Attenuated total reflection fourier transform infrared (ATR-FTIR) spectral discrimination of brain tumour severity from serum samples

Combining spectrum and machine learning algorithms to predict the weathering time of empty puparia of Sarcophaga peregrine (Diptera: Sarcophagidae)

The weathering time of empty puparia could be important in predicting the minimum postmortem interval (PMImin). As corpse decomposition progresses to the skeletal stage, empty puparia often remain the sole evidence of fly activity at the scene. In this study, we used empty puparia of Sarcophaga peregrina (Diptera: Sarcophagidae) collected at ten different time points between January 2019 and February 2023 as our samples. Initially, we used the scanning electron microscope (SEM) to observe the surface of the empty puparia, but it was challenging to identify significant markers to estimate weathering time. We then utilized attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to detect the puparia spectrogram. Absorption peaks were observed at 1064 cm-1, 1236 cm-1, 1381 cm-1, 1538 cm-1, 1636 cm-1, 2852 cm-1, 2920 cm-1. Three machine learning models were used to regress the spectral data after dimensionality reduction using principal component analysis (PCA). Among them, eXtreme Gradient Boosting regression (XGBR) showed the best performance in the wavenumber range of 1800-600 cm-1, with a mean absolute error (MAE) of 1.20. This study highlights the value of refining these techniques for forensic applications involving entomological specimens and underscores the considerable potential of combining FTIR and machine learning in forensic practice.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

BACKGROUND

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

METHODS

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

RESULTS

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

CONCLUSION

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Differential diagnosis of Crohn's disease and intestinal tuberculosis based on ATR-FTIR spectroscopy combined with machine learning

BACKGROUND

Crohn's disease (CD) is often misdiagnosed as intestinal tuberculosis (ITB). However, the treatment and prognosis of these two diseases are dramatically different. Therefore, it is important to develop a method to identify CD and ITB with high accuracy, specificity, and speed.

AIM

To develop a method to identify CD and ITB with high accuracy, specificity, and speed.

METHODS

A total of 72 paraffin wax-embedded tissue sections were pathologically and clinically diagnosed as CD or ITB. Paraffin wax-embedded tissue sections were attached to a metal coating and measured using attenuated total reflectance fourier transform infrared spectroscopy at mid-infrared wavelengths combined with XGBoost for differential diagnosis.

RESULTS

The results showed that the paraffin wax-embedded specimens of CD and ITB were significantly different in their spectral signals at 1074 cm-1 and 1234 cm-1 bands, and the differential diagnosis model based on spectral characteristics combined with machine learning showed accuracy, specificity, and sensitivity of 91.84%, 92.59%, and 90.90%, respectively, for the differential diagnosis of CD and ITB.

CONCLUSION

Information on the mid-infrared region can reveal the different histological components of CD and ITB at the molecular level, and spectral analysis combined with machine learning to establish a diagnostic model is expected to become a new method for the differential diagnosis of CD and ITB.

Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation

BACKGROUND

Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation.

METHODS

Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations.

RESULTS

The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas.

CONCLUSIONS

The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Advancements in Mid-Infrared spectroscopy of extracellular vesicles

Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.

Fiber optic probe-based ATR-FTIR spectroscopy for rapid breast cancer detection: A pilot study

Breast cancer diagnosis is crucial for timely treatment and improved outcomes. This paper proposes a novel approach for rapid breast cancer diagnosis using optical fiber probe-based attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy from 750 to 4000 cm-1 . The technique enables direct analysis of tissue samples, eliminating the need for microtome sectioning and staining, thus saving time and resources. By capturing molecular fingerprint information, various machine-learning models were used to analyze the spectroscopic data to classify cancerous and non-cancerous tissues accurately. Comparing deparaffinized and paraffinized samples reveals the impact of sample preparation and experimental methods. The study demonstrates a strong correlation between the cancerous nature of a sample and its ATR-FTIR spectrum, suggesting its potential for breast cancer diagnosis (sensitivity of 74.2% and specificity of 78.3%). The proposed approach holds promise for integration into clinical operations, providing a rapid method for preliminary breast cancer diagnosis.

Application of Fourier Transform InfraRed spectroscopy of machine learning with Support Vector Machine and principal components analysis to detect biochemical changes in dried serum of patients with primary myelofibrosis

