Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients

Inverted Transflection Spectroscopy of Live Cells Using Metallic Grating on Elevated Nanopillars

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes-including live biological cells-that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: ATR prisms are difficult to integrate with multiwell cell culture workflows, while metasurfaces suffer from a limited spectral range and small penetration depth into analytes. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated on top of elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as inverted transflection spectroscopy (ITS). The analytic power of ITS is established by measuring a wide range of protein concentrations in solution, with the limit of detection in the single-digit mg mL-1. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is used to characterize their adhesion kinetic.

Advances in far-infrared research: therapeutic mechanisms of disease and application in cancer detection

Far infrared (FIR) irradiation is commonly used as a convenient, non-contact, non-invasive treatment for diseases such as myocardial ischemia, diabetes, and chronic kidney disease. In this review, we focus on reviewing the potential therapeutic mechanisms of FIR and its cutting-edge applications in cancer detection. Firstly, we searched the relevant literature in the last decade for systematic screening and briefly summarized the biophysical properties of FIR. We then focused on the possible mechanisms of FIR in wound healing, cardiovascular diseases, and other chronic diseases. In addition, we review recent applications of FIR in cancer detection, where Fourier transform infrared spectroscopy and infrared thermography provide additional diagnostic methods for the medical diagnosis of cancer. Finally, we conclude and look into the future development of FIR for disease treatment and cancer detection. As a high-frequency non-ionizing wave, FIR has the advantages of safety, convenience, and low cost. We hope that this review can provide biological information reference and relevant data support for those who are interested in FIR and related high-frequency non-ionizing waves, to promote the further application of FIR in the biomedical field.

Differentiation of Salivary Gland and Salivary Gland Tumor Tissue via Raman Imaging Combined with Multivariate Data Analysis

Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.

Inverted transflection spectroscopy of live cells using metallic grating on elevated nanopillars

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes - including live biological cells - that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: high cost, incompatibility with standard cell culture workflows, limited spectral range, and small penetration depth into the analyte. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated atop elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as Inverted Transflection Spectroscopy (ITS). We demonstrate the analytic power of ITS by measuring protein concentrations in solution. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is also exploited to characterize their adhesion kinetics.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Label-Free Physical-Analytical Techniques Reveal Epigenetic Modifications of Breast Cancer Chromosomes

Epigenetic dysregulation including DNA methylation and histone modifications is being increasingly recognized as a promising biomarker for the diagnosis and prognosis of cancer. Herein, we devised a label-free analytical toolbox comprising IR, UV-vis, CD spectroscopy, and cyclic voltammetry, which is capable to differentiate significantly hyper-methylated breast cancer chromosomes from the normal breast epithelial counterparts.

The Impact of Tissue Preparation on Salivary Gland Tumors Investigated by Fourier-Transform Infrared Microspectroscopy

Due to the wide variety of benign and malignant salivary gland tumors, classification and malignant behavior determination based on histomorphological criteria can be difficult and sometimes impossible. Spectroscopical procedures can acquire molecular biological information without destroying the tissue within the measurement processes. Since several tissue preparation procedures exist, our study investigated the impact of these preparations on the chemical composition of healthy and tumorous salivary gland tissue by Fourier-transform infrared (FTIR) microspectroscopy. Sequential tissue cross-sections were prepared from native, formalin-fixed and formalin-fixed paraffin-embedded (FFPE) tissue and analyzed. The FFPE cross-sections were dewaxed and remeasured. By using principal component analysis (PCA) combined with a discriminant analysis (DA), robust models for the distinction of sample preparations were built individually for each parotid tissue type. As a result, the PCA-DA model evaluation showed a high similarity between native and formalin-fixed tissues based on their chemical composition. Thus, formalin-fixed tissues are highly representative of the native samples and facilitate a transfer from scientific laboratory analysis into the clinical routine due to their robust nature. Furthermore, the dewaxing of the cross-sections entails the loss of molecular information. Our study successfully demonstrated how FTIR microspectroscopy can be used as a powerful tool within existing clinical workflows.

