Applications of FT-IR Spectrophotometry in Cancer Diagnostics

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Infrared spectroscopy for fast screening of diabetes and periodontitis

SIGNIFICANCE

FT-IR is an important and emerging tool, providing information related to the biochemical composition of biofluids. It is important to demonstrate that there is an efficacy in separating healthy and diseased groups, helping to establish FT-IR uses as fast screening tool.

AIM

Via saliva diagnosis evaluate the accuracy of FT-IR associate with machine learning model for classification among healthy (control group), diabetic (D) and periodontitis (P) patients and the association of both diseases (DP).

APPROACH

Eighty patients diagnosed with diabetes and periodontitis through conventional methods were recruited and allocated in one of the four groups. Saliva samples were collected from participants of each group (n = 20) and were processed using Bruker Alpha II spectrometer in a FT-IR spectral fingerprint region between 600 and-1800 cm-1, followed by data preprocessing and analysis using machine learning tools.

RESULTS

Various FTI-R peaks were detectable and attributed to specific vibrational modes, which were classified based on confusion matrices showed in paired groups. The highest true positive rates (TPR) appeared between groups C vs D (93.5 % ± 2.7 %), groups C vs. DP (89.2 % ± 4.1 %), and groups D and P (90.4 % ± 3.2 %). However, P vs DP presented higher TPR for DP (84.1 % ±3.1 %) while D vs. DP the highest rate for DP was 81.7 % ± 4.3 %. Analyzing all groups together, the TPR decreased.

CONCLUSION

The system used is portable and robust and can be widely used in clinical environments and hospitals as a new diagnostic technique. Studies in our groups are being conducted to solidify and expand data analysis methods with friendly language for healthcare professionals. It was possible to classify healthy patients in a range of 78-93 % of accuracy. Range over 80 % of accuracy between periodontitis and diabetes were observed. A general classification model with lower TPR instead of a pairwise classification would only have advantages in scenarios where no prior patient information is available regarding diabetes and periodontitis status.

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.

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.

A disposable optofluidic micro-transmission cell with tailorable length for Fourier-transform infrared spectroscopy of biological fluids

Mid-infrared Fourier-transform infrared (FT-IR) spectroscopy of liquid biological samples is limited by the high absorption of water in this spectral range, which makes conventional transmission cuvettes unsuitable as their centimeter-scale length is already too big. The most common alternative relies on the use of attenuated total reflection (ATR) accessories, which provide a small interaction path length for light along the interface between the analyte and the expensive ATR crystals. In this work, we address this issue by proposing a disposable and low-cost micro-transmission cell. Its construction relies on a simple technique, which consists of dispersing plastic spherical microparticles in a liquid sample before dispensing it between two pieces of silicon assembled one onto the other and acting as windows for the cell. Consequently, the microparticles act as a spacer of very precise height in-between the two silicon windows. This technique allows easy construction of infrared absorption cells with near-optimum optical interaction path length just by selecting the most appropriate particle size. The concept is demonstrated by measuring the concentration of glucose in aqueous solutions using microspheres of diameter 20 μm then 40 μm and analyzing the corresponding glucose absorption peaks in the wavenumber range 950-1200 cm-1. The performance is compared to that of standard ATR spectroscopy of the same samples. This resulted in a root-mean-square error of cross-validation (RMSECV) of 58.8 mg dl-1 as obtained for transmission measurements by partial least squares (PLS) regression, which is comparable to the RMSECV of 53 mg dl-1 for single-reflection diamond ATR measurements.

Two-trace two-dimensional correlation spectra (2T2D-COS) analysis using FTIR spectra to monitor the immune response by COVID-19

