) levels by upregulation of glucose transporter 4 (GULT4). Moreover, BCAA peptides significantly increased the muscle glycogen content (22.6 ± 0.9 nmol/mg) via the downregulation of protein kinase B (AKT) and glycogen synthase kinase-3β (GSK-3β) while increasing the activity of glycogen synthase (GS). BCAA peptides also significantly upregulated the protein levels of phosphatidylinositol 3-kinase (PI3K). We show that BCAA peptides alleviated insulin resistance associated with altered PI3K/Akt protein expression in the skeletal muscle of db/db mice.

Determination of amino acids content in two samples of the plant mixtures by GC-MS

Due to the wide range of biologically active substances, the plant mixtures can influence the development of diabetes mellitus and its complications. Amino acids attract particular attention due to their ability to stimulate insulin secretion, reduce hyperglycemia and regulate metabolic processes in patients with diabetes. The aim of this study was to investigate the content of amino acids in the plant mixture samples: 1) Cichorium intybus roots, Elymus repens rhizome, Helichrysum arenarium flowers, Rosa majalis fruits, Zea mays columns with stigmas, 2) Urtica dioica leaf, Taraxacum officinale roots, Vaccinium myrtillus leaf, Rosa majalis fruits, Mentha piperita herb, which have proven antidiabetic activity in studies in vivo. The amino acids were separated by validated method of gas chromatography-mass spectrometry with pre-column derivatisation. Quantitative analyses of amino acids showed that the predominant components were L-proline in the sample 1 and L-leucine and L-proline in the sample 2 of the plant mixtures.

On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks

In this paper, we present an analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions. Indeed, we analysed our method for the estimation of sucrose in three solid industrialized food products: donuts, cereal, and cookies. Concentrations were estimated in two ways: using a non-linear fitting system, and using a classifier. Our experiments showed that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a Linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS). The best-case obtained by an FFNN for water solutions was 93.33% of classification and 3.51% of Root Mean Square Error in Prediction (RMSEP), compared with 82.22% obtained by a LDA. Our proposed method got an RMSEP of 1% for the best-case obtained with the food products.

Probing blood plasma samples for the detection of diabetes using SERS aided by PCA and LDA multivariate data analyses

Fabrication of a SERS-active substrate using Langmuir–Blodgett and self-assembly techniques for the detection of diabetes from blood plasma samples.

Biophotonics methods for functional monitoring of complications of diabetes mellitus

The prevalence of diabetes complications is a significant public health problem with a considerable economic cost. Thus, the timely diagnosis of complications and prevention of their development will contribute to increasing the length and quality of patient life, and reducing the economic costs of their treatment. This article aims to review the current state-of-the-art biophotonics technologies used to identify the complications of diabetes mellitus and assess the quality of their treatment. Additionally, these technologies assess the structural and functional properties of biological tissues, and they include capillaroscopy, laser Doppler flowmetry and hyperspectral imaging, laser speckle contrast imaging, diffuse reflectance spectroscopy and imaging, fluorescence spectroscopy and imaging, optical coherence tomography, optoacoustic imaging and confocal microscopy. Recent advances in the field of optical noninvasive diagnosis suggest a wider introduction of biophotonics technologies into clinical practice and, in particular, in diabetes care units.

Assessment of recombinant protein production in E. coli with Time-Gated Surface Enhanced Raman Spectroscopy (TG-SERS)

Time-Gated Surface-Enhanced Raman spectroscopy (TG-SERS) was utilized to assess recombinant protein production in Escherichia coli. TG-SERS suppressed the fluorescence signal from the biomolecules in the bacteria and the culture media. Characteristic protein signatures at different time points of the cell cultivation were observed and compared to conventional continuous wave (CW)-Raman with SERS. TG-SERS can distinguish discrete features of proteins such as the secondary structures and is therefore indicative of folding or unfolding of the protein. A novel method utilizing nanofibrillar cellulose as a stabilizing agent for nanoparticles and bacterial cells was used for the first time in order to boost the Raman signal, while simultaneously suppressing background signals. We evaluated the expression of hCNTF, hHspA1, and hHsp27 in complex media using the batch fermentation mode. HCNTF was also cultivated using EnBase in a fed-batch like mode. HspA1 expressed poorly due to aggregation problems within the cell, while hCNTF expressed in batch mode was correctly folded and protein instabilities were identified in the EnBase cultivation. Time-gated Raman spectroscopy showed to be a powerful tool to evaluate protein production and correct folding within living E. coli cells during the cultivation.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy's multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Metabolomics Study of Metabolic Changes in Renal Cells in Response to High-Glucose Exposure Based on Liquid or Gas Chromatography Coupled With Mass Spectrometry

Diabetic nephropathy (DN) is one of the most serious microvascular complications and the leading causes of death in diabetes mellitus (DM). To find biomarkers for prognosing the occurrence and development of DN has significant clinical value for its prevention, diagnosis, and treatment. In this study, a non-targeted cell metabolomics–based ultra-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry and gas chromatography coupled with mass spectrometry was developed and performed the dynamic metabolic profiles of rat renal cells including renal tubular epithelial cells (NRK-52E) and glomerular mesangial cells (HBZY-1) in response to high glucose at time points of 12 h, 24 h, 36 h, and 48 h. Some potential biomarkers were then verified using clinical plasma samples collected from 55 healthy volunteers, 103 DM patients, and 57 DN patients. Statistical methods, such as principal component analysis and partial least squares to latent structure-discriminant analysis were recruited for data analyses. As a result, palmitic acid and linoleic acid (all-cis-9,12) were the potential indicators for the occurrence and development of DN, and valine, leucine, and isoleucine could be used as the prospective biomarkers for DM. In addition, rise and fall of leucine and isoleucine levels in plasma could be used for prognosing DN in DM patients. Through this study, we established a novel non-targeted cell dynamic metabolomics platform and identified potential biomarkers that may be applied for the diagnosis and prognosis of DM and DN.

In Vitro and In Vivo SERS Biosensing for Disease Diagnosis

For many disease states, positive outcomes are directly linked to early diagnosis, where therapeutic intervention would be most effective. Recently, trends in disease diagnosis have focused on the development of label-free sensing techniques that are sensitive to low analyte concentrations found in the physiological environment. Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy that allows for label-free, highly sensitive, and selective detection of analytes through the amplification of localized electric fields on the surface of a plasmonic material when excited with monochromatic light. This results in enhancement of the Raman scattering signal, which allows for the detection of low concentration analytes, giving rise to the use of SERS as a diagnostic tool for disease. Here, we present a review of recent developments in the field of in vivo and in vitro SERS biosensing for a range of disease states including neurological disease, diabetes, cardiovascular disease, cancer, and viral disease.