Discrimination of mongoose hair from domestic cattle hair, human hair, and synthetic fiber using FTIR spectroscopy and chemometric analysis: a rapid, cost-effective, and field-deployable tool for wildlife forensics

Mongoose hair is used to prepare fine brushes, which increases the demand for mongooses to be poached from the wild and brutally bludgeoned to death. Mongooses were listed as Schedule I species under the Indian Wildlife (Protection) Act 1972. Species identification of wildlife case-related samples is necessary to convict a person under this legislation. Microscopy and DNA-based techniques are commonly used to identify mongoose hair in seized brushes. However, in painting brushes, the roots, and the lower part of the hair are mostly trimmed, and only the upper part is used to make the brushes. In addition, brushes are often prepared with mixed hair from mongoose, domestic cattle, human hair, and synthetic fibre. Therefore, the identification of mongoose hair by microscopy and DNA-based techniques is restricted due to the lack of complete strands of hair and the absence of hair roots. Therefore, there is an urgent need to develop an alternative methodology for the identification of mongoose hair from seized articles. FTIR spectroscopy for forensic analysis has gained significant attention over the years because of its sensitivity, specificity, and non-destructive nature. The present study aimed to discriminate Indian grey mongoose (Herpestes edwardsii) hair from domestic cattle hair (domestic water buffalo and domestic cow), human hair, and synthetic fiber based on their chemical composition using FTIR spectroscopy and chemometric analysis. We have taken hair from four individuals for each species, namely Indian grey mongoose, domestic cattle, human hair, and synthetic fibre. The FTIR spectrum was recorded, and partial least-squares discriminant analysis (PLS-DA) was used to discriminate hair and synthetic fiber. The established PLS-DA model showed an R-square value and an RMSE (root mean square error) value of 0.9 and 0.13 respectively. Our preliminary findings have shown that FTIR spectroscopy combined with chemometrics can quickly discriminate Indian grey mongoose hair, domestic cattle hair, human hair, and synthetic fiber, providing crucial evidence for judicial proceedings.

Rapid Hyperspectral Photothermal Mid-Infrared Spectroscopic Imaging from Sparse Data for Gynecologic Cancer Tissue Subtyping

Ovarian cancer detection has traditionally relied on a multistep process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled data sets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology.

Standardized Electric-Field-Resolved Molecular Fingerprinting

Field-resolved infrared spectroscopy (FRS) of impulsively excited molecular vibrations can surpass the sensitivity of conventional time-integrating spectroscopies, owing to a temporal separation of the molecular signal from the noisy excitation. However, the resonant response carrying the molecular signal of interest depends on both the amplitude and phase of the excitation, which can vary over time and across different instruments. To date, this has compromised the accuracy with which FRS measurements could be compared, which is a crucial factor for practical applications. Here, we utilize a data processing procedure that overcomes this shortcoming while preserving the sensitivity of FRS. We validate the approach for aqueous solutions of molecules. The employed approach is compatible with established processing and evaluation methods for the analysis of infrared spectra and can be applied to existing spectra from databases, facilitating the spread of FRS to new molecular analytical applications.

Saliva Based Diagnostic Prediction of Oral Squamous Cell Carcinoma using FTIR Spectroscopy

Oral cancer ranks as the sixth most prevalent form of cancer worldwide, presenting a significant public health concern. According to the World Health Organization (WHO), within a 5-year period following diagnosis, the mortality rate among oral cancer patients of all stages stands at 45%. In this study, a total of 60 patients divided into 2 groups were recruited. Group A included 30 histo-pathologically confirmed OSCC patients and Group B included 30 healthy controls. A standardized procedure was followed to collect saliva samples. FTIR spectroscopy was done for all the saliva samples collected from both Group A and B. An IR Prestige-21 (Shimadzu Corp, Japan) spectrometer was used to record IR spectra in the 40-4000 cm-1 range SVM classifier was applied in the classification of disease state from normal subjects using FTIR data. The peaks were identified at wave no 1180 cm-1, 1230 cm-1, 1340 cm-1, 1360 cm-1, 1420 cm-1, 1460 cm-1, 1500 cm-1, 1540 cm-1, 1560 cm-1, and 1637 cm-1. The observed results of SVM demonstrated the accuracy of 91.66% in the classification of Cancer tissues from the normal subjects with sensitivity of 83.33% while specificity and precision of 100.0%. The development of oral cancer leads to noticeable alterations in the secondary structure of proteins. These findings emphasize the promising use of ATR-FTIR platforms in conjunction with machine learning as a reliable, non-invasive, reagent-free, and highly sensitive method for screening and monitoring individuals with oral cancer.

