Vibrational spectroscopy: a clinical tool for cancer diagnostics

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung 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.

Expression data of FOS and JUN genes and FTIR spectra provide diagnosis of thyroid carcinoma

We explore the feasibility of using FOS and JUN gene expression and ATR-FTIR for diagnosis of thyroid cancer. For the study, 38 samples (6 non-neoplastic (NN), 10 papillary thyroid carcinoma (PTC), 7 follicular thyroid carcinoma (FTC), and 15 benign tumors (BT) were subjected to RNA extraction followed by quantitative real time PCR (qRT-PCR) and 30 samples (5 NN, 9 PTC, 5 FTC, and 11 BT) were used for Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) followed by multivariate analysis. Of the above, 20 samples were used for both gene expression and ATR-FTIR studies. We found FOS and JUN expression in malignant tumor samples to be significantly lower than NN and benign. ATR-FIR after multivariate analysis could identify the difficult to diagnose FTC with 93 % efficiency. Overall, results suggest the diagnostic potential of molecular biology techniques combined with ATR-FTIR spectroscopy in differentiated thyroid carcinomas (PTC and FTC) and BT.

Nondestructive and Rapid Screening of Aflatoxin-Contaminated Single Peanut Kernels Using Field-Portable Spectroscopy Instruments (FT-IR and Raman)

A nondestructive and rapid classification approach was developed for identifying aflatoxin-contaminated single peanut kernels using field-portable vibrational spectroscopy instruments (FT-IR and Raman). Single peanut kernels were either spiked with an aflatoxin solution (30 ppb-400 ppb) or hexane (control), and their spectra were collected via Raman and FT-IR. An uHPLC-MS/MS approach was used to verify the spiking accuracy via determining actual aflatoxin content on the surface of randomly selected peanut samples. Supervised classification using soft independent modeling of class analogies (SIMCA) showed better discrimination between aflatoxin-contaminated (30 ppb-400 ppb) and control peanuts with FT-IR compared with Raman, predicting the external validation samples with 100% accuracy. The accuracy, sensitivity, and specificity of SIMCA models generated with the portable FT-IR device outperformed the methods in other destructive studies reported in the literature, using a variety of vibrational spectroscopy benchtop systems. The discriminating power analysis showed that the bands corresponded to the C=C stretching vibrations of the ring structures of aflatoxins were most significant in explaining the variance in the model, which were also reported for Aspergillus-infected brown rice samples. Field-deployable vibrational spectroscopy devices can enable in situ identification of aflatoxin-contaminated peanuts to assure regulatory compliance as well as cost savings in the production of peanut products.

Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data

Background

Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC).

Methods

Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC.

Results

IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm-1) and nucleic acids (at 1080 cm-1), along with increased bands of phospholipids (at 1240 and 1450 cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set.

Conclusions

IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

Optical Imaging in Human Lymph Node Specimens for Detecting Breast Cancer Metastases: A Review

Assessment of regional lymph node status in breast cancer is of important staging and prognostic value. Even though formal histological examination is the currently accepted standard of care, optical imaging techniques have shown promising results in disease diagnosis. In the present article, we review six spectroscopic techniques and focus on their use as alternative tools for breast cancer lymph node assessment. Elastic scattering spectroscopy (ESS) seems to offer a simple, cost-effective, and reproducible method for intraoperative diagnosis of breast cancer lymph node metastasis. Optical coherence tomography (OCT) provides high-resolution tissue scanning, along with a short data acquisition time. However, it is relatively costly and experimentally complex. Raman spectroscopy proves to be a highly accurate method for the identification of malignant axillary lymph nodes, and it has been further validated in the setting of head and neck cancers. Still, it remains time-consuming. Near-infrared fluorescence imaging (NIRF) and diffuse reflectance spectroscopy (DFS) are related to significant advantages, such as deep tissue penetration and efficiency. Fourier-transform infrared spectroscopy (FTIR) is a promising method but has significant drawbacks. Nonetheless, only anecdotal reports exist on their clinical use for cancerous lymph node detection. Our results indicate that optical imaging methods can create informative and rapid tools to effectively guide surgical decision-making.