There is a growing trend in using saliva for SARS-CoV-2 detection with reasonable accuracy. We have studied the responses of IgA, IgG, and IgM in human saliva by directly comparing disease with control analyzing two-trace two-dimensional correlation spectra (2T2D-COS) employing Fourier transform infrared (FTIR) spectra. It explores the molecular-level variation between control and COVID-19 saliva samples. The advantage of 2T2D spectra is that it helps in discriminating remarkably subtle features between two simple pairs of spectra. It gives spectral information from highly overlapped bands associated with different systems. The clinical findings from 2T2D show the decrease of IgG and IgM salivary antibodies in the 50, 60, 65, and 75-years COVID-19 samples. Among the various COVID-19 populations studied the female 30-years group reveals defense mechanisms exhibited by IgM and IgA. Lipids and fatty acids decrease, resulting in lipid oxidation due to the SARS-CoV-2 in the samples studied. Study shows salivary thiocyanate plays defense against SARS-CoV-2 in the male population in 25 and 35 age groups. The receiver operation characteristics statistical method shows a sensitivity of 98% and a specificity of 94% for the samples studied. The measure of accuracy computed as F score and G score has a high value, supporting our study's validation. Thus, 2T2D-COS analysis can potentially monitor the progression of immunoglobulin's response function to COVID-19 with reasonable accuracy, which could help diagnose clinical trials. KEY MESSAGES: The molecular profile of salivary antibodies is well resolved and identified from 2T2D-COS FTIR spectra. The IgG antibody plays a significant role in the defense mechanism against SARS-CoV-2 in 25-40 years. 2T2D-COS reveals the absence of salivary thiocyanate in the 40-75 years COVID-19 population. The receiver operation characteristic (ROC) analysis validates our study with high sensitivity and specificity.

Spectroscopic imaging to assess biochemical alterations in liver carcinoma cells exposed to transition metals

Despite the invaluable role of transition metals in every living organism, it should be remembered that failure to maintain the proper balance and exceed the appropriate dose may have the opposite effect. In the era of such a popular and propagated need for supplementation in the media, one should bear in mind the harmful effects that may become the result of improper and excessive intake of transition metals. This article establishes the feasibility of Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopic imaging at the single-cell level to investigate the cellular response to various transition metals. These two non-destructive and perfectly complementary methods allow for in-depth monitoring of changes taking place within the cell under the influence of the agent used. HepG2 liver carcinoma cells were exposed to chromium, iron, cobalt, molybdenum, and nickel at 1 and 2 mM concentrations. Spectroscopic results were further supported by biological evaluation of selected caspases concentration. The caspase- 3, 6, 8, 9, and 12 concentrations were determined with the use of the enzyme-linked immunosorbent assay (ELISA) method. This study shows the induction of apoptosis in the intrinsic pathway by all studied transition metals. Cellular metabolism alterations are induced by mitochondrial metabolism changes and endoplasmic reticulum (ER) metabolism variations. Moreover, nickel induces not only the intrinsic pathway of apoptosis but also the extrinsic pathway of this process.

Preparation of nanoformulation of 5-fluorouracil to improve anticancer efficacy: integrated spectroscopic, docking, and MD simulation approaches

Nanoformulations (NFs) can be used as a novel drug delivery system to treat all cancer types. One of the major drawbacks of conventional anticancer drugs is that they have poor specificity and higher toxicity towards normal cells. 5-fluorouracil (5-FU) is a well-studied anticancer drug that has a significant role in various cancers, specifically colorectal cancer therapy. This study was performed to determine the functional groups, particle size, surface charge, heterogeneity, and stability of the NF. The NFs of 5-FU were prepared through the ultrasonication technique by increasing the surfactant (Tween-80) concentrations. Among all three NFs, nanoformulated 5-FU (n5-FU) showed the most effective particle size (10.72 nm) with a zeta potential of (-4.57 mV). The cytotoxicity and apoptosis profiles confirmed that n5-FU enhanced the anticancer effect of the pure drug in HCT-116 cells, as evident from MTT assay, fluorescence microscopy, and FACS analysis. In HCT-116 cells, the IC50 values of pure and n5-FU were obtained as 41.3 μM and 18.8 μM, respectively, indicating that n5-FU was more effective against the cancer cell line. The cellular uptake study was performed to check the intake of NF in cancer cells. However, the microtubule-affinity regulating kinase-4 (MARK-4), a cancer-target protein, was purified to study the inhibition and interaction studies. The inhibition assay confirmed the inhibitory potential of 5-FU against MARK-4 protein. the multi-spectroscopic, molecular docking and MD simulation studies were performed to analyse the conformational changes, binding studies, intermolecular interactions, and stability of MARK-4 protein upon binding 5-FU. This demonstrates that NF can enhance the effectiveness of anticancer drugs.Communicated by Ramaswamy H. Sarma.