Perturbation of Fermi Resonance on Hydrogen-Bonded > C═O: 2D IR Studies of Small Ester Probes

We utilized linear and 2D infrared spectroscopy to analyze the carbonyl stretching modes of small esters in different solvents. Particularly noteworthy were the distinct carbonyl spectral line shapes in aqueous solutions, prompting our investigation of the underlying factors responsible for these differences. Through our experimental and theoretical calculations, we identified the presence of the hydrogen-bond-induced Fermi resonance as the primary contributor to the varied line shapes of small esters in aqueous solutions. Furthermore, our findings revealed that the skeletal deformation mode plays a crucial role in the Fermi resonance for all small esters. Specifically, the first overtone band of the skeletal deformation mode intensifies when hydrogen bonds form with the carbonyl group of esters, whereas such coupling is rare in aprotic organic solvents. These spectral insights carry significant implications for the utilization of esters as infrared probes in both biological and chemical systems.

Structural characterization of DNA amplicons by ATR-FTIR spectroscopy as a guide for screening metainflammatory disorders in blood plasma

Attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy is a promising rapid, reagent-free, and low-cost technique considered for clinical translation. It allows to characterize biofluids proteome, lipidome, and metabolome at once. Metainflammatory disorders share a constellation of chronic systemic inflammation, oxidative stress, aberrant adipogenesis, and hypoxia, that significantly increased cardiovascular and cancer risk. As a result, these patients have elevated concentration of cfDNA in the bloodstream. Considering this, DNA amplicons were analyzed by ATR-FTIR at 3 concentrations with 1:100 dilution: (IU/mL): 718, 7.18, and 0.0718. The generated IR spectrum was used as a guide for variable selection. The main peaks in the biofingerprint (1800-900 cm-1) give important information about the base, base-sugar, phosphate, and sugar-phosphate transitions of DNA. To validate our method of selecting variables in blood plasma, 38 control subjects and 12 with metabolic syndrome were used. Using the wavenumbers of the peaks in the biofingerprint of the DNA amplicons, was generated a discriminant analysis model with Mahalanobis distance in blood plasma, and 100 % discrimination accuracy was obtained. In addition, the interval 1475-1188 cm-1 showed the greatest sensitivity to variation in the concentration of DNA amplicons, so curve fitting with Gaussian funcion was performed, obtaining adjusted-R2 of 0.993. PCA with Mahalanobis distance in the interval 1475-1188 cm-1 obtained an accuracy of 96 % and PLS-DA modeling in the interval 1475-1088 cm-1 obtained AUC = 0.991 with sensitivity of 95 % and specificity of 100 %. Therefore, ATR-FTIR spectroscopy with variable selection guided by DNA IR peaks is a promising and efficient method to be applied in metainflammatory disorders.

Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA)

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.

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.

Guess What We Can Hear-Novel Voice Biomarkers for the Remote Detection of Disease

The advancement of digital biomarkers and the provision of remote health care greatly progressed during the coronavirus disease 2019 global pandemic. Combining voice/speech data with artificial intelligence and machine-based learning offers a novel solution to the growing demand for telemedicine. Voice biomarkers, obtained from the extraction of characteristic acoustic and linguistic features, are associated with a variety of diseases and even coronavirus disease 2019. In the current review, we (1) describe the basis on which digital voice biomarkers could facilitate "telemedicine," (2) discuss potential mechanisms that may explain the association between voice biomarkers and disease, (3) offer a novel classification system to conceptualize voice biomarkers depending on different methods for recording and analyzing voice/speech samples, (4) outline evidence revealing an association between voice biomarkers and a number of disease states, and (5) describe the process of developing a voice biomarker from recording, storing voice samples, and extracting acoustic and linguistic features relevant to training and testing deep and machine-based learning algorithms to detect disease. We further explore several important future considerations in this area of research, including the necessity for clinical trials and the importance of safeguarding data and individual privacy. To this end, we searched PubMed and Google Scholar to identify studies evaluating the relationship between voice/speech features and biomarkers and various diseases. Search terms included digital biomarker, telemedicine, voice features, voice biomarker, speech features, speech biomarkers, acoustics, linguistics, cardiovascular disease, neurologic disease, psychiatric disease, and infectious disease. The search was limited to studies published in English in peer-reviewed journals between 1980 and the present. To identify potential studies not captured by our database search strategy, we also searched studies listed in the bibliography of relevant publications and reviews.