Primary myelofibrosis (PM) is a myeloproliferative neoplasm characterized by stem cell-derived clonal neoplasms. Several factors are involved in diagnosing PM, including physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. Commonly gene mutations are used. Also, these gene mutations exist in other diseases, such as polycythemia vera and essential thrombocythemia. Hence, understanding the molecular mechanism and finding disease-related biomarker characteristics only for PM is crucial for the treatment and survival rate. For this purpose, blood samples of PM (n = 85) vs. healthy controls (n = 45) were collected for biochemical analysis, and, for the first time, Fourier Transform InfraRed (FTIR) spectroscopy measurement of dried PM and healthy patients' blood serum was analyzed. A Support Vector Machine (SVM) model with optimized hyperparameters was constructed using the grid search (GS) method. Then, the FTIR spectra of the biomolecular components of blood serum from PM patients were compared to those from healthy individuals using Principal Components Analysis (PCA). Also, an analysis of the rate of change of FTIR spectra absorption was studied. The results showed that PM patients have higher amounts of phospholipids and proteins and a lower amount of H-O=H vibrations which was visible. The PCA results indicated that it is possible to differentiate between dried blood serum samples collected from PM patients and healthy individuals. The Grid Search Support Vector Machine (GS-SVM) model showed that the prediction accuracy ranged from 0.923 to 1.00 depending on the FTIR range analyzed. Furthermore, it was shown that the ratio between α-helix and β-sheet structures in proteins is 1.5 times higher in PM than in control people. The vibrations associated with the CO bond and the amide III region of proteins showed the highest probability value, indicating that these spectral features were significantly altered in PM patients compared to healthy ones' spectra. The results indicate that the FTIR spectroscope may be used as a technique helpful in PM diagnostics. The study also presents preliminary results from the first prospective clinical validation study.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

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.

A Novel Approach for Therapeutic Drug Monitoring of Valproic Acid Using FT-IR Spectroscopy and Nonlinear Support Vector Regression

BACKGROUND

Recent technological progress has bolstered efforts to bring personalized medicine from theory into clinical practice. However, progress in areas such as therapeutic drug monitoring (TDM) has remained somewhat stagnant. In drugs with well-known dose-response relationships, TDM can enhance patient outcomes and reduce health care costs. Traditional monitoring methods such as chromatography-based or immunoassay techniques are limited by their higher costs and slow turnaround times, making them unsuitable for real-time or onsite analysis.

OBJECTIVE

In this work, we propose the use of a fast, direct, and simple approach using Fourier transform infrared spectroscopy (FT-IR) combined with chemometric techniques for the therapeutic monitoring of valproic acid (VPA).

METHOD

In this context, a database of FT-IR spectra was constructed from human plasma samples containing various concentrations of VPA; these samples were characterized by the reference method (immunoassay technique) to determine the VPA contents. The FT-IR spectra were processed by two chemometric regression methods: partial least-squares regression (PLS) and support vector regression (SVR).

RESULTS

The results provide good evidence for the effectiveness of the combination of FT-IR spectroscopy and SVR modeling for estimating VPA in human plasma. SVR models showed better predictive abilities than PLS models in terms of root-mean-square error of calibration and prediction RMSEC, RMSEP, R2Cal, R2Pred, and residual predictive deviation (RPD).

CONCLUSIONS

This analytical tool offers potential for real-time TDM in the clinical setting.

HIGHLIGHTS

FTIR spectroscopy was evaluated for the first time to predict VPA in human plasma for TDM. Two regressions were evaluated to predict VPA in human plasma, and the best-performing model was obtained using nonlinear SVR.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Raman spectroscopy-based biomarker screening by studying the fingerprint and lipid characteristics of Polycythemıa Vera cases blood serum

This study aimed to develop a novel approach for diagnosing Polycythemia Vera (PV), a stem cell-derived neoplasm of the myeloid lineage. The approach utilized Raman spectroscopy coupled with multivariate analysis to analyze blood serum samples collected from PV patients. The results showed that PV serum exhibited lower protein and lipid levels and structural changes in the functional groups that comprise proteins and lipids. The study also demonstrated differences in lipid biosynthesis and protein levels in PV serum. Using the Partial Least Square Discriminant Analysis (PLS-DA) model, Raman-based multivariate analysis achieved high accuracy rates of 96.49% and 93.04% in the training sets and 93.10% and 89.66% in the test sets for the 800-1800 cm-1 and 2700-3000 cm-1 ranges, respectively. The area under the curve (AUC) values of the test datasets were calculated as 0.92 and 0.89 in the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively, demonstrating the effectiveness of the PLS-DA models for the diagnosis of PV. This study highlights the potential of Raman spectroscopy-based analysis in the early and accurate diagnosis of PV, enabling the application of effective treatment strategies.

Infrared Spectroscopy as a Potential Diagnostic Tool for Medulloblastoma

INTRODUCTION

Medulloblastoma (MB) is the most common malignant tumor of the central nervous system in childhood. FTIR spectroscopy provides a holistic view of the chemical composition of biological samples, including the detection of molecules such as nucleic acids, proteins, and lipids. This study evaluated the applicability of FTIR spectroscopy as a potential diagnostic tool for MB.

MATERIALS AND METHODS

FTIR spectra of MB samples from 40 children (boys/girls: 31/9; age: median 7.8 years, range 1.5-21.5 years) treated in the Oncology Department of the Children's Memorial Health Institute in Warsaw between 2010 and 2019 were analyzed. The control group consisted of normal brain tissue taken from four children diagnosed with causes other than cancer. Formalin-fixed and paraffin-embedded tissues were sectioned and used for FTIR spectroscopic analysis. The sections were examined in the mid-infrared range (800-3500 cm^-1) by ATR-FTIR. Spectra were analysed using a combination of principal component analysis, hierarchical cluster analysis, and absorbance dynamics.