Investigations of the Photothermal Properties of a Series of Molecular Gold-bis(dithiolene) Complexes Absorbing in the NIR-III Region

The photothermal properties of a series of neutral radical gold-bis(dithiolene) complexes absorbing in the near-infrared-III window (1550-1870 nm) have been investigated. This class of complexes was found to be good photothermal agents (PTAs) in toluene under 1600 nm laser irradiation with photothermal efficiencies around 40 and 60 % depending on the nature of the dithiolene ligand. To the best of our knowledge, these complexes are the first small molecular photothermal agents to absorb so far into the near infrared. To test their applicability in water, these hydrophobic complexes have been encapsulated into nanoparticles constituted by amphiphilic block-copolymers. Stable suspensions of polymeric nanoparticles (NPs) encapsulating the gold-bis(dithiolene) complexes have been prepared which show a diameter around 100 nm. The encapsulation rate was found to be strongly dependent on the nature of the dithiolene ligands. The photothermal properties of the aqueous suspensions containing gold-bis(dithiolene) complexes were then studied under 1600 nm laser irradiation. These studies demonstrate that water has strong photothermal activity in the NIR-III region that, cannot be overcome even with the addition of gold complexes displaying good photothermal properties.

Diagnosis and monitoring of hepatocellular carcinoma in Hepatitis C virus patients using attenuated total reflection Fourier transform infrared spectroscopy

BACKGROUND

Current diagnostic methods for assessment of hepatitis C virus related hepatocellular carcinoma and subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, to establish appropriate treatment strategies, are costly, invasive and requires multiple screening steps. This demands alternative diagnostic approaches that are cost-effective, time-efficient, and minimally invasive, while maintaining their efficacy for screening of hepatitis c virus related hepatocellular carcinoma. In this study, we propose that attenuated total reflection Fourier transform infrared in conjunction with principal component analysis - linear discriminant analysis and support vector machine multivariate algorithms holds a potential as a sensitive tool for the detection of hepatitis C virus-related hepatocellular carcinoma and the subsequent categorization of hepatocellular carcinoma into non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma.

METHODS

Freeze-dried sera samples collected from 31 hepatitis c virus related hepatocellular carcinoma patients and 30 healthy individuals, were used to acquire mid-infrared absorbance spectra (3500-900 cm-1) using attenuated total reflection Fourier transform infrared. Chemometric machine learning techniques were utilized to build principal component analysis - linear discriminant analysis and support vector machine discriminant models for the spectral data of hepatocellular carcinoma patients and healthy individuals. Sensitivity, specificity, and external validation on blind samples were calculated.

RESULTS

Major variations were observed in the two spectral regions i.e., 3500-2800 and 1800-900 cm-1. IR spectral signatures of hepatocellular carcinoma were reliably different from healthy individuals. Principal component analysis - linear discriminant analysis and support vector machine models computed 100% accuracy for diagnosing hepatocellular carcinoma. To classify the non-angio-invasive hepatocellular carcinoma/ angio-invasive hepatocellular carcinoma status, diagnostic accuracy of 86.21% was achieved for principal component analysis - linear discriminant analysis. While the support vector machine showed a training accuracy of 98.28% and a cross-validation accuracy of 82.75%. External validation for support vector machine based classification observed 100% sensitivity and specificity for accurately classifying the freeze-dried sera samples for all categories.

CONCLUSIONS

We present the specific spectral signatures for non-angio-invasive hepatocellular carcinoma and angio-invasive hepatocellular carcinoma, which were prominently differentiated from healthy individuals. This study provides an initial insight into the potential of attenuated total reflection Fourier transform infrared to diagnose hepatitis C virus related hepatocellular carcinoma but also to further categorize into non-angio-invasive and angio-invasive hepatocellular carcinoma.

Global serum profiling: an opportunity for earlier cancer detection

The advances in cancer research achieved in the last 50 years have been remarkable and have provided a deeper knowledge of this disease in many of its conceptual and biochemical aspects. From viewing a tumor as a 'simple' aggregate of mutant cells and focusing on detecting key cell changes leading to the tumorigenesis, the understanding of cancer has broadened to consider it as a complex organ interacting with its close and far surroundings through tumor and non-tumor cells, metabolic mechanisms, and immune processes. Metabolism and the immune system have been linked to tumorigenesis and malignancy progression along with cancer-specific genetic mutations. However, most technologies developed to overcome the barriers to earlier detection are focused solely on genetic information. The concept of cancer as a complex organ has led to research on other analytical techniques, with the quest of finding a more sensitive and cost-effective comprehensive approach. Furthermore, artificial intelligence has gained broader consensus in the oncology community as a powerful tool with the potential to revolutionize cancer diagnosis for physicians. We herein explore the relevance of the concept of cancer as a complex organ interacting with the bodily surroundings, and focus on promising emerging technologies seeking to diagnose cancer earlier, such as liquid biopsies. We highlight the importance of a comprehensive approach to encompass all the tumor and non-tumor derived information salient to earlier cancer detection.