Differentiation of Occlusal Discolorations and Carious Lesions with Hyperspectral Imaging In Vitro

Stains and stained incipient lesions can be challenging to differentiate with established clinical tools. New diagnostic techniques are required for improved distinction to enable early noninvasive treatment. This in vitro study evaluates the performance of artificial intelligence (AI)-based classification of hyperspectral imaging data for early occlusal lesion detection and differentiation from stains. Sixty-five extracted permanent human maxillary and mandibular bicuspids and molars (International Caries Detection and Assessment System [ICDAS] II 0–4) were imaged with a hyperspectral camera (Diaspective Vision TIVITA® Tissue, Diaspective Vision, Pepelow, Germany) at a distance of 350 mm, acquiring spatial and spectral information in the wavelength range 505–1000 nm; 650 fissural spectra were used to train classification algorithms (models) for automated distinction between stained but sound enamel and stained lesions. Stratified 10-fold cross-validation was used. The model with the highest classification performance, a fine k-nearest neighbor classification algorithm, was used to classify five additional tooth fissural areas. Polarization microscopy of ground sections served as reference. Compared to stained lesions, stained intact enamel showed higher reflectance in the wavelength range 525–710 nm but lower reflectance in the wavelength range 710–1000 nm. A fine k-nearest neighbor classification algorithm achieved the highest performance with a Matthews correlation coefficient (MCC) of 0.75, a sensitivity of 0.95 and a specificity of 0.80 when distinguishing between intact stained and stained lesion spectra. The superposition of color-coded classification results on further tooth occlusal projections enabled qualitative assessment of the entire fissure’s enamel health. AI-based evaluation of hyperspectral images is highly promising as a complementary method to visual and radiographic examination for early occlusal lesion detection.

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

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

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

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

Grade diagnosis of human glioma using Fourier transform infrared microscopy and artificial neural network

The World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, and the result interpretation highly relies on the pathologists, making the process time-consuming (>60 min, including the steps of dewaxing to water and H&E staining), resource-wasting, and labor-intensive. In the present study, we report an alternative workflow that combines the Fourier transform infrared (FTIR) microscopy and artificial neural network (ANN) to diagnose the grade of human glioma in a way that is faster (~20 min, including the processes of sample dewaxing, spectra acquisition and analysis), accurate (the prediction accuracy, specificity and sensitivity can reach above 99%), and without reagent. Moreover, this method is much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). The ANN mainly learned the characteristic region of 800-1800 cm^-1 to classify the major histopathologic classes of human glioma. These results demonstrate that the grade diagnosis of human glioma by FTIR microscopy plus ANN can be streamlined, and could serve as a complementary pathway that is independent of the traditional pathology laboratory.

Diagnostic segregation of human breast tumours using Fourier-transform infrared spectroscopy coupled with multivariate analysis: Classifying cancer subtypes

The present study aimed to investigate whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with multivariate analysis could be applied to discriminate and classify among breast tumour molecular subtypes based on the unique spectral "fingerprints" of their biochemical composition. The different breast cancer tissues and normal breast tissues were collected and identified by pathology and ATR-FTIR spectroscopy respectively. The study indicates that the levels of the lipid-to-protein, nucleic acid-to-lipid, phosphate-to-carbohydrate and their secondary structure ratio, including RNA-to-DNA, Amide I-to-Amide II, and RNA-to-lipid ratios were significantly altered among the molecular subtype of breast tumour compared with normal breast tissues, which helps explain the changes in the biochemical structure of different molecular phenotypes of breast cancer. Tentatively-assigned characteristic peak ratios of infrared (IR) spectra reflect the changes of the macromolecule structure in different issues to a great extent and can be used as a potential biomarker to predict the molecular subtype of breast tumour. The present study acts as the first case study to show the successful application of IR spectroscopy in classifying subtypes of breast cancer with biochemical alterations. Therefore, the present study is likely to help to provide a new diagnostic approach for the accurate diagnosis of breast tumours and differential molecular subtypes and has the potential to be used for further intraoperative management.

Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer

Application software is utilized to aid in the diagnosis of breast cancer. Yet, recent advances in artificial intelligence (AI) are addressing challenges related to the detection, classification, and monitoring of different types of tumors. AI can apply deep learning algorithms to perform automated analysis on mammographic or histologic examinations. Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning. This enables fast evaluation of every tissue patch on an image, resulting in a quicker more sensitivity, and more reproducible diagnoses compared to human performance. On the other hand, cancer cell-exosomes which are extracellular vesicles released by cancer cells into the blood circulation, are being explored as cancer biomarker. Recent studies on cancer-exosome-content revealed that the encapsulated miRNA and other biomolecules are indicative of tumor sub-type, possible metastasis and prognosis. Thus, theoretically, through nanogenomicas, a profile of each breast tumor sub-type, estrogen receptor status, and potential metastasis site can be constructed. Then, a laboratory instrument, fitted with an AI program, can be used to diagnose suspected patients by matching their sera miRNA and biomolecules composition with the available template profiles. In this paper, we discuss the advantages of establishing a nanogenomics-AI-based breast cancer diagnostic approach, compared to the gold standard radiology or histology based approaches that are currently being adapted to AI. Also, we discuss the advantages of building the diagnostic and prognostic biomolecular profiles for breast cancers based on the exosome encapsulated content, rather than the free circulating miRNA and other biomolecules.