RESULTS

FTIR spectra in MB were significantly different from those of normal brain tissue. The most significant differences related to the range of nucleic acids and proteins in the region 800-1800 cm^-1. Some major differences were also revealed in the quantification of protein conformations (α-helices, β-sheets, and others) in the amide I band, as well as in the absorbance dynamics in the 1714-1716 cm^-1 range (nucleic acids). It was not, however, possible to clearly distinguish between the various histological subtypes of MB using FTIR spectroscopy.

CONCLUSIONS

MB and normal brain tissue can be distinguished from one another to some extent using FTIR spectroscopy. As a result, it may be used as a further tool to hasten and enhance histological diagnosis.

Investigating centrifugal filtration of serum-based FTIR spectroscopy for the stratification of brain tumours

Discrimination of brain cancer versus non-cancer patients using serum-based attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy diagnostics was first developed by Hands et al with a reported sensitivity of 92.8% and specificity of 91.5%. Cameron et al. then went on to stratifying between specific brain tumour types: glioblastoma multiforme (GBM) vs. primary cerebral lymphoma with a sensitivity of 90.1% and specificity of 86.3%. Expanding on these studies, 30 GBM, 30 lymphoma and 30 non-cancer patients were selected to investigate the influence on test performance by focusing on specific molecular weight regions of the patient serum. Membrane filters with molecular weight cut offs of 100 kDa, 50 kDa, 30 kDa, 10 kDa and 3 kDa were purchased in order to remove the most abundant high molecular weight components. Three groups were classified using both partial least squares-discriminate analysis (PLS-DA) and random forest (RF) machine learning algorithms; GBM versus non-cancer, lymphoma versus non-cancer and GBM versus lymphoma. For all groups, once the serum was filtered the sensitivity, specificity and overall balanced accuracies decreased. This illustrates that the high molecular weight components are required for discrimination between cancer and non-cancer as well as between tumour types. From a clinical application point of view, this is preferable as less sample preparation is required.

Recurrent neural networks for time domain modelling of FTIR spectra: application to brain tumour detection

A recurrent neural network trained on time domain data can accurately identify brain tumours from serum spectral data.

Urinalysis of individuals with renal hyperfiltration using ATR-FTIR spectroscopy

Abnormal increased glomerular filtration rate (GFR), otherwise known as renal hyperfiltration (RHf), is associated with an increased risk of chronic kidney disease and cardiovascular mortality. Although it is not considered as a disease alone in medicine today, early detection of RHf is essential to reducing risk in a timely manner. However, detecting RHf is a challenge since it does not have a practical biochemical marker that can be followed or quantified. In this study, we tested the ability of ATR-FTIR spectroscopy to distinguish 17 individuals with RHf (hyperfiltraters; RHf (+)), from 20 who have normal GFR (normofiltraters; RHf(-)), using urine samples. Spectra collected from hyperfiltraters were significantly different from the control group at positions 1621, 1390, 1346, 933 and 783/cm. Intensity changes at these positions could be followed directly from the absorbance spectra without the need for pre-processing. They were tentatively attributed to urea, citrate, creatinine, phosphate groups, and uric acid, respectively. Using principal component analysis (PCA), major peaks of the second derivative forms for the classification of two groups were determined. Peaks at 1540, 1492, 1390, 1200, 1000 and 840/cm were significantly different between the two groups. Statistical analysis showed that the spectra of normofiltraters are similar; however, those of hyperfiltraters show diversity at multiple positions that can be observed both from the absorbance spectra and the second derivative profiles. This observation implies that RHf can simultaneously affect the excretion of many substances, and that a spectroscopic analysis of urine can be used as a rapid and non-invasive pre-screening tool.

Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma

Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF₂) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.

One-Drop Serum Screening Test for Anal Cancer in Men via Infrared Attenuated Total Reflection Spectroscopy

Rare cancers are a challenge for clinical practice, the treatment experience at major centers to which rare cancers are referred is limited and are the most difficult to diagnose. Research to identify causes or develop prevention and early detection strategies is extremely challenging. Anal cancer is an example of a rare cancer, with the human papillomavirus (HPV) infection being the most important risk factor associated. In the early stages, anal cancer does not exhibit evident symptoms. This disease is diagnosed by means of anoscopy, which diagnoses some cases of early cancer; nevertheless, sensitivity of this test ranges between 47 and 89%. Therefore, the development of new, effective, and evidence-based screening methodologies for the early detection of rare cancers is of great relevance. In this study, the potential of ATR-FTIR spectroscopy has been explored as a sensitive, nondestructive, and inexpensive analytical method for developing disease screening platforms in serum. Spectral differences were found in the regions of 1700-1100 and 1700-1400 cm^-1 between the control group and the anal cancer group related to the presence of proteins and nucleic acids. The chemometric analysis presented differences in the spectral fingerprints for both spectral regions with a high sensitivity ranging from 95.2 to 99.9% and a specificity ranging from 99.2 to 100%. This is the first step that we report for a methodology that is fast, nondestructive, and easy to perform, and the high sensitivity and specificity of the method are the basis for extensive research studies to implement these technologies in the clinical field.