A high-precision multi-dimensional microspectroscopic technique for morphological and properties analysis of cancer cell

Raman and Brillouin scattering are sensitive approaches to detect chemical composition and mechanical elasticity pathology of cells in cancer development and their medical treatment researches. The application is, however, suffering from the lack of ability to synchronously acquire the scattering signals following three-dimensional (3D) cell morphology with reasonable spatial resolution and signal-to-noise ratio. Herein, we propose a divided-aperture laser differential confocal 3D Geometry-Raman-Brillouin microscopic detection technology, by which reflection, Raman, and Brillouin scattering signals are simultaneously in situ collected in real time with an axial focusing accuracy up to 1 nm, in the height range of 200 μm. The divided aperture improves the anti-noise capability of the system, and the noise influence depth of Raman detection reduces by 35.4%, and the Brillouin extinction ratio increases by 22 dB. A high-precision multichannel microspectroscopic system containing these functions is developed, which is utilized to study gastric cancer tissue. As a result, a 25% reduction of collagen concentration, 42% increase of DNA substances, 17% and 9% decrease in viscosity and elasticity are finely resolved from the 3D mappings. These findings indicate that our system can be a powerful tool to study cancer development new therapies at the sub-cell level.

Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures

Sarcophaga peregrina (Robineau-Desvoidy, 1830) has the potential to estimate the minimum postmortem interval (PMI_min). Development data and intra-puparial age estimation are significant for PMI_min estimation. Previous research has focused on constant temperatures, although fluctuating temperatures are a more real scenario at a crime scene. The current study examined the growth patterns of S. peregrina under constant (25.75 °C) and fluctuating temperatures (18-36 °C; 22-30 °C). Furthermore, differentially expressed genes, attenuated total reflectance Fourier-transform infrared spectroscopy, and cuticular hydrocarbons of S. peregrina during the intra-puparial period were used to estimate age. The results indicated that S. peregrina at fluctuating temperatures took longer to develop and had a lower pupariation rate, eclosion rate, and pupal weight than the group at constant temperatures did. Moreover, we found that six DEG expression profiles and ATR-FTIR technology, CHCs detection methods, and chemometrics can potentially estimate the intra-puparial age of S. peregrina at both constant and fluctuating temperatures. The findings of the study support the use of S. peregrina for PMI_min estimation and encourage the use of entomological evidence in forensic practice.

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.

Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms

Cervical cancer is the most common reproductive malignancy in the female reproductive system. The incidence rate and mortality rate of cervical cancer among women in China are high. In this study, Raman spectroscopy was used to collect tissue sample data from patients with cervicitis, cervical precancerous low-grade lesions, cervical precancerous high-grade lesions, well differentiated squamous cell carcinoma, moderately differentiated squamous cell carcinoma, poorly differentiated squamous cell carcinoma and cervical adenocarcinoma. The collected data were preprocessed using an adaptive iterative reweighted penalized least squares (airPLS) algorithm and derivatives. Convolutional neural network (CNN) and residual neural network (ResNet) classification models were constructed to classify and identify seven types of tissue samples. The attention mechanism efficient channel attention network (ECANet) module and squeeze-and-excitation network (SENet) module were combined with the established CNN and ResNet network models, respectively, to make the models have better diagnostic performance. The results showed that efficient channel attention convolutional neural network (ECACNN) had the best discrimination, and the average accuracy, recall, F1 and AUC values after five cross-validations could reach 94.04%, 94.87%, 94.43% and 96.86%, respectively.

Exploration of utility of combined optical photothermal infrared and Raman imaging for investigating the chemical composition of microcalcifications in breast cancer

Microcalcifications play an important role in cancer detection. They are evaluated by their radiological and histological characteristics but it is challenging to find a link between their morphology, their composition and the nature of a specific type of breast lesion. Whilst there are some mammographic features that are either typically benign or typically malignant often the appearances are indeterminate. Here, we explore a large range of vibrational spectroscopic and multiphoton imaging techniques in order to gain more information about the composition of the microcalcifications. For the first time, we validated the presence of carbonate ions in the microcalcifications by O-PTIR and Raman spectroscopy at the same time, the same location and the same high resolution (0.5 μm). Furthermore, the use of multiphoton imaging allowed us to create stimulated Raman histology (SRH) images which mimic histological images with all chemical information. In conclusion, we established a protocol for efficiently analysing the microcalcifications by iteratively refining the area of interest.