Concurrent Vibrational Circular Dichroism Measurements with Infrared Spectroscopic Imaging

Vibrational circular dichroism (VCD) spectroscopy has emerged as a powerful platform to quantify chirality, a vital biological property that performs a pivotal role in the metabolism of life organisms. With a photoelastic modulator (PEM) integrated into an infrared spectrometer, the differential response of a sample to the direction of circularly polarized light can be used to infer conformation handedness. However, these optical components inherently exhibit chromatic behavior and are typically optimized at discrete spectral frequencies. Advancements of discrete frequency infrared (DFIR) spectroscopic microscopes in spectral image quality and data throughput are promising for use toward analytical VCD measurements. Utilizing the PEM advantages incorporated into a custom-built QCL microscope, we demonstrate a point scanning VCD instrument capable of acquiring spectra rapidly across all fingerprint region wavelengths in transmission configuration. Moreover, for the first time, we also demonstrate the VCD imaging performance of our instrument for site-specific chirality mapping of biological tissue samples. This study offers some insight into future possibilities of examining small, localized changes in tissue that have major implications for systemic diseases and their progression, while also laying the groundwork for additional modeling and validation in advancing the capability of VCD spectroscopy and imaging.

The potential of terahertz sensing for cancer diagnosis

The terahertz (THz) region lies between the microwave and infrared regions of the electromagnetic (EM) spectrum such that it is strongly attenuated by water and very sensitive to water content. Here, we numerically present what is to our knowledge the detecting system based on THz reflectance spectral responses data in the diagnosis of in vivo and ex vivo of some cancer's samples such as skin, breast and colon cancer tissue samples. The numerical analysis on the use of semiconductor metamaterial design/device as a complex refractive index (CRI) biosensor have been carried out. We demonstrate the application of terahertz pulse detecting (TPD) in reflection geometry for the study of normal and cancerous biological tissues. THz radiation has very low photon energy and thus it does not pose any ionization hazard for biological tissues. The sensitivity of THz radiation to polar molecules, such as water, makes TPD suitable to study the diseases in human body. By studying the THz pulse shape in the time domain, we have been able to differentiate between diseased and normal tissue for the study of basal cell carcinoma (BCC), breast and colon cancers. These results demonstrate the potential of TPD for the study of skin tissue and its related disorders, both in vivo and ex vivo. Findings of this study demonstrate the potential of TPD to depict breast and colon cancers and both in vivo and ex vivo of skin cancer and encourage further studies to determine the sensitivity and specificity of the technique.

ATR-FTIR spectroscopy as adjunct method to the microscopic examination of hematoxylin and eosin-stained tissues in diagnosing lung cancer

Lung cancer remains the leading cause of cancer-related death worldwide. Since prognosis and treatment outcomes rely on fast and accurate diagnosis, there is a need for more cost-effective, sensitive, and specific method for lung cancer detection. Thus, this study aimed to determine the ability of ATR-FTIR in discriminating malignant from benign lung tissues and evaluate its concordance with H&E staining. Three (3) 5μm-thick sections were cut from formalin fixed paraffin embedded (FFPE) cell or tissue blocks from patients with lung lesions. The outer sections were H&E-stained and sent to two (2) pathologists to confirm the histopathologic diagnosis. The inner section was deparaffinized by standard xylene method and then subjected to ATR-FTIR analysis. Distinct spectral profiles that distinguished (p<0.05) one sample from another, called the "fingerprint region", were observed in five (5) peak patterns representing the amides, lipids, and nucleic acids. Principal component analysis and hierarchical cluster analysis evidently clustered the benign from malignant tissues. ATR-FTIR showed 97.73% sensitivity, 92.45% specificity, 94.85% accuracy, 91.49% positive predictive value and 98.00% negative predictive value in discriminating benign from malignant lung tissue. Further, strong agreement was observed between histopathologic readings and ATR-FTIR analysis. This study shows the potential of ATR-FTIR spectroscopy as a potential adjunct method to the gold standard, the microscopic examination of hematoxylin and eosin (H&E)-stained tissues, in diagnosing lung cancer.

Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis

Pectobacterium and Dickeya spp. are soft rot Pectobacteriaceae that cause aggressive diseases on agricultural crops leading to substantial economic losses. The accurate, rapid and low-cost detection of these pathogenic bacteria are very important for controlling their spread, reducing the consequent financial loss and for producing uninfected potato seed tubers for future generations. Currently used methods for the identification of these bacterial pathogens at the strain level are based mainly on molecular techniques, which are expensive. We used an alternative method, infrared spectroscopy, to measure 24 strains of five species of Pectobacterium and Dickeya. Measurements were then analyzed using machine learning methods to differentiate among them at the genus, species and strain levels. Our results show that it is possible to differentiate among different bacterial pathogens with a success rate of ~99% at the genus and species levels and with a success rate of over 94% at the strain level.