Metabolomics Profiling of Pituitary Adenomas by Raman Spectroscopy, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy, and Mass Spectrometry of Serum Samples

To date, no studies are available in which pituitary adenomas (PAs) have been studied using techniques like confocal Raman spectroscopy, attenuated total reflection-Fourier transform infrared (FT-IR), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the same serum samples. To understand the metabolomics fingerprint, Raman spectra of 16 acromegaly, 19 Cushing's, and 33 nonfunctional PA (NFPA) and ATR-FTIR spectral acquisition of 16 acromegaly, 18 Cushing's, and 22 NFPA patient's serum samples were acquired. Next, Principal component-based linear discriminant analysis (PC-LDA) models were developed, Raman spectral analysis classified acromegaly with an accuracy of 79.17%, sensitivity of 75%, and specificity of 81.25%, Cushing's with an accuracy of 66.67%, sensitivity of 100%, and specificity of 52.63%, and NFPA with an accuracy of 73.17%, sensitivity of 75%, and specificity of 72.73%. ATR-FTIR spectral analysis classified acromegaly with an accuracy of 95.83%, sensitivity of 100%, and specificity of 93.75%, Cushing's with an accuracy of 65.38%, sensitivity of 87.5%, and specificity of 55.56%, and NFPA with an accuracy of 70%, sensitivity of 87.5%, and specificity of 43.75%. In either of the cases, healthy individual cohorts were clearly segregated from the disease cohort, which identified differential regulated regions of nucleic acids, lipids, amides, phosphates, and polysaccharide/C-C residue α helix regions. Furthermore, LC-MS/MS-based analysis of sera samples resulted in the identification of various sphingosine, lipids, acylcarnitines, amino acids, ethanolamine, choline, and their derivatives that differentially regulated in each tumor cohort. We believe cues obtained from the study may be used to generate the metabolite-based test to diagnose PAs from serum in addition to conventional techniques and also to understand disease biology for better disease management, point of care, and improving quality of life in PA patients.

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.

Liquid Biopsy for Pancreatic Cancer Detection Using Infrared Spectroscopy

Pancreatic cancer claims over 460,000 victims per year. The carbohydrate antigen (CA) 19-9 test is the blood test used for pancreatic cancer's detection; however, its levels can be raised in symptomatic patients with other non-malignant diseases, or with other tumors in the surrounding area. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has demonstrated exceptional potential in cancer diagnostics, and its clinical implementation could represent a significant step towards early detection. This proof-of-concept study, investigating the use of ATR-FTIR spectroscopy on dried blood serum, focused on the discrimination of both cancer versus healthy control samples, and cancer versus symptomatic non-malignant control samples, as a novel liquid biopsy approach for pancreatic cancer diagnosis. Machine learning algorithms were applied, achieving results of up to 92% sensitivity and 88% specificity when discriminating between cancers (n = 100) and healthy controls (n = 100). An area under the curve (AUC) of 0.95 was obtained through receiver operating characteristic (ROC) analysis. Balanced sensitivity and specificity over 75%, with an AUC of 0.83, were achieved with cancers (n = 35) versus symptomatic controls (n = 35). Herein, we present these results as demonstration that our liquid biopsy approach could become a simple, minimally invasive, and reliable diagnostic test for pancreatic cancer detection.

ATR-IR Spectroscopy Application to Diagnostic Screening of Advanced Endometriosis

Endometriosis is one of the most common gynecological diseases among young women of reproductive age. Thus far, it has not been possible to define a parameter that is sensitive and specific enough to be a recognized biomarker for diagnosing this disease. Nonspecific symptoms of endometriosis and delayed diagnosis are impulses for researching noninvasive methods of differentiating endometriosis from other gynecological disorders. We compared three groups of individuals in our research: women with endometriosis (E), patients suffering from other gynecological disorders (nonendometriosis, NE), and healthy women from the control group (C). Partial least squares discriminant analysis (PLS-DA) models were developed based on selected serum biochemical parameters, specific regions of the serum’s infrared attenuated total reflectance (FTIR ATR) spectra, and combined data. Incorporating the spectral data into the models significantly improved differentiation among the three groups, with an overall accuracy of 87.5%, 97.3%, and 98.5%, respectively. This study shows that infrared spectroscopy and discriminant analysis can be used to differentiate serum samples among women with advanced endometriosis, women without this disease, i.e., healthy women, and, most importantly, also women with other benign gynecological disorders.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm^-1 , 1539 cm^-1 and 1234 cm^-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.