Identification of acute myeloid leukemia by infrared difference spectrum of peripheral blood

Acute myeloid leukemia (AML) is a high mortality and recurrence rates hematologic malignancy. Thus, whatever early detection or subsequent visit are both of high significance. Traditional AML diagnosis is conducted via peripheral blood (PB) smear and bone marrow (BM) aspiration. But BM aspiration is a painful burden for patients especially in early detection or subsequent visit. Herein, the use of PB to evaluate and identify the leukemia characteristics will be an attractive alternative source for early detection or subsequent visit. Fourier transform infrared spectroscopy (FTIR) is a time- and cost-effective approach to reveal the disease-related molecular features and variations. However, to the best of our knowledge, there is no attempts using infrared spectroscopic signatures of PB to replace BM for identifying AML. In this work, we are the first to develop a rapid and minimally invasive method to identify AML by infrared difference spectrum (IDS) of PB with only 6 characteristic wavenumbers. We dissect the leukemia-related spectroscopic signatures of three subtypes of leukemia cells (U937, HL-60, THP-1) by IDS, revealing biochemical molecular information about leukemia for the first time. Furthermore, the novel study links cellular features to complex features of blood system which demonstrates the sensitivity and specificity with IDS method. On this basis, BM and PB of AML patients and healthy controls were provided to parallel comparison. The IDS of BM and PB combined with principal component analysis method revealing that the leukemic components in BM and PB can be described by IDS peaks of PCA loadings, respectively. It is demonstrated that the leukemic IDS signatures of BM can be replaced by the leukemic IDS signatures of PB. In addition, the IDS signatures of leukemia cells are reflected in PB of AML patients with peaks of 1629, 1610, 1604, 1536, 1528 and 1404 cm^-1 for the first time as well. To this end, we access the leukemic signatures of IDS peaks to compare the PB of AMLs and healthy controls. It is confirmed that the leukemic components can be detected from PB of AML and distinguished into positive (100%) and negative (100%) groups successfully by IDS classifier which is a novel and unique spectral classifier. This work demonstrates the potential use of IDS as a powerful tool to detect leukemia via PB which can release subjects' pain remarkably.

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

Protocol for bacterial typing using Fourier transform infrared spectroscopy

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

The Potential Use of Near- and Mid-Infrared Spectroscopy in Kidney Diseases

Traditional renal biomarkers such as serum creatinine and albuminuria/proteinuria are rather insensitive since they change later in the course of the disease. In order to determine the extent and type of kidney injury, as well as to administer the proper therapy and enhance patient management, new techniques for the detection of deterioration of the kidney function are urgently needed. Infrared spectroscopy is a label-free and non-destructive technique having the potential to be a vital tool for quick and inexpensive routine clinical diagnosis of kidney disorders. The aim of this review is to provide an overview of near- and mid-infrared spectroscopy applications in patients with acute kidney injury and chronic kidney disease (e.g., diabetic nephropathy and glomerulonephritis).

Potential Role of Fourier Transform Infrared Spectroscopy as a Screening Approach for Breast Cancer

Breast cancer is a heterogeneous disease, and its spread involves a succession of clinical and pathological stages. Screening is predominantly based on mammography, which has critical limitations related to the effectiveness and production of false-positive or false-negative results, generating discomfort and low adherence. In this context, infrared with attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy emerges as a non-destructive sample tool, which is non-invasive, label-free, has a low operating-cost, and requires only a small amount of sample, including liquid plasma samples. We sought to evaluate the clinical applicability of ATR FT-IR in breast cancer screening. ATR FT-IR spectroscopy through its highest potential spectral biomarker could distinguish, by liquid plasma biopsy, breast cancer patients and healthy controls, obtaining a sensitivity of 97%, specificity of 93%, a receiver operating characteristic ROC curve of 97%, and a prediction accuracy of 94%. The main variance between the groups was mainly in the band 1511 cm^-1 of the control group, 1502 and 1515 cm^-1 of the cancer group, which are the peaks of the bands referring to proteins and amide II. ATR FT-IR spectroscopy has demonstrated to be a promising tool for breast cancer screening, given its time efficiency, cost of approach, and its high ability to distinguish between the liquid plasma samples of breast cancer patients and healthy controls.