Potential of infrared microscopy to differentiate between dementia with Lewy bodies and Alzheimer's diseases using peripheral blood samples and machine learning algorithms

SIGNIFICANCE

Accurate and objective identification of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is of major clinical importance due to the current lack of low-cost and noninvasive diagnostic tools to differentiate between the two. Developing an approach for such identification can have a great impact in the field of dementia diseases as it would offer physicians a routine objective test to support their diagnoses. The problem is especially acute because these two dementias have some common symptoms and characteristics, which can lead to misdiagnosis of DLB as AD and vice versa, mainly at their early stages.

AIM

The aim is to evaluate the potential of mid-infrared (IR) spectroscopy in tandem with machine learning algorithms as a sensitive method to detect minor changes in the biochemical structures that accompany the development of AD and DLB based on a simple peripheral blood test, thus improving the diagnostic accuracy of differentiation between DLB and AD.

APPROACH

IR microspectroscopy was used to examine white blood cells and plasma isolated from 56 individuals: 26 controls, 20 AD patients, and 10 DLB patients. The measured spectra were analyzed via machine learning.

RESULTS

Our encouraging results show that it is possible to differentiate between dementia (AD and DLB) and controls with an ∼86  %   success rate and between DLB and AD patients with a success rate of better than 93%.

CONCLUSIONS

The success of this method makes it possible to suggest a new, simple, and powerful tool for the mental health professional, with the potential to improve the reliability and objectivity of diagnoses of both AD and DLB.

Application of metasurface-enhanced infra-red spectroscopy to distinguish between normal and cancerous cell types

Fourier transform infrared (FTIR) spectra of biological cells can reveal clinically important information about cells' composition, including their normal or cancerous status. The recently emerged diagnostic technique of spectral cytopathology (SCP) combines FTIR with multivariate statistical analysis to detect cell abnormalities, differentiate between cell types, and monitor disease progression. We demonstrate a new variant of SCP, a metasurface-enhanced infrared reflection spectroscopic cytopathology (MEIRSC) that utilises judiciously designed plasmonic metasurfaces to localize and enhance the evanescent field near the cell's membrane, and to carry out spectroscopic interrogations of the cells attached to the metasurface using reflected infrared light. Our findings indicate that the MEIRSC approach enables us to differentiate between normal and cancerous human colon cells. The sensitivity of MEIRSC is such that a very small (about 50 nm deep) portion of the cell can yield valuable diagnostic information.

Early diagnosis of Alzheimer's disease using infrared spectroscopy of isolated blood samples followed by multivariate analyses

Alzheimer's disease (AD) is the most common cause of dementia, particularly in the elderly. The disease is characterized by cognitive decline that typically starts with insidious memory loss and progresses relentlessly to produce global impairment of all higher cortical functions. Due to better living conditions and health facilities in developed countries, which result in higher overall life spans, these countries report upward trends of AD among their populations. There are, however, no specific diagnostic tests for AD and clinical diagnosis is especially difficult in the earliest stages of the disease. Early diagnosis of AD is frequently subjective and is determined by physicians (generally neurologists, geriatricians, and psychiatrists) depending on their experience. Diagnosing AD requires both medical history and mental status testing. Having trouble with memory does not mean you have AD. AD has no current cure, but treatments for symptoms are available and research continues. In this study, we investigated the potential of infrared microscopy to differentiate between AD patients and controls, using Fourier transform infrared (FTIR) spectroscopy of isolated blood components. FTIR is known as a quick, safe, and minimally invasive method to investigate biological samples. For this goal, we measured infrared spectra from white blood cells (WBCs) and plasma taken from AD patients and controls, with the consent of the patients or their guardians. Applying multivariate analysis, principal component analysis (PCA) followed by linear discriminant analysis (LDA), it was possible to differentiate among the different types of mild, moderate, and severe AD, and the controls, with 85% accuracy when using the WBC spectra and about 77% when using the plasma spectra. When only the moderate and severe stages were included, an 83% accuracy was obtained using the WBC spectra and about 89% when using the plasma spectra.

