Comparison of red blood cells from gastric cancer patients and healthy persons using FTIR spectroscopy

A biospectroscopic approach toward colorectal cancer diagnosis from bodily fluid samples via ATR-MIR spectroscopy combined with multivariate data analysis

In this study, a biospectroscopic approach was reported for the detection of spectral changes and biomarkers for the diagnosis of colorectal cancer (CC) cases from different bodily fluids (blood plasma, blood serum, saliva and colonoscopy disinfection/wash fluids) by using attenuated total reflection-mid infrared (ATR-MIR) spectroscopy. To recognize the molecular level changes in the spectral characteristics of CC and their healthy/control (CH) groups, different multivariate data analyses (HCA, LDA, PCA and SIMCA) were successfully performed over the data of ATR-MIR spectroscopy. Two hundred specimens were characterized in detail over the data of spectral regions (4000-650 cm-1 and regions V-XXII). The findings revealed that significant changes were clearly observed in the concentrations of lipid, protein, nucleic acid and carbohydrate biomolecules for cancer cases based upon their necessity to overcome energy requirements. Supervised multivariate data methodology SIMCA, presented an excellent classification for the studied groups; similarly 100% of the specimens from different bodily fluids were correctly classified by supervised methodology LDA. As a result, the developed ATR-MIR methodology for the classification of CC and their healthy groups highlighted a rapid cancer diagnosis approach from different bodily fluids; therefore, it could be guide to make well decision before histopathological assessment and to screen CC populations existing in society.

Vibrational spectroscopies for biochemical investigation of X-ray exposure effects on SH-SY5Y human neuroblastoma cells

Neuroblastoma is the most recurring cancer in childhood and adolescence. The SH-SY5Y neuroblastoma cell line is generally adopted for elaborating new therapeutical approaches and/or elaborating strategies for the prevention of central nervous system disturbances. In fact, it represents a valid model system for investigating in vitro the effects on the brain of X-ray exposure using vibrational spectroscopies that can detect early radiation-induced molecular alterations of potential clinical usefulness. In recent years, we dedicated significant efforts in the use of Fourier-transform and Raman microspectroscopy techniques for characterizing such radiation-induced effects on SH-SY5Y cells by examining the contributions from different cell components (DNA, proteins, lipids, and carbohydrates) to the vibrational spectra. In this review, we aim at revising and comparing the main results of our studies to provide a wide outlook of the latest outcomes and a framework for future radiobiology research using vibrational spectroscopies. A short description of our experimental approaches and data analysis procedures is also reported.

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

Evaluation of Proton-Induced Biomolecular Changes in MCF-10A Breast Cells by Means of FT-IR Microspectroscopy

Radiotherapy (RT) with accelerated beams of charged particles (protons and carbon ions), also known as hadrontherapy, is a treatment modality that is increasingly being adopted thanks to the several benefits that it grants compared to conventional radiotherapy (CRT) treatments performed by means of high-energy photons/electrons. Hence, information about the biomolecular effects in exposed cells caused by such particles is needed to better realize the underlying radiobiological mechanisms and to improve this therapeutic strategy. To this end, Fourier transform infrared microspectroscopy (μ-FT-IR) can be usefully employed, in addition to long-established radiobiological techniques, since it is currently considered a helpful tool for examining radiation-induced cellular changes. In the present study, MCF-10A breast cells were chosen to evaluate the effects of proton exposure using μ-FT-IR. They were exposed to different proton doses and fixed at various times after exposure to evaluate direct effects due to proton exposure and the kinetics of DNA damage repair. Irradiated and control cells were examined in transflection mode using low-e substrates that have been recently demonstrated to offer a fast and direct way to examine proton-exposed cells. The acquired spectra were analyzed using a deconvolution procedure and a ratiometric approach, both of which showed the different contributions of DNA, protein, lipid, and carbohydrate cell components. These changes were particularly significant for cells fixed 48 and 72 h after exposure. Lipid changes were related to variations in membrane fluidity, and evidence of DNA damage was highlighted. The analysis of the Amide III band also indicated changes that could be related to different enzyme contributions in DNA repair.

Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics-area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.

Characterisation of breast cancer molecular signature and treatment assessment with vibrational spectroscopy and chemometric approach

Triple negative breast cancer (TNBC) is regarded as the most aggressive breast cancer subtype with poor overall survival and lack of targeted therapies, resulting in many patients with recurrent. The insight into the detailed biochemical composition of TNBC would help develop dedicated treatments. Thus, in this study Fourier Transform Infrared microspectroscopy combined with chemometrics and absorbance ratios investigation was employed to compare healthy controls with TNBC tissue before and after chemotherapy within the same patient. The primary spectral differences between control and cancer tissues were found in proteins, polysaccharides, and nucleic acids. Amide I/Amide II ratio decrease before and increase after chemotherapy, whereas DNA, RNA, and glycogen contents increase before and decrease after the treatment. The chemometric results revealed discriminatory features reflecting a clinical response scheme and proved the chemotherapy efficacy assessment with infrared spectroscopy is possible.