Molecular fingerprints resolve affinities of Rhynie chert organic fossils

The affinities of extinct organisms are often difficult to resolve using morphological data alone. Chemical analysis of carbonaceous specimens can complement traditional approaches, but the search for taxon-specific signals in ancient, thermally altered organic matter is challenging and controversial, partly because suitable positive controls are lacking. Here, we show that non-destructive Fourier Transform Infrared Spectroscopy (FTIR) resolves in-situ molecular fingerprints in the famous 407 Ma Rhynie chert fossil assemblage of Aberdeenshire, Scotland, an important early terrestrial Lagerstätte. Remarkably, unsupervised clustering methods (principal components analysis and K-mean) separate the fossil spectra naturally into eukaryotes and prokaryotes (cyanobacteria). Additional multivariate statistics and machine-learning approaches also differentiate prokaryotes from eukaryotes, and discriminate eukaryotic tissue types, despite the overwhelming influence of silica. We find that these methods can clarify the affinities of morphologically ambiguous taxa; in the Rhynie chert for example, we show that the problematic "nematophytes" have a plant-like composition. Overall, we demonstrate that the famously exquisite preservation of cells, tissues and organisms in the Rhynie chert accompanies similarly impressive preservation of molecular information. These results provide a compelling positive control that validates the use of infrared spectroscopy to investigate the affinity of organic fossils in chert.

Detection of metabolic syndrome with ATR-FTIR spectroscopy and chemometrics in blood plasma

Metabolic Syndrome (MetS) is a constellation of 3 or more risk factor (abdominal obesity, high triglycerides, low HDL-c, high blood pressure, and elevated blood glucose) for atherosclerotic cardiovascular disease. Considering these systemic metabolic changes in the biochemical pathways of all biomolecules, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy is a rapid, low-cost, and reagent-free alternative technique capable of identifying spectral biomarkers that differentiate subjects with MetS from control. In this study, plasma samples from 74 subjects (14 MetS, 60 control) were analyzed on the ATR-FTIR spectrophotometer. The objective was to differentiate subjects with MetS from control with supervised chemometrics modeling (Orthogonal Partial Least Squares-Discriminant Analysis, OPLS-DA). Additionally, the inflammatory status of subjects with MetS and control (supervised by C-reactive protein - CRP, leptin, and cell-free DNA - cfDNA) was verified. The OPLS-DA model achieved 100% sensitivity and specificity in cross-validation. For 1 latent variable (93.4% of variance), RMSECV < 0.002, PRESS CV < 0.0001, and R² > 0.9999 was obtained. Significant spectrochemical differences (p < 0.05) were found between MetS and control subjects in the following biomolecular regions (cm^-1): 1717-1703 [ν(CO) and δ(NH)], 1166-1137 [ν(C-OH) + ν(CO) and ν(CC) + δ(OH) + ν(CO)], 1113-1040 [ν(PO₂^-) and ν(C-OH)], and 1027-1008 [ν(CO) and v(CH₂OH)]. In the OPLS-DA model loadings, amide I [1720-1600 cm^-1, ν(CO)] and amide II [1570-1480 cm^-1, δ(NH) + ν(CH)] had significantly greater weight than all other regions. There was a significant difference in inflammatory status between MetS patient and control (p < 0.05 for CRP and leptin, and p < 0.01 for cfDNA).

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

Chronological Age Estimation of Male Occipital Bone Based on FTIR and Raman Microspectroscopy

Age-related changes in bone tissue have always been an important part of bone research, and age estimation is also of great significance in forensic work. In our study, FTIR and Raman microspectroscopy were combined to explore the structural and chronological age-related changes in the occipital bones of 40 male donors. The FTIR micro-ATR mode not only achieves the comparison of FTIR and Raman efficiency but also provides a new pattern for the joint detection of FTIR and Raman in hard tissue. Statistical analysis and PCA results revealed that the structure had little effect on the FTIR and Raman results. The FTIR and Raman mineral/matrix ratio, carbonate/phosphate ratio, crystallinity, and collagen maturity of the whole showed an increasing trend during maturation, and a significant correlation was found between FTIR and Raman by comparing four outcomes. Furthermore, the results indicated that the cutoff point of the change in the relative proportion of organic matrix and inorganic minerals in males was between 19 and 35 years old, and the changes in the relative proportion of organic matrix and inorganic minerals may play a key role in age estimation. Ultimately, we established age estimation regression models. The FTIR GA-PLS regression model has the best performance and is more suitable for our experiment (RMSECV = 10.405, RMSEP = 9.2654, R²CV = 0.814, and R²Pred = 0.828). Overall, FTIR and Raman combined with chemometrics are an ideal method to estimate chronological age based on age-dependent component changes in male occipital bones. Our experiment provides a proof of concept and potential experimental method for chronological age estimation.

Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra

Oral epithelial dysplasia (OED) is a histopathologically-defined, potentially premalignant condition of the oral cavity. The rate of transformation to frank carcinoma is relatively low (12% within 2 years) and prediction based on histopathological grade is unreliable, leading to both over- and under-treatment. Alternative approaches include infrared (IR) spectroscopy, which is able to classify cancerous and non-cancerous tissue in a number of cancers, including oral. The aim of this study was to explore the capability of FTIR (Fourier-transform IR) microscopy and machine learning as a means of predicting malignant transformation of OED. Supervised, retrospective analysis of longitudinally-collected OED biopsy samples from 17 patients with high risk OED lesions: 10 lesions transformed and 7 did not over a follow-up period of more than 3 years. FTIR spectra were collected from routine, unstained histopathological sections and machine learning used to predict malignant transformation, irrespective of OED classification. PCA-LDA (principal component analysis followed by linear discriminant analysis) provided evidence that the subsequent transforming status of these 17 lesions could be predicted from FTIR data with a sensitivity of 79 ± 5% and a specificity of 76 ± 5%. Six key wavenumbers were identified as most important in this classification. Although this pilot study used a small cohort, the strict inclusion criteria and classification based on known outcome, rather than OED grade, make this a novel study in the field of FTIR in oral cancer and support the clinical potential of this technology in the surveillance of OED.

Label-Free Diagnosis of Pulmonary Fat Embolism Using Fourier Transform Infrared (FT-IR) Spectroscopic Imaging

The diagnosis of pulmonary fat embolism (PFE) is of great significance in the field of forensic medicine because it can be considered a major cause of death or a vital reaction. Conventional histological analysis of lung tissue specimens is a widely used method for PFE diagnosis. However, variable and labor-intensive tissue staining procedures impede the validity and informativeness of histological image analysis. To obtain complete information from tissues, a method based on infrared imaging of unlabeled tissue sections was developed to identify pulmonary fat emboli in the present study. We selected 15 PFE-positive lung samples and 15 PFE-negative samples from real cases. Oil red O (ORO) staining and infrared spectral imaging collection were both performed on all lung tissue samples. And the fatty tissue of the abdominal wall and the embolized lipid droplets in the lungs were taken for comparison. The results of the blind, evaluation by pathologists, showed good agreement between the infrared spectral imaging of the lung tissue and the standard histological stained images. Fourier transform infrared (FT-IR) spectroscopic imaging significantly simplifies the typical painstakingly laborious histological staining procedure. And we found a difference between lipid droplets embolized in abdominal wall fat and lung tissue.

Progress in infrared spectroscopy as an efficient tool for predicting protein secondary structure

Infrared (IR) spectroscopy is a highly sensitive technique that provides complete information on chemical compositions. The IR spectra of proteins or peptides give rise to nine characteristic IR absorption bands. The amide I bands are the most prominent and sensitive vibrational bands and widely used to predict protein secondary structures. The interference of H₂O absorbance is the greatest challenge for IR protein secondary structure prediction. Much effort has been made to reduce/eliminate the interference of H₂O, simplify operation steps, and increase prediction accuracy. Progress in sampling and equipment has rendered the Fourier transform infrared (FTIR) technique suitable for determining the protein secondary structure in broader concentration ranges, greatly simplifying the operating steps. This review highlights the recent progress in sample preparation, data analysis, and equipment development of FTIR in A/T mode, with a focus on recent applications of FTIR spectroscopy in the prediction of protein secondary structure. This review also provides a brief introduction of the progress in ATR-FTIR for predicting protein secondary structure and discusses some combined IR methods, such as AFM-based IR spectroscopy, that are used to analyze protein structural dynamics and protein aggregation.

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

The chemical composition of recent and fossil organic-walled dinoflagellate cyst walls and its diversity is poorly understood and analyses on single microscopic specimens are rare. A series of infrared spectroscopic experiments resulted in the proposition of a standardized attenuated total reflection micro-Fourier transform infrared-based method that allows the collection of robust data sets consisting of spectra from individual dinocysts. These data sets are largely devoid of nonchemical artifacts inherent to other infrared spectrochemical methods, which have typically been used to study similar specimens in the past. The influence of sample preparation, specimen morphology and size and spectral data processing steps is also assessed within this methodological framework. As a result, several guidelines are proposed which facilitate the collection and qualitative interpretation of highly reproducible and repeatable spectrochemical data. These, in turn, pave the way for a systematic exploration of dinocyst chemistry and its assessment as a chemotaxonomical tool or proxy.

Detection of lipid efflux from foam cell models using a label-free infrared method

Synchrotron-based microFTIR spectroscopy was used to study the process of lipid efflux in a foam cell model. The anti-atherosclerotic drug, atorvastatin, removed low-density lipoprotein from the foam cells in a dose, and time dependent manner.