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Analysis of urine using electronic tongue towards non-invasive cancer diagnosis

Electronic tongues (e-tongues) have been broadly employed in monitoring the quality of food, beverage, cosmetics, and pharmaceutical products, and in diagnosis of diseases, as the e-tongues can discriminate samples of high complexity, reduce interference of the matrix, offer rapid response. Compared to other analytical approaches using expensive and complex instrumentation as well as required sample preparation, the e-tongue is non-destructive, miniaturizable and on-site method with little or no preparation of samples. Even though e-tongues are successfully commercialized, their application in cancer diagnosis from urine samples is underestimated. In this review, we would like to highlight the various analytical techniques such as Raman spectroscopy, infrared spectroscopy, fluorescence spectroscopy, and electrochemical methods (potentiometry and voltammetry) used as e-tongues for urine analysis towards non-invasive cancer diagnosis. Besides, different machine learning approaches, for instance, supervised and unsupervised learning algorithms are introduced to analyze extracted chemical data. Finally, capabilities of e-tongues in distinguishing between patients diagnosed with cancer and healthy controls are highlighted.

A new approach for clinical translation of infrared spectroscopy: exploitation of the signature of glioblastoma for general brain tumor recognition

PURPOSE

Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition.

METHODS

Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors.

RESULTS

The model correctly classified 82% (517/628) of specimens as "tumor" or "non-tumor", respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified.

CONCLUSION

The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.

Frequency modulation stimulated Raman scattering scheme for real-time background correction with a single light source

A frequency modulation (FM) scheme for stimulated Raman scattering (SRS) is presented with a single fiber-based light source. Pulse-to-pulse wavelength-switching allows real-time subtraction of parasitic signals leaving only the resonant SRS signal with a noise reduction of up to 30 % compared to digital subtraction schemes, leading effectively to a contrast improvement by a factor of up to 8.3. The wide tuning range of the light source from 1500 cm^-1 to 3000 cm^-1 and the possibility to separately adjust the resonant and the nonresonant wavenumber for every specimen allow to investigate a variety of samples with high contrast and high signal-to-noise ratio, e. g., for medical diagnostics.

Quantitative Determination of Diosmin in Tablets by Infrared and Raman Spectroscopy

Diosmin is widely used in the treatment of chronic venous diseases and hemorrhoids. Based on Raman and infrared reflection spectra of powdered tablets in the mid- and near-infrared regions and results of reference high-performance liquid chromatographic analysis, partial least squares models that enable fast and reliable quantification of the studied active ingredient in tablets, without the need for extraction, were elaborated. Eight commercial preparations containing diosmin in the 66-92% (w/w) range were analyzed. In order to assess and compare the quality of the developed chemometric models, the relative standard errors of prediction for calibration and validation sets were calculated. We found these errors to be in the 1.0-2.4% range for the three spectroscopic techniques used. Diosmin content in the analyzed preparations was obtained with recoveries in the 99.5-100.5% range.

Irradiation Induced Biochemical Changes in Human Mandibular Bone: A Raman Spectroscopic Study

Understanding the biochemical changes in irradiated human mandible after radiotherapy of cancer patients is critical for oral rehabilitation. The underlying mechanism for radiation-associated changes in the bone at the molecular level could lead to implant failure and osteoradionecrosis. The study aimed to assess the chemical composition and bone quality in irradiated human mandibular bone using Raman spectroscopy. A total of 33 bone biopsies from 16 control and 17 irradiated patients were included to quantify different biochemical parameters from the Raman spectra. The differences in bone mineral and matrix band intensities between control and irradiated groups were analyzed using unpaired Student's t-test with statistical significance at p < 0.05. Findings suggest that the intensity of the phosphate band is significantly decreased and the carbonate band is significantly increased in the irradiated group. Further, the mineral crystallinity and carbonate to phosphate ratio are increased. The mineral to matrix ratio is decreased in the irradiated group. Principal component analysis (PCA) based on the local radiation dose and biopsy time interval of irradiated samples did not show any specific classification between irradiation sub-groups. Irradiation disrupted the interaction and bonding between the organic matrix and hydroxyapatite minerals affecting the bone biochemical properties. However, the normal clinical appearance of irradiated bone would have been accompanied by underlying biochemical and microscopical changes which might result in radiation-induced delayed complications.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

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.

Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy

In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.