Disposable Coverslip for Rapid Throughput Screening of Malaria Using Attenuated Total Reflection Spectroscopy

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, nonperturbative, and nondestructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of attenuated total reflection spectroscopy is promising as a point-of-care diagnostic tool in resource-limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with partial least squares regression, attenuated total reflection spectroscopy was able to detect and quantify the parasitemia with root mean square error of cross-validation and R² values of 0.177 and 0.985, respectively. Here, we characterize an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the internal reflection element.

Rapid Discrimination of Neuromyelitis Optica Spectrum Disorder and Multiple Sclerosis Using Machine Learning on Infrared Spectra of Sera

Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient’s serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients’ treatment.

Clinical validation of a spectroscopic liquid biopsy for earlier detection of brain cancer

Background

Diagnostic delays impact the quality of life and survival of patients with brain tumors. Earlier and expeditious diagnoses in these patients are crucial to reducing the morbidities and mortalities associated with brain tumors. A simple, rapid blood test that can be administered easily in a primary care setting to efficiently identify symptomatic patients who are most likely to have a brain tumor would enable quicker referral to brain imaging for those who need it most.

Methods

Blood serum samples from 603 patients were prospectively collected and analyzed. Patients either had non-specific symptoms that could be indicative of a brain tumor on presentation to the Emergency Department, or a new brain tumor diagnosis and referral to the neurosurgical unit, NHS Lothian, Scotland. Patient blood serum samples were analyzed using the Dxcover®Brain Cancer liquid biopsy. This technology utilizes infrared spectroscopy combined with a diagnostic algorithm to predict the presence of intracranial disease.

Results

Our liquid biopsy approach reported an area under the receiver operating characteristic curve of 0.8. The sensitivity-tuned model achieves a 96% sensitivity with 45% specificity (NPV 99.3%) and identified 100% of glioblastoma multiforme patients. When tuned for a higher specificity, the model yields sensitivity of 47% with 90% specificity (PPV 28.4%).

Conclusions

This simple, non-invasive blood test facilitates the triage and radiographic diagnosis of brain tumor patients, while providing reassurance to healthy patients. Minimizing time to diagnosis would facilitate identification of brain tumor patients at an earlier stage, enabling more effective, less morbid surgical and adjuvant care.

Assessment of Measurement of Salivary Urea by ATR-FTIR Spectroscopy to Screen for CKD

Stages of CKD are currently defined by eGFR and require measurement of serum creatinine concentrations. Previous studies have shown a good correlation between salivary and serum urea levels and the stage of CKD. However, quantitative salivary urea assays in current clinical use require costly and labor-intensive commercial kits, which restricts the advantage of using saliva and limits wider applicability as a quick and easy means of assessing renal function. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy has been shown to provide a potentially straightforward, reagent-free method for the identification of a range of disease-related biomarkers and is in current clinical use for analyses of the chemical composition of kidney stones. We assessed the feasibility of ATR-FTIR spectroscopy as an alternative method to measure salivary urea in patients with different stages of CKD. The ATR-FTIR spectra of dried saliva samples from six healthy controls and 20 patients with CKD (stages 1-5) were analyzed to provide their urea concentrations. The lower limit of detection of salivary urea by the ATR-FTIR spectroscopy method was 1-2 mM, at the lower end of the clinically relevant range. Statistically significant differences in salivary urea concentrations were demonstrated between healthy subjects (4.1±0.5 mM) and patients with CKD stages 3-5 (CKD stage 3, 6.8±0.7 mM; CKD stage 4, 9.1±1 mM; CKD stage 5, 14.8±1.6 mM). These salivary urea concentrations correlated well with serum urea levels in the same patients measured by an automated analyzer (Spearman rank correlation coefficient of 0.71; P<0.001). The ability of the method to detect and stage CKD was assessed from the sensitivity and specificity parameters of a receiver operating characteristics (ROC) curve analysis. This proof-of-concept study demonstrates that quantitation of salivary urea by ATR-FTIR spectroscopy could provide a viable tool for rapid and cost-effective diagnosis of stages 3-5 CKD.

Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer

Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78-0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Detection of ovarian cancer (± neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort

Ovarian cancer remains the most lethal gynaecological malignancy, as its timely detection at early stages remains elusive. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy of biofluids has been previously applied in pilot studies for ovarian cancer diagnosis, with promising results. Herein, these initial findings were further investigated by application of ATR-FTIR spectroscopy in a large patient cohort. Spectra were obtained by measurements of blood plasma and serum, as well as urine, from 116 patients with ovarian cancer and 307 patients with benign gynaecological conditions. A preliminary chemometric analysis revealed significant spectral differences in ovarian cancer patients without previous chemotherapy (n = 71) and those who had received neo-adjuvant chemotherapy-NACT (n = 45), so these groups were compared separately with benign controls. Classification algorithms with blind predictive model validation demonstrated that serum was the best biofluid, achieving 76% sensitivity and 98% specificity for ovarian cancer detection, whereas urine exhibited poor performance. A drop in sensitivities for the NACT ovarian cancer group in plasma and serum indicates the potential of ATR-FTIR spectroscopy to identify chemotherapy-related spectral changes. Comparisons of regression coefficient plots for identification of biomarkers suggest that glycoproteins (such as CA125) are the main classifiers for ovarian cancer detection and responsible for smaller differences in spectra between NACT patients and benign controls. This study confirms the capacity of biofluids' ATR-FTIR spectroscopy (mainly blood serum) to diagnose ovarian cancer with high accuracy and demonstrates its potential in monitoring response to chemotherapy, which is reported for the first time. ATR-FTIR spectroscopy of blood serum achieves good segregation of ovarian cancers from benign controls, with attenuation of differences following neo-adjuvant chemotherapy.