Regional differences in clonal Japanese knotweed revealed by chemometrics-linked attenuated total reflection Fourier-transform infrared spectroscopy

BACKGROUND

Japanese knotweed (R. japonica var japonica) is one of the world's 100 worst invasive species, causing crop losses, damage to infrastructure, and erosion of ecosystem services. In the UK, this species is an all-female clone, which spreads by vegetative reproduction. Despite this genetic continuity, Japanese knotweed can colonise a wide variety of environmental habitats. However, little is known about the phenotypic plasticity responsible for the ability of Japanese knotweed to invade and thrive in such diverse habitats. We have used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, in which the spectral fingerprint generated allows subtle differences in composition to be clearly visualized, to examine regional differences in clonal Japanese knotweed.

RESULTS

We have shown distinct differences in the spectral fingerprint region (1800-900 cm^- 1) of Japanese knotweed from three different regions in the UK that were sufficient to successfully identify plants from different geographical regions with high accuracy using support vector machine (SVM) chemometrics.

CONCLUSIONS

These differences were not correlated with environmental variations between regions, raising the possibility that epigenetic modifications may contribute to the phenotypic plasticity responsible for the ability of R. japonica to invade and thrive in such diverse habitats.

Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy

The non-destructive visible/near-infrared (VIS/NIR) spectroscopy is a promising technique in determining the quality of agricultural commodities. Therefore, this study aimed to examine the ability of VIS/NIR spectroscopy (550–1100 nm) to distinguish between three different varieties of tomato (i.e., Ekram, Harver and Izmer), as well as to predict the quality parameters of tomato, such as soluble solids content (SSC), titratable acidity (TA), taste (SSC/TA) and firmness. Ninety intact samples from three tomato varieties were used. These samples were examined using VIS/NIR spectroscopy and quality parameters were also measured using traditional methods. Principal component analysis (PCA) and partial least square (PLS) were carried out. The results of PCA showed the ability of VIS/NIR spectroscopy to distinguish between the three varieties, where two PCs explained about 99% of the total variance in both calibration and validation sets. Moreover, PLS showed the possibility of modelling quality parameters. The correlation coefficient (R2) and the ratio of performance deviation (RPD) for all quality parameters (except for firmness) were found to be higher than 0.85 and 2.5, respectively. Thus, these results indicate that the VIS/NIR spectroscopy can be used to discriminate between different varieties of tomato and predict their quality parameters.

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.

Rapid classification of chromoblastomycosis agents genera by infrared spectroscopy and chemometrics supervised by sequencing of rDNA regions

Chromoblastomycosis (CBM) is a skin and subcutaneous infection caused by species of seven fungal genera. Identification of CBM species is performed by DNA sequencing of one or more genes, which becomes a time-consuming work. Fourier Transform Infrared Spectroscopy (FTIR) has been used for the identification of other microorganisms, however, only one CBM genus was evaluated by FTIR analysis to date. Therefore, the study is aimed to differentiate the CBM agents for identification at genera level using FTIR supervised by Internal Transcribed Spacer (ITS) rDNA region. Seventy-seven isolates of the main five CBM genera were prepared for Attenuated Total Reflection FTIR (ATR-FTIR) with a new methodology using slices of dry fungus in glass fixing-modeling proposed in this study. The algorithm Hierarchical Cluster Analysis (HCA) was used to analyze the differences and similarities between species through the spectra. Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) allowed to correctly classify all samples of five CBM genera. The ATR-FTIR/OPLS-DA models highlighted important contributions of regions attributed to NH and OH stretching, amide I of proteins, polysaccharides bands and fingerprint region for the complete differentiation of the genera investigated. Thus, FTIR can be a fast and inexpensive alternative for identification of CBM agents.

Distinguishing active from quiescent disease in ANCA-associated vasculitis using attenuated total reflection Fourier-transform infrared spectroscopy

The current lack of a reliable biomarker of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA) associated vasculitis poses a significant clinical unmet need when determining relapsing or persisting disease. In this study, we demonstrate for the first time that attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy offers a novel and functional candidate biomarker, distinguishing active from quiescent disease with a high degree of accuracy. Paired blood and urine samples were collected within a single UK centre from patients with active disease, disease remission, disease controls and healthy controls. Three key biofluids were evaluated; plasma, serum and urine, with subsequent chemometric analysis and blind predictive model validation. Spectrochemical interrogation proved plasma to be the most conducive biofluid, with excellent separation between the two categories on PC2 direction (AUC 0.901) and 100% sensitivity (F-score 92.3%) for disease remission and 85.7% specificity (F-score 92.3%) for active disease on blind predictive modelling. This was independent of organ system involvement and current ANCA status, with similar findings observed on comparative analysis following successful remission-induction therapy (AUC > 0.9, 100% sensitivity for disease remission, F-score 75%). This promising technique is clinically translatable and warrants future larger study with longitudinal data, potentially aiding earlier intervention and individualisation of treatment.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Biomarkers in ANCA-Associated Vasculitis: Potential Pitfalls and Future Prospects