Illuminating Host-Parasite Interaction at the Cellular and Subcellular Levels with Infrared Microspectroscopy

Toxoplasma gondii (T. gondii) is an opportunistic protozoan that can cause brain infection and other serious health consequences in immuno-compromised individuals. This parasite has a remarkable ability to cross biological barriers and exploit the host cell microenvironment to support its own survival and growth. Recent advances in label-free spectroscopic imaging techniques have made it possible to study biological systems at a high spatial resolution. In this study, we used conventional Fourier-transform infrared (FTIR) microspectroscopy and synchrotron-based FTIR microspectroscopy to analyze the chemical changes that are associated with infection of human brain microvascular endothelial cells (hBMECs) by T. gondii (RH) tachyzoites. Both FTIR microspectroscopic methods showed utility in revealing the chemical alterations in the infected hBMECs. Using a ZnS hemisphere device, to increase the numerical aperture, and the synchrotron source to increase the brightness, we obtained spatially resolved spectra from within a single cell. The spectra extracted from the nucleus and cytosol containing the tachyzoites were clearly distinguished. RNA sequencing analysis of T. gondii-infected and uninfected hBMECs revealed significant changes in the expression of host cell genes and pathways in response to T. gondii infection. These FTIR spectroscopic and transcriptomic findings provide significant insight into the molecular changes that occur in hBMECs during T. gondii infection.

Raman imaging and statistical methods for analysis various type of human brain tumors and breast cancers

Spectroscopic methods provide information on the spatial localization of biochemical components based on the analysis of vibrational spectra. Raman spectroscopy and Raman imaging can be used to analyze various types of human brain tumors and breast cancers. The objective of this study is to evaluate the Raman biomarkers to distinguish tumor types by Raman spectroscopy and Raman imaging. We have demonstrated that bands characteristic for carotenoids (1156 cm^-1, 1520 cm^-1), proteins (1004 cm^-1), fatty acids (1444 cm^-1, 1655 cm^-1) and cytochrome (1585 cm^-1) can be used as universal biomarkers to assess aggressiveness of human brain tumors. The sensitivity and specificity obtained from PLS-DA have been over 73%. Only for gliosarcoma WHO IV the specificity is lower and takes equal 50%. The presented results confirm clinical potential of Raman spectroscopy in oncological diagnostics.

The Ground-Based BIOMEX Experiment Verification Tests for Life Detection on Mars

The success of an astrobiological search for life campaign on Mars, or other planetary bodies in the Solar System, relies on the detectability of past or present microbial life traces, namely, biosignatures. Spectroscopic methods require little or no sample preparation, can be repeated almost endlessly, and can be performed in contact or even remotely. Such methods are therefore ideally suited to use for the detection of biosignatures, which can be confirmed with supporting instrumentation. Here, we discuss the use of Raman and Fourier Transform Infrared (FT-IR) spectroscopies for the detection and characterization of biosignatures from colonies of the fungus Cryomyces antarcticus, grown on Martian analogues and exposed to increasing doses of UV irradiation under dried conditions. The results report significant UV-induced DNA damage, but the non-exceeding of thresholds for allowing DNA amplification and detection, while the spectral properties of the fungal melanin remained unaltered, and pigment detection and identification was achieved via complementary analytical techniques. Finally, this work found that fungal cell wall compounds, likely chitin, were not degraded, and were still detectable even after high UV irradiation doses. The implications for the preservation and detection of biosignatures in extraterrestrial environments are discussed.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Multi-color stimulated Raman scattering with a frame-to-frame wavelength-tunable fiber-based light source

We present multi-color imaging by stimulated Raman scattering (SRS) enabled by an ultrafast fiber-based light source with integrated amplitude modulation and frame-to-frame wavelength tuning. With a relative intensity noise level of -153.7 dBc/Hz at 20.25 MHz the light source is well suited for SRS imaging and outperforms other fiber-based light source concepts for SRS imaging. The light source is tunable in under 5 ms per arbitrary wavelength step between 700 cm^-1 and 3200 cm^-1, which allows for addressing Raman resonances from the fingerprint to the CH-stretch region. Moreover, the compact and environmentally stable system is predestined for fast multi-color assessments of medical or rapidly evolving samples with high chemical specificity, paving the way for diagnostics and sensing outside of specialized laser laboratories.

Fourier-transform infrared microspectroscopy of pulmonary edema fluid for postmortem diagnosis of diabetic ketoacidosis

Determination of the cause of death for diabetic ketoacidosis (DKA), a common and fatal acute complication of diabetes mellitus, is a challenging forensic task owing to the lack of characteristic morphological findings at autopsy. In this study, Fourier-transform infrared (FTIR) microspectroscopy coupled with chemometrics was employed to characterize biochemical differences in pulmonary edema fluid from different causes of death to supplement conventional methods and provide an efficient postmortem diagnosis of DKA. With this aim, FTIR spectra in three different situations (DKA-caused death, other causes of death with diabetes history, and other causes of death without diabetes history) were measured. The results of principal component analysis indicated different spectral profiles between these three groups, which mainly exhibited variations in proteins. Subsequently, two binary classification models were established using an algorithm of partial least squares discriminant analysis (PLS-DA) to determine whether decedents had diabetes and whether the diabetic patients died from DKA. Satisfactory prediction results of PLS-DA models demonstrated good differentiation among these three groups. Therefore, it is feasible to make a postmortem diagnosis of DKA and detect diabetes history via FTIR microspectroscopic analysis of the pulmonary edema fluid.