A rapid diagnostic approach for gastric and colon cancers via Fourier transform mid-infrared spectroscopy coupled with chemometrics from paraffin-embedded tissues

This paper describes the feasibility of Attenuated total reflection-Fourier transform mid-infrared (ATR-MIR) spectroscopy method coupled with chemometrics for the rapid diagnostic approach and screening spectral changes for gastric and colon cancers from paraffin-embedded tissues. A total number of 82 tissue samples were analyzed by a simple ATR-MIR method combined with PCA, HCA, SIMCA and LDA methodologies. Spectral analyses showed significant differences for the molecular contents particularly about the lipid, nucleic acid, protein and other biomolecules in the samples of gastric cancer (GC) and colon cancer (CC) groups from their control/healthy groups. Significant changes in the characteristic of these molecules were only observed for cancer groups based upon the increment in their biosynthesis, and they could be utilized as diagnostic spectral biomarkers. Under the optimum conditions, SIMCA provided excellent classification for diseased and control groups, with 5% significance level. As well, 97.75% of the studied tissue samples were correctly discriminated on the basis of their origin by LDA. Consequently, the findings of this study highlighted the rapid diagnosis of gastric and colon cancer cases from paraffin-embedded tissues via ATR-MIR spectroscopy complemented with chemometrics.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

In vitro cell composition identification of wood decay fungi by Fourier transform infrared spectroscopy

Decay pathogens follow dissimilar metabolic mechanisms to cause irreversible damage to woody tissues. The objective of this study is to perform inter- and intra-species microbial cell structural comparison using attenuated total reflectance Fourier transform infrared spectroscopy. Representative fungi species, causing brown rot and white rot, namely, Postia placenta and Trametes versicolor, respectively, were cultured in laboratory conditions. In vitro spectral measurements were performed at periodic two week intervals of fungal growth. The study shows structural differences for both species of fungi. The prominent presence of protein amide, carbohydrate and carboxyl bands was of interest. Spectral deconvolution of the infrared broadband around approximately 3300 cm-1 produced peaks at four different wavenumbers. The hydrogen bond energy obtained at the four wavenumbers, from deconvolution, varied from approximately 41 kJ mol-1 to approximately 7 kJ mol-1, indicating the presence of strong and weak forces in microbial cell structure. The hydrogen bond distance, obtained at the deconvoluted wavenumbers, varied between 2.7 Å-2.8 Å, indicating the presence of short and long-distance forces within microbial cells. Microscopic observation showed mycelium colonization, hyphal tip and lateral branching.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.

Spectroscopic study of the effect of low dose fast neutrons on the hemoglobin structure

Cosmic rays, nuclear accidents, and neutron therapy could be sources for exposure to low-dose fast neutrons. However, the study of low dose effects needs sentient techniques to detect slight alteration happen by this low dose. Herein, the effects of low-dose fast neutrons on the structure of hemoglobin (Hb) using spectroscopic techniques, namely, Fourier transform infrared (FTIR), Raman, and ultraviolet-visible (UV-Vis) spectroscopic. Forty (20 control/20 irradiated) female Wistar rats were used in this work. The irradiated rats were irradiated to low-dose at a total dose of 10 mGy from a fast neutron source (241Am-Be, 0.2 mGy/h). Multivariate analyses were applied to differentiate between the control and irradiated rats' Raman spectra. The erythrocytes samples were isolated from whole blood to explore the Hb structure. FTIR results revealed changes in the ν(S-H) bond of α-104 and β-93 cysteines by low-dose fast neutrons. Raman spectra showed changes in the spin state and oxidation state of the iron atom of the Hb. Besides, deformation in methine C-H was recorded. UV-Vis spectroscopy disclosed that the irradiated rats might be more susceptive to oxidation than control rats. The study deduced that the low dose fast neutron could cause tiny Hb structure changes by indirect effects. Besides, the spectroscopic techniques showed a potent ability to reveal tiny changes in the Hb structure that happened by a low dose of fast neutrons.