Spectroscopy as a useful tool for the identification of changes with time in post-mortem vitreous humor for forensic toxicology purposes

Vitreous humor (VH) is an alternative biological matrix with a great advantage of longer availability for analysis due to the lack of many enzymes. The use of VH in forensic toxicology may have an added benefit, however, this application requires rapid, simple, non-destructive, and relatively portable analytical analysis methods. These requirements may be met by Fourier transform infrared spectroscopy technique (FT-IR) equipped with attenuated total reflection accessory (ATR). FT-IR spectra of vitreous humor samples, deposited on glass slides, were collected and subsequent chemometric data analysis by means of Hierarchical Cluster Analysis and Principal Component Analysis was conducted. Differences between animal and human VH samples and human VH samples stored for diverse periods of time were detected. A kinetic study of changes in the VH composition up to 2 weeks showed the distinction of FT-IR spectra collected on the 1st and 14th day of storage. In addition, data obtained for the most recent human vitreous humor samples—collected 3 and 2 years before the study, presented successful discrimination of all time points studied. The method introduced was unable to detect mephedrone addition to VH in the concentration of 10 µg/cm3.

Graphic abstract

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Potential of FTIR Spectroscopy Applied to Exosomes for Alzheimer's Disease Discrimination: A Pilot Study

Alzheimer's disease (AD) diagnosis is based on psychological and imaging tests but can also include monitoring cerebrospinal fluid (CSF) biomarkers. However, CSF based-neurochemical approaches are expensive and invasive, limiting their use to well-equipped settings. In contrast, blood-based biomarkers are minimally invasive, cost-effective, and a widely accessible alternative. Blood-derived exosomes have recently emerged as a reliable AD biomarker source, carrying disease-specific cargo. Fourier-transformed infrared (FTIR) spectroscopy meets the criteria for an ideal diagnostic methodology since it is rapid, easy to implement, and has high reproducibility. This metabolome-based technique is useful for diagnosing a broad range of diseases, although to our knowledge, no reports for FTIR spectroscopy applied to exosomes in AD exist. In this ground-breaking pilot study, FTIR spectra of serum and serum-derived exosomes from two independent cohorts were acquired and analyzed using multivariate analysis. The regional UA-cohort includes 9 individuals, clinically diagnosed with AD, mean age of 78.7 years old; and the UMG-cohort comprises 12 individuals, clinically diagnosed with AD (based on molecular and/or imaging data), mean age of 73.2 years old. Unsupervised principal component analysis of FTIR spectra of serum-derived exosomes revealed higher discriminatory value for AD cases when compared to serum as a whole. Consistently, the partial least-squares analysis revealed that serum-derived exosomes present higher correlations than serum. In addition, the second derivative peak area calculation also revealed significant differences among Controls and AD cases. The results obtained suggest that this methodology can discriminate cases from Controls and thus be potential useful to assist in AD clinical diagnosis.

Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas

Glioma has a low cure rate and a high mortality rate. Therefore, correct diagnosis and treatment are essential for patients. This research aims to use mid-infrared spectroscopy combined with machine learning algorithms to identify patients with glioma. The glioma infrared spectra and the control group serum are smoothed and normalized, then the principal component analysis (PCA) algorithm is used to reduce the data dimensionality, and finally, the particle swarm optimization-support vector machine (PSO-SVM), backpropagation (BP) neural network and decision tree (DT) model are established. The classification accuracy of the three models was 92.00 %, 91.83 %, 87.20 %, and the AUC values were 0.919, 0.945, and 0.866, respectively. The results show that PCA-PSO-SVM has a better classification effect. This study shows that mid-infrared spectroscopy combined with machine learning algorithms has great potential in the application of non-invasive, rapid and accurate identification of glioma patients.