Imaging and SERS Study of the Au Nanoparticles Interaction with HPV and Carcinogenic Cervical Tissues

In this work, gold NPs were prepared by the Turkevich method, and their interaction with HPV and cancerous cervical tissues were studied by scanning electron microscopy, energy-dispersive x-ray spectroscopy, confocal and multiphoton microscopy and SERS. The SEM images confirmed the presence and localization of the gold NPs inside of the two kinds of tissues. The light absorption of the gold NPs was at 520 nm. However, it was possible to obtain two-photon imaging (red emission region) of the gold NPs inside of the tissue, exciting the samples at 900 nm, observing the morphology of the tissues. The infrared absorption was probably due to the aggregation of gold NPs inside the tissues. Therefore, through the interaction of gold nanoparticles with the HPV and cancerous cervical tissues, a surface enhanced Raman spectroscopy (SERS) was obtained. As preliminary studies, having an average of 1000 Raman spectra per tissue, SERS signals showed changes between the HPV-infected and the carcinogenic tissues; these spectral signatures occurred mainly in the DNA bands, potentially offering a tool for the rapid screening of cancer.

The Search for Biomarkers in Fibromyalgia

Fibromyalgia is the most common of the central sensitivity syndromes affecting 2–5% of the adult population in the United States. This pain amplification syndrome has enormous societal impact as measured by work absenteeism, decreased work productivity, disability and injury compensation and over-utilization of healthcare resources. Multiple studies have shown that early diagnosis of this condition can improve patient outlook and redirect valuable healthcare resources towards more appropriate targeted therapy. Efforts have been made towards improving diagnostic accuracy through updated criteria. The search for biomarkers for diagnosis and verification of Fibromyalgia is an ongoing process. Inadequacies with current diagnostic criteria for this condition have fueled these efforts for identification of a reproducible marker that can verify this disease in a highly sensitive, specific and reproducible manner. This review focuses on areas of research for biomarkers in fibromyalgia and suggests that future efforts might benefit from approaches that utilize arrays of biomarkers to identify this disorder that presents with a diverse clinical phenotype.

[Spectroscopic investigation of the middle ear mucosa]

BACKGROUND

The middle ear mucosa (MEM) plays a central role in the middle ear due to its function of providing regular ventilation. To date, assessment of the state of the MEM is only possible subjectively by the surgeon. An objective characterization of the state of the MEM is desirable.

OBJECTIVE

The aim of this study was to enable objective characterization of the MEM and test infrared (IR) spectroscopy as a possible diagnostic tool for clinical use.

MATERIALS AND METHODS

During middle ear surgery, 48 MEM samples were collected and divided into four groups according to clinical appearance: group I: normal MEM; group II: sclerotic MEM; group III: inflammatory thickened MEM; group IV: granulated MEM. After collection, samples were analyzed by IR spectroscopy to identify characteristic IR spectra.

RESULTS

In the supervised analysis of the selected images, the biochemical differences representing the decisive factors for classification into groups I to IV were characterized. The differences in amide bands, carbohydrates, lipids, and proteins permit reliable separation of the clinical categories.

CONCLUSION

Spectroscopic investigations enable objective characterization of the MEM. Conclusions regarding biochemical differences make it possible to weigh up treatment options. Routine use of IR spectroscopy in the operating theater requires histopathological comparison and an extended dataset with reference values of the individual groups.

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.

Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper

SIGNIFICANCE

Hyperspectral imaging (HSI) is an emerging optical technique that has a double function of spectroscopy and imaging.

AIM

Near-infrared hyperspectral imaging (NIR-HSI) (900 to 1700 nm) with the help of chemometrics was investigated for gastric cancer diagnosis.

APPROACH

Mean spectra and standard deviation of normal and cancerous pixels were extracted. Principal component analysis (PCA) was used to compress the dimension of hypercube data and select the optimal wavelengths. Moreover, spectral angle mapper (SAM) was utilized as chemometrics to discriminate gastric cancer from normal.