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it greatly relies on visual examination. Hence, there is a need to develop a more sensitive and specific method for diagnosing cancer. Here, Fourier transform infrared (FTIR) spectroscopy of thyroid tumors (n = 164; 76 malignant, 88 benign) was performed and five (5) neural network (NN) models were designed to discriminate the obtained spectral data. PCA-LDA was used as classical benchmark for comparison. Each NN model was evaluated using a stratified 10-fold cross-validation method to avoid overfitting, and the performance metrics-accuracy, area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NN models were able to perform excellently as classifiers, and all were able to surpass the LDA model in terms of accuracy. Among the NN models, the RNN model performed best, having an AUC of 95.29% ± 6.08%, an accuracy of 98.06% ± 2.87%, a PPV of 98.57% ± 4.52%, a NPV of 93.18% ± 7.93%, a SR value of 98.89% ± 3.51%, and a RR value of 91.25% ± 10.29%. The RNN model outperformed the LDA model for all metrics except for the AUC, NPV, and RR. In conclusion, NN-based tools were able to predict thyroid cancer based on infrared spectroscopy of tissues with a high level of diagnostic performance in comparison to the gold standard.

A Comparison between FTIR Spectra from HUKE and SH-SY5Y Cell Lines Grown on Different Substrates

In recent years, Fourier Transform Infrared (FTIR) micro-spectroscopy has shown promising potential in medical diagnostics at the cellular level. In fact, FTIR spectra can provide information related to DNA, protein, and lipid content and how such a content changes when a pathological state arises. Most of these information is included in the so-called fingerprint region (1000–1800 cm−1), consisting of several spectral peaks related to vibrational modes occurring inside cellular components. Unfortunately, the slides commonly used in cytology (as the glass microscopy slides and coverslips) are opaque to IR radiation in the fingerprint region, whereas they are transparent for wavenumber values larger than 2000 cm−1, where few and broad spectral absorption bands, mainly due to lipids and proteins, are present. Nonetheless, here we show that FTIR spectra performed in the high wavenumber range 2750–3000 cm−1 can be used to discriminate two different types of cells, one from a normal cell line (Human Keratinocyte, HUKE) and the other from a cancer one (SH-SY5Y). The spectra are discriminated by means of their Principal Component Analysis, according to the PC1 component, and by means of ratiometric analysis, according to the ratio of the intensity of the peak at 2956 cm−1 and that of the peak at 2924 cm−1. The PC1 score values of the HUKE are statistically different from the PC1 score values of SH-SY5Y, whereas the intensity ratio results larger for SH-SY5Y than for HUKE cells. Such results occur for different substrates over which the cells have been grown, including the thick glass slides used for cytological analysis. This result is a further step toward the application of FTIR microspectroscopy in the cytological routine diagnosis.

An FTIR Microspectroscopy Ratiometric Approach for Monitoring X-ray Irradiation Effects on SH-SY5Y Human Neuroblastoma Cells

The ability of Fourier transform infrared (FTIR) spectroscopy in analyzing cells at a molecular level was exploited for investigating the biochemical changes induced in protein, nucleic acid, lipid, and carbohydrate content of cells after irradiation by graded X-ray doses. Infrared spectra from in vitro SH-SY5Y neuroblastoma cells following exposure to X-rays (0, 2, 4, 6, 8, 10 Gy) were analyzed using a ratiometric approach by evaluating the ratios between the absorbance of significant peaks. The spectroscopic investigation was performed on cells fixed immediately (t0 cells) and 24 h (t24 cells) after irradiation to study both the initial radiation-induced damage and the effect of the ensuing cellular repair processes. The analysis of infrared spectra allowed us to detect changes in proteins, lipids, and nucleic acids attributable to X-ray exposure. The ratiometric analysis was able to quantify changes for the protein, lipid, and DNA components and to suggest the occurrence of apoptosis processes. The ratiometric study of Amide I band indicated also that the secondary structure of proteins was significantly modified. The comparison between the results from t0 and t24 cells indicated the occurrence of cellular recovery processes. The adopted approach can provide a very direct way to monitor changes for specific cellular components and can represent a valuable tool for developing innovative strategies to monitor cancer radiotherapy outcome.

FTIR, Raman and AFM characterization of the clinically valid biochemical parameters of the thrombi in acute ischemic stroke

The significance and utility of innovative imaging techniques in arterial clot analysis, which enable far more detailed and automated analysis compared to standard methods, are presented. The examination of two types of human thrombi is shown, representing the main ischemic stroke etiologies: fibrin–predominant clot of large vessel origin and red blood cells–rich clot of cardioembolic origin. The synergy effect of Fourier–transform infrared spectroscopy (FTIR), Raman spectroscopy (RS) and atomic force microscopy (AFM) techniques supported by chemometrics in comparison with reference histological staining was presented. The main advantage of such approach refers to free–label and non–destructive quantitative imaging of clinically valid, biochemical parameters in whole sample (FTIR–low resolution) and selected regions (RS–ultra–high resolution). We may include here analysis of lipid content, its distribution and total degree of unsaturation as well as analysis of protein content (mainly fibrin and hemoproteins). The AFM studies enhanced the vibrational data, showed clearly shape and thickness of clot features as well as visualized the fibrin framework. The extraordinary sensitivity of FTIR and RS imaging toward detection and discrimination of clinically valid parameters in clot confirms its applicability in assessment of thrombi origin.