Over the past 3 decades, significant advancements in the understanding of the pathophysiology of ANCA-associated vasculitis has led to the development of a multitude of potential candidate biomarkers. Accompanied by the advent of increasingly effective therapeutic strategies, the need for a dependable biomarker to help determine the extent of disease activity and risk of relapse is ever present. Implementation of such a biomarker would enable tailored therapy, optimizing disease control while helping to mitigate unnecessary exposure to therapy and potential treatment-related damage. Although far from perfect, ANCA serology and B-cell population are the two main staple biomarker tools widely used in practice to help supplement clinical assessment. Over recent years, the application and progress of more novel biomarker tools have arisen in both organ-limited and multisystem disease, including genomics, urinary proteins, degradation products of the alternative complement system, cytokines, metabolomics, and biospectroscopy. Validation studies and clinical translation of these tools are required, with serial assessment of disease activity and determination of therapy according to biomarker status correlated with patient outcomes.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

ATR-FTIR spectroscopy and spectroscopic imaging for the analysis of biopharmaceuticals

Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a label-free, non-destructive technique that can be applied to a vast range of biological applications, from imaging cancer tissues and live cells, to determining protein content and protein secondary structure composition. This review summarises the recent advances in applications of ATR-FTIR spectroscopy to biopharmaceuticals, the application of this technique to biosimilars, and the current uses of FTIR spectroscopy in biopharmaceutical production. We discuss the use of ATR-FTIR spectroscopic imaging to investigate biopharmaceuticals, and finally, give an outlook on the possible future developments and applications of ATR-FTIR spectroscopy and spectroscopic imaging to this field. Throughout the review comparisons will be made between FTIR spectroscopy and alternative analytical techniques, and areas will be identified where FTIR spectroscopy could perhaps offer a better alternative in future studies. This review focuses on the most recent advances in the field of using ATR-FTIR spectroscopy and spectroscopic imaging to characterise and evaluate biopharmaceuticals, both in industrial and academic research based environments.

Towards screening Barrett's oesophagus: current guidelines, imaging modalities and future developments

Barrett's oesophagus is the only known precursor to oesophageal adenocarcinoma (OAC). Although guidelines on the screening and surveillance exist in Barrett's oesophagus, the current strategies are inadequate. Oesophagogastroduodenoscopy (OGD) is the gold standard method in screening for Barrett's oesophagus. This invasive method is expensive with associated risks negating its use as a current screening tool for Barrett's oesophagus. This review explores current definitions, epidemiology, biomarkers, surveillance, and screening in Barrett's oesophagus. Imaging modalities applicable to this condition are discussed, in addition to future developments. There is an urgent need for an alternative non-invasive method of screening and/or surveillance which could be highly beneficial towards reducing waiting times, alleviating patient fears and reducing future costs in current healthcare services. Vibrational spectroscopy has been shown to be promising in categorising Barrett's oesophagus through to high-grade dysplasia (HGD) and OAC. These techniques need further validation through multicentre trials.

Rapid intraoperative diagnosis of gynecological cancer by ATR-FTIR spectroscopy of fresh tissue biopsy

A rapid and reliable intraoperative diagnostic technique to support clinical decisions was developed using Fourier-transform infrared (FTIR) spectroscopy. Twenty-six fresh tissue samples were collected intraoperatively from patients undergoing gynecological surgeries. Frozen section (FS) histopathology aimed to discriminate between malignant and benign tumors was performed, and attenuated total reflection (ATR) FTIR spectra were collected from these samples. Digital dehydration and principal component analysis and linear discriminant analysis (PCA-LDA) models were developed to classify samples into malignant and benign groups. Two validation schemes were employed: k-fold and "leave one out." FTIR absorption spectrum of a fresh tissue sample was obtained in less than 5 minutes. The fingerprint spectral region of malignant tumors was consistently different from that of benign tumors. The PCA-LDA discrimination model correctly classified the samples into malignant and benign groups with accuracies of 96% and 93% for the k-fold and "leave one out" validation schemes, respectively. We showed that a simple tissue preparation followed by ATR-FTIR spectroscopy provides accurate means for very rapid tumor classification into malignant and benign gynecological tumors. With further development, the proposed method has high potential to be used as an adjunct to the intraoperative FS histopathology technique.