Assessment of structural protein expression by FTIR and biochemical assays as biomarkers of metabolites response in gastric and colon cancer

Colon and gastric cancers are the widespread benign types of cancers which are synchronous and metachronous neoplasms. In terms of the progression and progress of the disease, metabolic processes and differentiation in protein structures have an important role in for treatment of the disease. In this study we proposed to investigate the metabolic process and the differentiation of protein secondary structure among colon and gastric cancer as well as healthy controls using biochemistry and Fourier Transform InfraRed spectroscopy (FTIR) methods. For this purpose, we measured blood serum of 133 patients, which were conducted upon oncology department (45 colon cancer, 45 gastric cancer and 43 control individuals). The obtained spectroscopic results and biochemical assays showed significant reduction in the amount of functional groups in cancer groups contrary with total protein measurements and structure of protein differences between colon and gastric cancers. Differentiations were visible in serum levels of CEA, CA-125, CA-15-3, CA-19-9 AFP (Alpha fetoprotein) of gastric and colon cancer patients as well as in amide III and secondly described amide I regions. Our findings suggest that amide I bonds in colon cancer cells can be helpful in diagnosis of colon cancer. Indeed, our results showed that metabolic processes were higher in gastric cancer group than in colon cancer. Hence, FTIR spectroscopy and curve-fitting analysis of amide I profile can be successfully applied as tools for identifying quantitative and qualitative changes of proteins in human cancerous blood serum. However, what is very important, in PCA analysis we see, that the scatter plot of PC1 (variability 80%) and PC2 (variability 15%) show that the data related to the control and two cancer groups are clustered together with different magnitudes and directions.

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient's plasma and serum sample using vibrational spectroscopy.

Tailoring functional nanostructured lipid carriers for glioblastoma treatment with enhanced permeability through in-vitro 3D BBB/BBTB models

The blood-brain barrier (BBB) and blood-brain tumour barrier (BBTB) pose a significant challenge to drug delivery to brain tumours, including aggressive glioblastoma (GB). The present study rationally designed functional nanostructured lipid carriers (NLC) to tailor their BBB penetrating properties with high encapsulation of CNS negative chemotherapeutic drug docetaxel (DTX). We investigated the effect of four liquid lipids, propylene glycol monolaurate (Lauroglycol® 90), Capryol® propylene glycol monocaprylate, caprylocaproylmacrogol-8-glycerides (Labrasol®) and polyoxyl-15-hydroxystearate (Kolliphor® HS15) individually and in combination to develop NLCs with effective permeation across in-vitro 3D BBB model without alteration in the integrity of the barrier. With desirable spherical shape as revealed by TEM and an average particle size of 123.3 ± 0.642 nm and zeta potential of -32 mV, DTX-NLCs demonstrated excellent stability for six months in its freeze-dried form. The confocal microscopy along with flow cytometry data revealed higher internalisation of DTX-NLCs in U87MG over SVG P12 cells. Micropinocytosis was observed to be one of the dominant pathways for internalisation in U87MG cells while clathrin-mediated pathway was more predominat in patient-derived glioblastoma cells. The NLCs readily penetrated the actively proliferating peripheral cells on the surface of the 3D tumour spheroids as compared to the necrotic core. The DTX-NLCs induced cell arrest through G2/M phase with a significant decrease in the mitochondrial reserve capacity of cells. The NLCs circumvented BBTB with high permeability followed by accumulation in glioblastoma cells with patient-derived cells displaying ~2.4-fold higher uptake in comparison to U87MG when studied in a 3D in-vitro model of BBTB/GB. We envisage this simple and industrially feasible technology as a potential candidate to be developed as GB nanomedicine.

The Diagnostic and Therapeutic Role of Leptin and Its Receptor ObR in Glioblastoma Multiforme

Simple Summary

Despite recent advances in molecular brain tumor therapies, glioblastoma multiforme remains a diagnostic and therapeutic challenge with, in most cases, unfavorable outcome. Leptin and related mediators of immune-metabolic traffic have attracted increased recognition in the past decade in brain tumor biology, in particular potential implications in the diagnosis and treatment of recurrent and newly diagnosed high and low grade gliomas. Randomized controlled trails are on the way to elaborate the role of leptin and its receptor ObR by targeting and using antidiabetic drugs known to interact with distinct pathways associated with leptin signaling. To date, most of the findings in clinical studies remain preliminary and of heterogenous character, although experimental studies have underpinned the relevance of leptin and ObR in the pathophysiology of brain tumors in general.

Abstract

Leptin has been recognized as a potential tumor growth promoter in various cancers including cranial tumor pathologies such as pituitary adenomas, meningiomas and gliomas. Despite recent advances in adjunctive therapy and the established surgical resection, chemo- and radiotherapy regimen, glioblastoma multiforme remains a particular diagnostic and therapeutic challenge among the intracranial tumor pathologies, with a poor long-term prognosis. Systemic inflammation and immune-metabolic signaling through diverse pathways are thought to impact the genesis and recurrence of brain tumors, and glioblastoma multiforme in particular. Among the various circulating mediators, leptin has gained especial diagnostic and therapeutic interest, although the precise relationship between leptin and glioblastoma biology remains largely unknown. In this narrative review (MEDLINE/OVID, SCOPUS, PubMed and manual searches of the bibliographies of known primary and review articles), we discuss the current literature using the following search terms: leptin, glioblastoma multiforme, carcinogenesis, immunometabolism, biomarkers, metformin, antidiabetic medication and metabolic disorders. An increasing body of experimental evidence implicates a relationship between the development and maintenance of gliomas (and brain tumors in general) with a dysregulated central and peripheral immune-metabolic network mediated by circulating adipokines, chemokines and cellular components, and in particular the leptin adipokine. In this review, we summarize the current evidence of the role of leptin in glioblastoma pathophysiology. In addition, we describe the status of alternative diagnostic tools and adjunctive therapeutics targeting leptin, leptin-receptors, antidiabetic drugs and associated pathways. Further experimental and clinical trials are needed to elucidate the mechanism of action and the value of immune-metabolism molecular phenotyping (central and peripheral) in order to develop novel adjunctive diagnostics and therapeutics for newly diagnosed and recurrent glioblastoma patients.