Serum-based diagnostic prediction of oral submucous fibrosis using FTIR spectrometry

Oral submucous fibrosis (OSF) is found to have the highest malignant potentiality among all other pre-cancerous lesions. However, its detection prior to tissue biopsy can be challenging in clinics. Moreover, biopsy examination is invasive and painful. Hence, there is an urgent need of new technology that facilitates accurate diagnostic prediction of OSF prior to biopsy. Here, we used FTIR spectroscopy coupled with chemometric techniques to distinguish the serum metabolic signatures of OSF patients (n=30) and healthy controls (n=30). Serum biochemical analyses have been performed to further support the FTIR findings. Absorbance intensities of 45 infrared wavenumbers differed significantly between OSF and normal serum FTIR spectra representing alterations in carbohydrates, proteins, lipids and nucleic acids. Nineteen prominent significant wavenumbers (P≤0.001) at 1020, 1025, 1035, 1039, 1045, 1078, 1055, 1100, 1117, 1122, 1151, 1169, 1243, 1313, 1398, 1453, 1544, 1650 and 1725cm^-1 provided excellent segregation of OSF spectra from normal using multivariate statistical techniques. These findings provided essential information on the metabolic features of blood serum of OSF patients and established that FTIR spectroscopy coupled with chemometric analysis can be potentially useful in the rapid and accurate preoperative screening/diagnosis of OSF.

Using Infrared Spectroscopy and Multivariate Analysis to Detect Antibiotics' Resistant Escherichia coli Bacteria

Bacterial pathogens are one of the primary causes of human morbidity worldwide. Historically, antibiotics have been highly effective against most bacterial pathogens; however, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Early and rapid determination of bacterial susceptibility to antibiotics has become essential in many clinical settings and, sometimes, can save lives. Currently classical procedures require at least 48 h for determining bacterial susceptibility, which can constitute a life-threatening delay for effective treatment. Infrared (IR) microscopy is a rapid and inexpensive technique, which has been used successfully for the detection and identification of various biological samples; nonetheless, its true potential in routine clinical diagnosis has not yet been established. In this study, we evaluated the potential of this technique for rapid identification of bacterial susceptibility to specific antibiotics based on the IR spectra of the bacteria. IR spectroscopy was conducted on bacterial colonies, obtained after 24 h culture from patients' samples. An IR microscope was utilized, and a computational classification method was developed to analyze the IR spectra by novel pattern-recognition and statistical tools, to determine E. coli susceptibility within a few minutes to different antibiotics, gentamicin, ceftazidime, nitrofurantoin, nalidixic acid, ofloxacin. Our results show that it was possible to classify the tested bacteria into sensitive and resistant types, with success rates as high as 85% for a number of examined antibiotics. These promising results open the potential of this technique for faster determination of bacterial susceptibility to certain antibiotics.

Raman microspectroscopy of nucleus and cytoplasm for human colon cancer diagnosis

Subcellular Raman analysis is a promising clinic tool for cancer diagnosis, but constrained by the difficulty of deciphering subcellular spectra in actual human tissues. We report a label-free subcellular Raman analysis for use in cancer diagnosis that integrates subcellular signature spectra by subtracting cytoplasm from nucleus spectra (Nuc.-Cyt.) with a partial least squares-discriminant analysis (PLS-DA) model. Raman mapping with the classical least-squares (CLS) model allowed direct visualization of the distribution of the cytoplasm and nucleus. The PLS-DA model was employed to evaluate the diagnostic performance of five types of spectral datasets, including non-selective, nucleus, cytoplasm, ratio of nucleus to cytoplasm (Nuc./Cyt.), and nucleus minus cytoplasm (Nuc.-Cyt.), resulting in diagnostic sensitivity of 88.3%, 84.0%, 98.4%, 84.5%, and 98.9%, respectively. Discriminating between normal and cancerous cells of actual human tissues through subcellular Raman markers is feasible, especially when using the nucleus-cytoplasm difference spectra. The subcellular Raman approach had good stability, and had excellent diagnostic performance for rectal as well as colon tissues. The insights gained from this study shed new light on the general applicability of subcellular Raman analysis in clinical trials.

Tracing overlapping biological signals in mid-infrared using colonic tissues as a model system

AIM

To understand the interference of carbohydrates absorbance in nucleic acids signals during diagnosis of malignancy using Fourier transform infrared (FTIR) spectroscopy.

METHODS

We used formalin fixed paraffin embedded colonic tissues to obtain infrared (IR) spectra in the mid IR region using a bruker II IR microscope with a facility for varying the measurement area by varying the aperture available. Following this procedure we could measure different regions of the crypt circles containing different biochemicals. Crypts from 18 patients were measured. Circular crypts with a maximum diameter of 120 μm and a lumen of about 30 μm were selected for uniformity. The spectral data was analyzed using conventional and advanced computational methods.