RESULTS

Major spectral difference of cancerous and normal gastric tissue was observed around 975, 1215, and 1450 nm by comparison. A total of six wavelengths (i.e., 975, 1075, 1215, 1275, 1390, and 1450 nm) were then selected as optimal wavelengths by PCA. The accuracy using SAM is up to 90% according to hematoxylin-eosin results.

CONCLUSIONS

These results suggest that NIR-HSI has the potential as a cutting-edge optical diagnostic technique for gastric cancer diagnosis with suitable chemometrics.

Comparison between high definition FT-IR, Raman and AFM-IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones, especially in the case of demanding biological samples. Three vibrational spectroscopy techniques (high definition Fourier-transform infrared [FT-IR], Raman and atomic force microscopy infrared [AFM-IR]) were applied for subcellular chemical imaging of cholesteryl esters in PC-3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern and chemical information. All tested techniques were found to be useful in chemical imaging of cholesterol derivatives in cancer cells. The results obtained from FT-IR and Raman imaging showed to be comparable, whereas those achieved from AFM-IR study exhibited higher spectral heterogeneity. It confirms AFM-IR method as a powerful tool in local chemical imaging of cells at the nanoscale level. Furthermore, due to polarization effect, p-polarized AFM-IR spectra showed strong enhancement of lipid bands when compared to FT-IR.

Sensitivity analysis of Raman endoscopy with and without wavefront shaping

Vibrational spectroscopy is a powerful method for the label-free identification of molecules. Spontaneous Raman spectroscopy integrated with an ultra-thin fiber-based endoscope can provide remote, local, and minimally invasive chemical analysis in many fields from biomedical diagnostics to the materials industry. Miniaturization of the probe in combination with a large field of view (FOV) and high sensitivity would be beneficial for a broad class of applications. Here we quantitatively analyze signal-to-noise ratio (SNR) and the sensitivity improvement due to wavefront shaping. We show that wavefront shaping in an ultra-thin single-fiber probe allows to decrease the total measurements time up to several orders of magnitude even without any prior knowledge of the Raman particle location. Such a fiber probe is well suited for minimally-invasive endoscopy in biological and medical applications.

Spectroscopic Methods Used in Implant Material Studies

It is recognized that interactions between most materials are governed by their surface properties and manifest themselves at the interface formed between them. To gain more insight into this thin layer, several methods have been deployed. Among them, spectroscopic methods have been thoroughly evaluated. Due to their exceptional sensitivity, data acquisition speed, and broad material tolerance they have been proven to be invaluable tools for surface analysis, used by scientists in many fields, for example, implant studies. Today, in modern medicine the use of implants is considered standard practice. The past two decades of constant development has established the importance of implants in dentistry, orthopedics, as well as extended their applications to other areas such as aesthetic medicine. Fundamental to the success of implants is the knowledge of the biological processes involved in interactions between an implant and its host tissue, which are directly connected to the type of implant material and its surface properties. This review aims to demonstrate the broad applications of spectroscopic methods in implant material studies, particularly discussing hard implants, surface composition studies, and surface-cell interactions.

Raman Spectroscopy: Guiding Light for the Extracellular Matrix

The extracellular matrix (ECM) consists of a complex mesh of proteins, glycoproteins, and glycosaminoglycans, and is essential for maintaining the integrity and function of biological tissues. Imaging and biomolecular characterization of the ECM is critical for understanding disease onset and for the development of novel, disease-modifying therapeutics. Recently, there has been a growing interest in the use of Raman spectroscopy to characterize the ECM. Raman spectroscopy is a label-free vibrational technique that offers unique insights into the structure and composition of tissues and cells at the molecular level. This technique can be applied across a broad range of ECM imaging applications, which encompass in vitro, ex vivo, and in vivo analysis. State-of-the-art confocal Raman microscopy imaging now enables label-free assessments of the ECM structure and composition in tissue sections with a remarkably high degree of biomolecular specificity. Further, novel fiber-optic instrumentation has opened up for clinical in vivo ECM diagnostic measurements across a range of tissue systems. A palette of advanced computational methods based on multivariate statistics, spectral unmixing, and machine learning can be applied to Raman data, allowing for the extraction of specific biochemical information of the ECM. Here, we review Raman spectroscopy techniques for ECM characterizations over a variety of exciting applications and tissue systems, including native tissue assessments (bone, cartilage, cardiovascular), regenerative medicine quality assessments, and diagnostics of disease states. We further discuss the challenges in the widespread adoption of Raman spectroscopy in biomedicine. The results of the latest discovery-driven Raman studies are summarized, illustrating the current and potential future applications of Raman spectroscopy in biomedicine.