Spectrochemical analysis in blood plasma combined with subsequent chemometrics for fibromyalgia detection

Fibromyalgia is a rheumatologic condition characterized by multiple and chronic body pain, and other typical symptoms such as intense fatigue, anxiety and depression. It is a very complex disease where treatment is often made by non-medicated alternatives in order to alleviate symptoms and improve the patient’s quality of life. Herein, we propose a method to detect patients with fibromyalgia (n = 252, 126 controls and 126 patients with fibromyalgia) through the analysis of their blood plasma using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy in conjunction with chemometric techniques, hence, providing a low-cost, fast and accurate diagnostic approach. Different chemometric algorithms were tested to classify the spectral data; genetic algorithm with linear discriminant analysis (GA-LDA) achieved the best diagnostic results with a sensitivity of 89.5% in an external test set. The GA-LDA model identified 24 spectral wavenumbers responsible for class separation; amongst these, the Amide II (1,545 cm⁻¹) and proteins (1,425 cm⁻¹) were identified to be discriminant features. These results reinforce the potential of ATR-FTIR spectroscopy with multivariate analysis as a new tool to screen and detect patients with fibromyalgia in a fast, low-cost, non-destructive and minimally invasive fashion.

Identifying muscle hemorrhage in rat cadavers with advanced decomposition by FT-IR microspectroscopy combined with chemometrics

The identification of muscle hemorrhage in a cadaver that is in an advanced stage of decomposition is an important but challenging task. Our study investigated whether Fourier transform infrared (FT-IR) microspectroscopy in conjunction with chemometrics could identify muscle hemorrhage using rat cadavers with advanced decomposition. In this study, an intramuscular blood injection method, instead of a mechanical injury method, was used to construct a muscle hemorrhage model, and the modeling idea of muscle hemorrhage identification was to discriminate and classify hemoglobin-leaking myofibrils from negative myofibrils. First, the optical images of hematoxylin/eosin (H&E) stained hemorrhagic muscle at different postmortem intervals (PMIs) were observed and showed that the morphological features of whole erythrocytes disappeared since the PMI of 4 d. Subsequently, principle component analysis (PCA) was performed and indicated that the biochemical differences in protein structures between fresh erythrocytes and myofibrils can be detected by the IR spectroscopic method. Ultimately, several classification models based on the partial least square discriminant analysis (PLS-DA) algorithm were successfully constructed for different PMIs and PMI ranges and achieved great prediction performances in external validations. This preliminary study demonstrates the feasibility of using FT-IR microspectroscopy combined with chemometrics as a potential approach for identifying muscle hemorrhage in cadavers with advanced decomposition for offering vital evidences in judicial process.

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.

Vibrational Spectroscopy of Peritoneal Dialysis Effluent for Rapid Assessment of Patient Characteristics

Peritoneal dialysis (PD) offers specific advantages over hemodialysis, enabling increased autonomy of patients with end-stage renal disease, but PD-related complications need to be detected in a timely manner. Fourier transform infrared (FTIR) spectroscopy could provide rapid and essential insights into the patients' risk profiles via molecular fingerprinting of PD effluent, an abundant waste material that is rich in biological information. In this study, we measured FTIR spectroscopic profiles in PD effluent from patients taking part in a randomized controlled trial of alanyl-glutamine addition to the PD-fluid. Principal component analysis of FTIR spectra enabled us to differentiate between effluent samples from patients immediately after completion of instillation of the PD-fluid into the patients' cavity and 4 h later as well as between patients receiving PD-fluid supplemented with 8 mM alanyl-glutamine compared with control. Moreover, feasibility of FTIR spectroscopy coupled to supervised classification algorithms to predict patient-, PD-, as well as immune-associated parameters were investigated. PD modality (manual continuous ambulatory PD (CAPD) vs. cycler-assisted automated PD (APD)), residual urine output, ultrafiltration, transport parameters, and cytokine concentrations showed high predictive potential. This study provides proof-of-principle that molecular signatures determined by FTIR spectroscopy of PD effluent, combined with machine learning, are suitable for cost-effective, high-throughput diagnostic purposes in PD.

Rise of Raman spectroscopy in neurosurgery: a review

SIGNIFICANCE

Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.

AIM

We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.

APPROACH

We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.

RESULTS

The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.

CONCLUSIONS

The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.