RESULTS

Among the various components that are observed to contribute to the diagnostic capabilities of FTIR, the carbohydrates and nucleic acids are prominent. However there are intrinsic difficulties in the diagnostic capabilities due to the overlap of major absorbance bands of nucleic acids, carbohydrates and phospholipids in the mid-IR region. The result demonstrates colonic tissues as a biological system suitable for studying interference of carbohydrates and nucleic acids under ex vivo conditions. Among the diagnostic parameters that are affected by the absorbance from nucleic acids is the RNA/DNA ratio, dependent on absorbance at 1121 cm^-1 and 1020 cm^-1 that is used to classify the normal and cancerous tissues especially during FTIR based diagnosis of colonic malignancies. The signals of the nucleic acids and the ratio (RNA/DNA) are likely increased due to disappearance of interfering components like carbohydrates and phosphates along with an increase in amount of RNA.

CONCLUSION

The present work, proposes one mechanism for the observed changes in the nucleic acid absorbance in mid-IR during disease progression (carcinogenesis).

Detection of antibiotic resistant Escherichia Coli bacteria using infrared microscopy and advanced multivariate analysis

DeterminingE. colibacteria susceptibility by analyzing their FTIR spectra using multivariate analysis.

Detection of Vero Cells Infected with Herpes Simplex Types 1 and 2 and Varicella Zoster Viruses Using Raman Spectroscopy and Advanced Statistical Methods

Of the eight members of the herpes family of viruses, HSV1, HSV2, and varicella zoster are the most common and are mainly involved in cutaneous disorders. These viruses usually are not life-threatening, but in some cases they might cause serious infections to the eyes and the brain that can lead to blindness and possibly death. An effective drug (acyclovir and its derivatives) is available against these viruses. Therefore, early detection and identification of these viral infections is highly important for an effective treatment. Raman spectroscopy, which has been widely used in the past years in medicine and biology, was used as a powerful spectroscopic tool for the detection and identification of these viral infections in cell culture, due to its sensitivity, rapidity and reliability. Our results showed that it was possible to differentiate, with a 97% identification success rate, the uninfected Vero cells that served as a control, from the Vero cells that were infected with HSV-1, HSV-2, and VZV. For that, linear discriminant analysis (LDA) was performed on the Raman spectra after principal component analysis (PCA) with a leave one out (LOO) approach. Raman spectroscopy in tandem with PCA and LDA enable to differentiate among the different herpes viral infections of Vero cells in time span of few minutes with high accuracy rate. Understanding cell molecular changes due to herpes viral infections using Raman spectroscopy may help in early detection and effective treatment.

Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy: An analytical technique to understand therapeutic responses at the molecular level

Rapid monitoring of the response to treatment in cancer patients is essential to predict the outcome of the therapeutic regimen early in the course of the treatment. The conventional methods are laborious, time-consuming, subjective and lack the ability to study different biomolecules and their interactions, simultaneously. Since; mechanisms of cancer and its response to therapy is dependent on molecular interactions and not on single biomolecules, an assay capable of studying molecular interactions as a whole, is preferred. Fourier Transform Infrared (FTIR) spectroscopy has become a popular technique in the field of cancer therapy with an ability to elucidate molecular interactions. The aim of this study, was to explore the utility of the FTIR technique along with multivariate analysis to understand whether the method has the resolution to identify the differences in the mechanism of therapeutic response. Towards achieving the aim, we utilized the mouse xenograft model of retinoblastoma and nanoparticle mediated targeted therapy. The results indicate that the mechanism underlying the response differed between the treated and untreated group which can be elucidated by unique spectral signatures generated by each group. The study establishes the efficiency of non-invasive, label-free and rapid FTIR method in assessing the interactions of nanoparticles with cellular macromolecules towards monitoring the response to cancer therapeutics.

Infrared hollow optical fiber probes for reflectance spectral imaging

Systems for infrared reflectance imaging are built with an FT-IR spectrometer, hollow optical fibers, and a high-speed infrared camera. To obtain reflectance images of biological samples, an optical fiber probe equipped with a light source at the distal end and a hybrid fiber probe composed of fibers for beam radiation and ones for image detection have been developed. By using these systems, reflectance spectral images of lipid painted on biomedical hard tissue, which provides reflectance of around 4%, are successfully acquired.

Improving prediction of prostate cancer recurrence using chemical imaging

Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.

The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors

Protein-related changes associated with the development of human brain gliomas are of increasing interest in modern neuro-oncology.

Medical hyperspectral imaging: a review

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Genetic algorithm spectral feature selection coupled with quadratic discriminant analysis for ATR-FTIR spectrometric diagnosis of basal cell carcinoma via blood sample analysis

A diagnostic approach for basal cell carcinoma (BCC) has been developed based on investigation of the infrared spectra of blood samples.