In vivo Tracking of DNA for Precise Determination of the Stratum Corneum Thickness and Superficial Microbiome Using Confocal Raman Microscopy

The skin barrier function is mostly provided by the stratum corneum (SC), the uppermost layer of the epidermis. To noninvasively analyze the physiological properties of the skin barrier functionin vivo, it is important to determine the SC thickness. Confocal Raman microscopy (CRM) is widely used for this task. In the present in vivo study, a new method based on the determination of the DNA concentration profile using CRM is introduced for determining the SC thickness. The obtained SC thickness values are compared with those obtained using other CRM-based methods determining the water and lipid depth profiles. The obtained results show almost no significant differences in SC thickness for the utilized methods. Therefore, the results indicate that it is possible to calculate the SC thickness by using the DNA profile in the fingerprint region, which is comparable with the SC thickness calculated by the water depth profiles (ANOVA test p = 0.77) and the lipid depth profile (ANOVA test p = 0.74). This provides the possibility to measure the SC thickness by using the DNA profile, in case the water or lipid profile analyses are influenced by a topically applied formulation. The increase in DNA concentration in the superficial SC (0-2 µm) is related to the DNA presence in the microbiome of the skin, which was not present in the SC depth below 4 µm.

Raman Spectroscopy for Rapid Evaluation of Surgical Margins during Breast Cancer Lumpectomy

Failure to precisely distinguish malignant from healthy tissue has severe implications for breast cancer surgical outcomes. Clinical prognoses depend on precisely distinguishing healthy from malignant tissue during surgery. Laser Raman spectroscopy (LRS) has been previously shown to differentiate benign from malignant tissue in real time. However, the cost, assembly effort, and technical expertise needed for construction and implementation of the technique have prohibited widespread adoption. Recently, Raman spectrometers have been developed for non-medical uses and have become commercially available and affordable. Here we demonstrate that this current generation of Raman spectrometers can readily identify cancer in breast surgical specimens. We evaluated two commercially available, portable, near-infrared Raman systems operating at excitation wavelengths of either 785 nm or 1064 nm, collecting a total of 164 Raman spectra from cancerous, benign, and transitional regions of resected breast tissue from six patients undergoing mastectomy. The spectra were classified using standard multivariate statistical techniques. We identified a minimal set of spectral bands sufficient to reliably distinguish between healthy and malignant tissue using either the 1064 nm or 785 nm system. Our results indicate that current generation Raman spectrometers can be used as a rapid diagnostic technique distinguishing benign from malignant tissue during surgery.

FTIR bio-spectroscopy scattering correction using natural biological characteristics of different cell lines

Fourier transform infrared (FTIR) spectroscopy is a well-known method of analysis, with various applications, including promising potential for analyzing biological samples. In the bio-spectroscopy of cells, Mie scattering may increase, which then causes spectral distortion, due to the similarity of cell size with the IR medium-wavelength. These changes make the spectrum unreliable. In previous scattering elimination studies, questionable estimations were considered. For instance, all cells were considered as spherical objects or cell size was estimated randomly. In an attempt to provide the best equation based on the natural existence of cells for the FTIR Mie scattering correction, we examined the actual biological data of cells - as opposed to those yielded from mathematical manipulations. So five biological factors: cell size, shape, granularity, circularity, and edge irregularities, for each cell line were considered as factors which cause scattering. For measuring cell size, roundness and edge irregularity, microscopy images were obtained and processed. For evaluating cell line granularity, flow cytometry was used. Finally, by including these factors, an algorithm was designed. To assess the accuracy of the proposed algorithm, the trypsinized cell spectrum was considered as the high scattering spectrum. Cells were also cultured on a MirrIR slide, and their ATR-FTIR spectrum was considered as the minimum scattering spectrum. The algorithm using the abovementioned five characteristics was used for 13 different cell lines, and in some cases the corrected spectrum demonstrated more than 97% resemblance with the ATR spectra of the same cells. A comparison between the results of this algorithm with the Bassan et al. (2017) algorithm for scattering correction that is freely available on the Internet was then conducted on two different cell lines, clearly showing the advantages of our algorithm, in terms of accuracy and precision. Therefore, this method can be viewed as a more suitable solution for scattering correction in cell investigations.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