On the characteristic and stability of iron diet supplements

The iron diet supplements: AproFER 1000 and AproTHEM were subjected to various chemical, microbial and magnetic analysis. The microbial analysis revealed no presence of pathogenic bacteria in the studied products. No significant changes in iron content or forms (bivalent/trivalent) were observed in EPR analysis of supplements stored at different conditions for a long period of time. The chemical and magnetic analysis showed that both AproFER 1000 and AproTHEM contain a high concentration of bivalent iron so they can be used as an iron diet supplements.

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning

Background: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing sarcoma—the second most common paediatric malignant bone tumour. Fourier transform infrared (FTIR) spectroscopy of tissues allows the bulk biochemical content of a biological sample to be probed and makes possible the study and diagnosis of disease. Methods: In this retrospective study, FTIR spectra of sections of biopsy-obtained bone tissue were recorded. Twenty-seven patients (between 5 and 20 years of age) with newly diagnosed Ewing sarcoma of bone were included in this study. The prognostic value of FTIR spectra obtained from Ewing sarcoma (ES) tumours before and after neoadjuvant chemotherapy were analysed in combination with various data-reduction and machine learning approaches. Results: Random forest and linear discriminant analysis supervised learning models were able to correctly predict patient mortality in 92% of cases using leave-one-out cross-validation. The best performing model for predicting patient relapse was a linear Support Vector Machine trained on the observed spectral changes as a result of chemotherapy treatment, which achieved 92% accuracy. Conclusion: FTIR spectra of tumour biopsy samples may predict treatment outcome in paediatric Ewing sarcoma patients with greater than 92% accuracy.

Distinguishing Ewing sarcoma and osteomyelitis using FTIR spectroscopy

The differential diagnosis of Ewing sarcoma and osteomyelitis can be challenging and can lead to delays in treatment with possibly devastating results. In this retrospective, small-cohort study we demonstrate, that the Fourier Transformed Infrared (FTIR) spectra of osteomyelitis bone tissue can be differentiated from Ewing sarcoma and normal bone tissue sampled outside tumour area. Significant differences in osteomyelitis samples can be seen in lipid and protein composition. Supervised learning using a quadratic discriminant analysis classifier was able to differentiate the osteomyelitis samples with high accuracy. FTIR spectroscopy, alongside routine radiological and histopathological methods, may offer an additional tool for the differential diagnosis of osteomyelitis and ES.

Prediction of Ewing Sarcoma treatment outcome using attenuated tissue reflection FTIR tissue spectroscopy

Ewing sarcoma is the second most common type of primary bone cancer and predominantly affects children and young people. Improved outcome prediction is key to delivering risk-adjusted, appropriate and effective care to cancer patients. Advances in the Fourier Transform Infrared (FTIR) spectroscopy of tissues enable it to be a non-invasive method to obtain information about the biochemical content of any biological sample. In this retrospective study, attenuated tissue reflection FTIR spectroscopy of biopsy samples from paediatric patients reveals spectral features that are diagnostic for Ewing Sarcoma. Furthermore, our results suggest that spectral features such as these may be of value for the prediction of treatment outcome independent to well-known, routinely used risk factors.

A Plasma Biochemical Analysis of Acute Lead Poisoning in a Rat Model by Chemometrics-Based Fourier Transform Infrared Spectroscopy: An Exploratory Study

In this work, we explored to use chemometrics-based Fourier transform infrared (FTIR) spectroscopy to investigate the plasma biochemical changes due to acute lead poisoning (ALP) in a rat model. We first collected the FTIR spectra of the plasma samples from the rats with and without suffering from ALP. We then performed the chemometric analysis of these FTIR spectra using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). We found that the chemometrics-based FTIR spectroscopy can discriminate the rats with and without ALP. Further analysis on the PLS-DA regression coefficient revealed that the spectral changes, in particular, corresponding to the biochemical changes of proteins in the plasma may be used as potential spectral biomarkers for the diagnostics of lead poisoning. Our work demonstrates the potential of chemometrics-based FTIR spectroscopy as a promising tool for the biochemical analysis of plasma that could consequently enable an objective, convenient and non-destructive diagnostics of lead poisoning. To the best of our knowledge, this work is the first application of chemometrics-based FTIR spectroscopy in the diagnostics of lead poisoning.