Medical hyperspectral imaging: a review

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Quantum cascade laser-based hyperspectral imaging of biological tissue

The spectroscopy of analyte-specific molecular vibrations in tissue thin sections has opened up a path toward histopathology without the need for tissue staining. However, biomedical vibrational imaging has not yet advanced from academic research to routine histopathology due to long acquisition times for the microscopic hyperspectral images and/or cost and availability of the necessary equipment. Here we show that the combination of a fast-tuning quantum cascade laser with a microbolometer array detector allows for a rapid image acquisition and bares the potential for substantial cost reduction. A 3.1 x 2.8 mm2 unstained thin section of mouse jejunum has been imaged in the 9.2 to 9.7 μm wavelength range (spectral resolution ~1 cm(-1)) within 5 min with diffraction limited spatial resolution. The comparison of this hyperspectral imaging approach with standard Fourier transform infrared imaging or mapping of the identical sample shows a reduction in acquisition time per wavenumber interval and image area by more than one or three orders of magnitude, respectively.

Infrared spectral histopathology for cancer diagnosis: a novel approach for automated pattern recognition of colon adenocarcinoma

Automated and label-free colon cancer diagnosis and identification of tumor-associated features using FTIR spectral histopathology directly on paraffinized tissue arrays.

Changes in the infrared microspectroscopic characteristics of DNA caused by cationic elements, different base richness and single-stranded form

BACKGROUND

The infrared (IR) analysis of dried samples of DNA and DNA-polypeptide complexes is still scarce. Here we have studied the FT-IR profiles of these components to further the understanding of the FT-IR signatures of chromatin and cell nuclei.

METHODOLOGY/PRINCIPAL FINDINGS

Calf thymus and salmon testis DNA, and complexes of histone H1, protamine, poly-L-lysine and poly-L-arginine (histone-mimic macromolecules) with DNA were analyzed in an IR microspectroscope equipped with an attenuated total reflection diamond objective and Grams software. Conditions including polypeptides bound to the DNA, DNA base composition, and single-stranded form were found to differently affect the vibrational characteristics of the chemical groups (especially, PO(2)(-)) in the nucleic acid. The antisymmetric stretching (ν(as)) of the DNA PO(2)(-) was greater than the symmetric stretching (ν(s)) of these groups and increased in the polypeptide-DNA complexes. A shift of the ν(as) of the DNA PO(2)(-) to a lower frequency and an increased intensity of this vibration were induced especially by lysine-rich histones. Lysine richness additionally contributed to an increase in the vibrational stretching of the amide I group. Even in simple molecules such as inorganic phosphates, the vibrational characteristics of the phosphate anions were differently affected by different cations. As a result of the optimization of the DNA conformation by binding to arginine-rich polypeptides, enhancements of the vibrational characteristics in the FT-IR fingerprint could be detected. Although different profiles were obtained for the DNA with different base compositions, this situation was no longer verified in the polypeptide-DNA complexes and most likely in isolated chromatin or cell nuclei. However, the ν(as) PO(2)(-)/ν(s) PO(2)(-) ratio could discriminate DNA with different base compositions and DNA in a single-stranded form.

CONCLUSIONS/SIGNIFICANCE

FT-IR spectral profiles are a valuable tool for establishing the vibrational characteristics of individualized chromatin components, such as DNA and DNA-polypeptide complexes in dried samples.

Utilizing FTIR-ATR spectroscopy for classification and relative spectral similarity evaluation of different Colletotrichum coccodes isolates

Colletotrichum coccodes (C. coccodes) is a pathogenic fungus which causes anthracnose on tomatoes and black dot disease in potatoes. It is important to differentiate among these isolates and to detect the origin of newly discovered isolates, in order to treat the disease in its early stages. However, distinguishing between isolates using common biological methods is time-consuming, and not always available. We used Fourier Transform Infra-Red (FTIR)-Attenuated Total Reflectance (ATR) spectroscopy and advanced mathematical and statistical methods to distinguish between different isolates of C. coccodes. To our knowledge, this is the first time that FTIR-ATR spectroscopy was used, combined with multivariate analysis, to classify such a large number of 15 isolates belonging to the same species. We obtained a success rate of approximately 90% which was achieved using the region 800-1775 cm(-1). In addition we succeeded in determining the relative spectral similarity between different fungal isolates by developing a new algorithm. This method could be an important potential diagnostic tool in agricultural research, since it may outline the extent of the biological similarity between fungal isolates. Based on the PCA calculations, we grouped the fifteen isolates included in this study into four different degrees of similarity.