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

Portable all-fiber dual-output widely tunable light source for coherent Raman imaging

We present a rapidly tunable dual-output all-fiber light source for coherent Raman imaging, based on a dispersively matched mode-locked laser pumping a parametric oscillator. Output pump and Stokes pulses with a maximal power of 170 and 400 mW, respectively, and equal durations of 7 ps could be generated. The tuning mechanism required no mechanical delay line, enabling all-electronic arbitrary wavelength switching across more than 2700  cm - 1 in less than 5 ms. The compact setup showed a reliable operation despite mechanical shocks of more than 25  m / s 2 and is, thus, well suited for operation in a mobile cart. Imaging mouse and human skin tissue with both the portable light source and a commercial laboratory-bound reference system yielded qualitatively equal results and verified the portable light source being well suited for coherent Raman microscopy.

Postmortem diagnosis of fatal hypothermia/hyperthermia by spectrochemical analysis of plasma

Postmortem diagnosis of extreme-weather-related deaths is a challenging forensic task. Here, we present a state-of-the-art study that employed attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy in combination with Chemometrics for postmortem diagnosis of fatal hypothermia/hyperthermia by biochemical investigation of plasma in rats. The results of principal component analysis (PCA) and spectral analysis revealed that plasma samples from the fatal hypothermia, fatal hyperthermia, and control groups, are substantially different from each other based on the spectral variations associated with the lipid, carbohydrate and nucleic acid components. Two partial least squares-discriminant analysis (PLS-DA) classification models (hypothermia-nonhypothermia and hyperthermia-nonhyperthermia binary models) with a 100% accuracy rate were constructed. Subsequently, internal cross-validation was performed to assess the robustness of these two models, which resulted in 98.1 and 100% accuracy. Ultimately, classification predictions of 42 unknown plasma samples were performed by these two models, and both models achieved 100% accuracy. Additionally, our results demonstrated that hemolysis and postmortem hypothermic/hyperthermic effects did not weaken the prediction ability of these two classification models. In summary, this work demonstrates ATR-FTIR spectroscopy's great potential for postmortem diagnosis of fatal hypothermia/hyperthermia.

A roadmap for the clinical implementation of optical-imaging biomarkers

Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrett's oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical-imaging technologies.

FT Raman spectroscopy in the evaluation of biomarkers of normal and pathological placenta tissue

The basic precondition of proper intrauterine growth is appropriate supply of nutrients transported through placenta. Placenta capacity in the scope of transportation is dependent on transport systems and the structure of the basement membrane and syncytiotrophoblast microvillous membrane. The present pilot study demonstrates preliminary results of the analysis of placenta structure in the course of selected pathologies by FT Raman spectroscopy analysis. The observed changes of the molecular structure in the so-called average spectra, independent of methodical processing, may be an indicator of the efficiency of transportation controlled by syncytiotrophoblast. In particular, an increase in the intensity of dispersion and transfer within the frequency of 3425-3300 cm^-1 demonstrate the dynamics of the interaction in the scope of hydrogen bonds in healthy tissues. Changes in the molecular structure within the frequency of 950-750 cm^-1 and conformational changes within disulphide bonds differentiate the healthy tissue from the pathological one. Changes in the molecular structure observed in the FTR spectra are a spectroscopic image of placenta functions in the course of various pathologies. They also document a complex goal of our research that is finding spectroscopic biomarkers of regular and pathological placental tissue.

Evaluating the effects of causes of death on postmortem interval estimation by ATR-FTIR spectroscopy

Estimating postmortem interval (PMI) is one of the most challenging tasks in forensic practice due to the effects of many factors. Here, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was utilized to evaluate the effects of causes of death when estimating PMI and to establish a partial least square (PLS) regression model, which can precisely predict PMI under different causes of death. First, the sensitivities to causes of death (brainstem injury, mechanical asphyxia, and hemorrhage shock) of seven kinds of organs were evaluated based on their degrees of cohesion and separation. Then, the liver was selected as the most sensitive organ to establish a PMI estimation model to compare the predicted deviations from different causes of death. It turns out that the cause of death has no significant effect on estimating PMI. Next, a PLS regression model was built with kidney tissues, which have the lowest sensitivity, and this model showed a satisfactory predictive ability and wide applicability. This study demonstrates the feasibility of using ATR-FTIR spectroscopy in conjunction with chemometrics as a powerful alternative for detecting changes in biochemistry and estimating PMI. A new perspective was also provided for evaluating the effect of causes of death when predicting PMI.