Quantitative analysis of human blood serum using vibrational spectroscopy

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State of the art of quantitative Vibrational Spectroscopic analysis of human blood serum is reviewed.

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Technical considerations for infrared absorption and Raman analysis are discussed.

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Quantitative analyses of Endogenous and Exogenous constituents are presented.

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The potential for clinical translation of spectroscopic serology is argued.

Analysis of bodily fluids using vibrational spectroscopy has attracted increasing attention in recent years. In particular, infrared spectroscopic screening of blood products, particularly blood serum, for disease diagnostics has been advanced considerably, attracting commercial interests. However, analyses requiring quantification of endogenous constituents or exogenous agents in blood are less well advanced. Recent advances towards this end are reviewed, focussing on infrared and Raman spectroscopic analyses of human blood serum. The importance of spectroscopic analysis in the native aqueous environment is highlighted, and the relative merits of infrared absorption versus Raman spectroscopy are considered, in this context. It is argued that Raman spectroscopic analysis is more suitable to quantitative analysis in liquid samples, and superior performance for quantification of high and low molecular weight components, is demonstrated. Applications for quantitation of viral loads, and therapeutic drug monitoring are also discussed.

Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying"

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and β-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Salivary molecular spectroscopy: A sustainable, rapid and non-invasive monitoring tool for diabetes mellitus during insulin treatment

Monitoring of blood glucose is an invasive, painful and costly practice in diabetes. Consequently, the search for a more cost-effective (reagent-free), non-invasive and specific diabetes monitoring method is of great interest. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy has been used in diagnosis of several diseases, however, applications in the monitoring of diabetic treatment are just beginning to emerge. Here, we used ATR-FTIR spectroscopy to evaluate saliva of non-diabetic (ND), diabetic (D) and insulin-treated diabetic (D+I) rats to identify potential salivary biomarkers related to glucose monitoring. The spectrum of saliva of ND, D and D+I rats displayed several unique vibrational modes and from these, two vibrational modes were pre-validated as potential diagnostic biomarkers by ROC curve analysis with significant correlation with glycemia. Compared to the ND and D+I rats, classification of D rats was achieved with a sensitivity of 100%, and an average specificity of 93.33% and 100% using bands 1452 cm^-1 and 836 cm^-1, respectively. Moreover, 1452 cm^-1 and 836 cm^-1 spectral bands proved to be robust spectral biomarkers and highly correlated with glycemia (R² of 0.801 and 0.788, P < 0.01, respectively). Both PCA-LDA and HCA classifications achieved an accuracy of 95.2%. Spectral salivary biomarkers discovered using univariate and multivariate analysis may provide a novel robust alternative for diabetes monitoring using a non-invasive and green technology.

Breast cancer detection by ATR-FTIR spectroscopy of blood serum and multivariate data-analysis

Detection of breast cancer has particular importance for the diagnosis of cancer diseases. This is the most common type of cancer among women. Breast cancer is a malignant tumor of the glandular tissue of the breast. It is proposed to use infrared spectroscopy of blood serum as a simple and quick way to detect breast cancer. The paper presents the results of research using the methods of multivariate processing of IR spectra of human blood serum obtained by ATR-FTIR spectroscopy. The paper presents the results of research using the methods of multivariate processing of IR spectra of human blood serum obtained by ATR-FTIR spectroscopy. A sufficiently large sample of patients and healthy donors was diagnosed. Blood samples are examined from 66 patients who are clinically diagnosed with breast cancer and 80 healthy volunteers. A feature of the applied approach was a combination of the method of principal component analysis (PCA) and principal component regression (PCR) for processing the IR spectra of blood serum. The PCA method allows us to determine the spectral bands referring for the intensity differences between the control group and the patient group. Shown, that the range of 1306-1250cm^-1 in the IR spectrum of blood serum is diagnostically significant for breast cancer. This range corresponds to the vibrations of several functional groups of DNA and RNA, which play a key role in discrimination in breast cancer screening using ATR-FTIR spectroscopy. It is shown that the proposed method has advantages in ease of use for clinical diagnosis and gives good results for the identification of breast cancer. The values of sensitivity (92.3%) and specificity (87.1%) obtained using the PCR method are close to those of mammography and ultrasound. This indicates the possibility of using this method in real clinical laboratory diagnostics.