Bone marrow mesenchymal stem cells in patients with beta thalassemia major: molecular analysis with attenuated total reflection-Fourier transform infrared spectroscopy study as a novel method

Bone marrow mesenchymal stem cells (BM-MSCs) are the main cellular components of the bone marrow, providing a supportive cellular microenvironment to maintain healthy hematopoiesis. β-thalassemia major (β-TM) is characterized by anemia that is caused by a genetic defect in hemoglobin synthesis and results in ineffective erythropoiesis (IE). The alterations in the microenvironment in thalassemic bone marrow during IE can cause changes in BM-MSCs. This study aimed to investigate global structural and compositional changes in BM-MSCs in β-TM that may provide a basis in understanding interactions of hematopoietic stem cells (HSCs)-MSCs in such a pathological bone marrow microenvironment. Following characterization of morphological, immunophenotypical, and differentiation properties, the changes in healthy and thalassemic BM-MSCs before and after bone marrow transplantation (BMT) were examined by attenuated total reflection-Fourier transform infrared (ATR-FTIR). The significant increase in lipid, protein, glycogen, and nucleic acid contents in thalassemic BM-MSCs with respect to healthy BM-MSCs was attributed to enhanced cell proliferation and BM activity during IE. The significant decreases in the content of mentioned macromolecules in post-transplant group BM-MSCs versus pre-transplant BM-MSCs was interpreted as restoring effect of BMT therapy on IE and defective BM microenvironment. These alterations were also supported by ELISA results of erythropoietin (EPO) and growth differentiation factor (GDF15) in bone marrow plasma samples as a reflection of IE and by MTT proliferation assay on BM-MSCs. Based on these changes, sampling groups were discriminated by cluster analysis. These results provide information for the studies that concentrate on interactions between HSCs-MSCs in bone marrow.

Detection and quantification of poliovirus infection using FTIR spectroscopy and cell culture

Background

In a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and time-consuming, making point-of-care detection a challenge. Faster, more sensitive, highly specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular-based sensing, may offer a precise way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of cells by tracking changes in absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles.

Results

Buffalo green monkey kidney (BGMK) cells infected with different virus titers were studied at 1 - 12 hours post-infection (h.p.i.). A partial least squares (PLS) regression method was used to analyze and model cellular responses to different infection titers and times post-infection. The model performs best at 8 h.p.i., resulting in an estimated root mean square error of cross validation (RMSECV) of 17 plaque forming units (PFU)/ml when using low titers of infection of 10 and 100 PFU/ml. Higher titers, from 10³ to 10⁶ PFU/ml, could also be reliably detected.

Conclusions

This approach to poliovirus detection and quantification using FTIR spectroscopy and cell culture could potentially be extended to compare biochemical cell responses to infection with different viruses. This virus detection method could feasibly be adapted to an automated scheme for use in areas such as water safety monitoring and medical diagnostics.

Celecoxib reduces fluidity and decreases metastatic potential of colon cancer cell lines irrespective of COX-2 expression

CLX (celecoxib), a selective COX-2 (cyclo-oxygenase-2) inhibitor, has numerous pleiotropic effects on the body that may be independent of its COX-2 inhibitory activity. The cancer chemopreventive ability of CLX, particularly in CRC (colorectal cancer), has been shown in epidemiological studies. Here we have, for the first time, examined the biophysical effects of CLX on the cellular membranes of COX-2 expressing (HT29) and COX-2 non-expressing (SW620) cell lines using ATR-FTIR (attenuated total reflectance–Fourier transform IR) spectroscopy and SL-ESR (spin label–ESR) spectroscopy. Our results show that CLX treatment decreased lipid fluidity in the cancer cell lines irrespective of COX-2 expression status. As metastatic cells have higher membrane fluidity, we examined the effect of CLX on the metastatic potential of these cells. The CLX treatment efficiently decreased the proliferation, anchorage-independent growth, ability to close a scratch wound and migration and invasion of the CRC cell lines through Matrigel. We propose that one of the ways by which CLX exerts its anti-tumorigenic effects is via alterations in cellular membrane fluidity which has a notable impact on the cells' metastatic potential.

Spectral signatures of colonic malignancies in the mid-infrared region: from basic research to clinical applicability

The process of carcinogenesis in the colon progresses through several overlapping stages, making the evaluation process challenging, as well as subjective. Owing to the complexity of colonic tissues and the search for a technique that is rapid and foolproof for precise grading and evaluation of biopsies, many spectroscopic techniques have been evaluated in the past few decades for their efficiency and clinical compatibility. Fourier-transform infrared spectroscopy, being quantitative and objective, has the capacity for automation and relevance to cancer diagnosis. This article highlights investigations on the application of Fourier-transform infrared spectroscopy (particularly microscopy) in colon cancer diagnosis and parallel developments in data analysis techniques for the characterization of spectral signatures of malignant tissues in the colon.