Nanoscale insight into biochemical changes in cervical cancer cells exposed to adaptogenic drug

This paper describes for the first time the application of atomic force microscopy-based infrared spectroscopy (AFM-IR) to evaluate cellular response to adaptogen, based on an in vitro model of cervical cancer. HeLa cervical cells were exposed to different concentrations of withaferin A, a very promising anti-cancer adaptogenic substance. AFM-IR approach was used to image single cells post-adaptogen treatment and to track subtle biochemical changes in cells at the nanoscale level. Partial least squares (PLS) regression was applied to build predictive models that allowed for the identification of spectral markers of adaptogen-induced alterations Spectroscopic studies were enriched with fluorescence staining to determine whether the adaptogen affects cell morphology. The results showed that with the increase in the concentration of adaptogen, changes in the cell nucleus and the actin cytoskeleton become more and more significant. It has been demonstrated that the AFM-IR technique can successfully study the cellular response to the anti-cancer agent at the single-cell level with nanoscale spatial resolution. On the basis of the promising findings presented in this paper, it is possible to conclude that withaferin A has great potential in inhibiting the proliferation of cervical cancer cells in a dose-dependent manner. It has been found that both the increase in the concentration of withaferin A and the increase in incubation time with the adaptogen resulted in a decrease in the intensity of the bands assigned to nucleic acids. This may be due to DNA condensation, internuclear cleavage, or degradation during apoptosis. The findings also suggest changes in the secondary structure of proteins that may be a consequence of disruption of the actin cytoskeleton, progressive apoptosis, or significant biochemical changes. Furthermore, noticeable changes were also observed in the bands originating from lipids vibrations, and an increased share of the band near 2920 cm^-1, considered an important marker of apoptosis, was noted. The metabolism of carbohydrates in cells also changes under the influence of the adaptogen. AFM-IR provides nanoscale insight into the structural and morphological properties of cells after drug treatment and is an indisputable milestone in the development of new anti-cancer approaches.

Adaptogenic activity of withaferin A on human cervical carcinoma cells using high-definition vibrational spectroscopic imaging

Despite invaluable advances in cervical cancer therapy, treatment regimens for recurrent or persistent cancers and low-toxicity alternative treatment options are scarce. In recent years, substances classified as adaptogens have been identified as promising drug sources for preventing and treating cancer-based diseases on their ability to attack multiple molecular targets. This paper establishes the effectiveness of inhibition of the neoplastic process by a withaferin A (WFA), an adaptogenic substance, based on an in vitro model of cervical cancer. This study explores for the first time the potential of high-definition vibrational spectroscopy methods, i.e. Fourier-transform infrared (FT-IR) and Raman spectroscopic (RS) imaging at the single-cell level to evaluate the efficacy of the adaptogenic drug. HeLa cervical cancer cells were incubated with various concentrations of WFA at different incubation times. The multimodal spectroscopic approach combined with partial least squares (PLS) regression allowed the identification of molecular changes (e.g., lipids, protein secondary structures, or nucleic acids) induced by WFA at the cellular level. The results clearly illustrate the enormous potential of WFA in inhibiting the proliferation of cervical cancer cells. WFA inhibited the growth of the studied cancer cell line in a dose-dependent manner. Such studies provide comprehensive information on the sensitivity of cells to adaptogenic drugs. This is a fundamental step towards determining the rate and nature of adaptogen-induced changes in cancer cells.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

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

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

Characterizing biological macromolecules with attenuated total reflectance-Fourier transform infrared spectroscopy provides hands-on spectroscopy experiences for undergraduates

Infrared (IR) spectra of biologically derived materials display distinct absorption bands correlating to individual macromolecules: protein, polysaccharide, lipid, and nucleic acids. A series of experiments aimed at teaching qualitative bioorganic spectroscopy using attenuated total reflectance (ATR) Fourier transform infrared (IR) with biological polymers as samples is proposed. Labs targeting 1st and 4th year undergraduate students at St. Francis College are being developed. During 2014 ➔ 2017, an integrated biology/chemistry exercise featuring elementary spectroscopy as an addition to an existing lab on light microscopy was administered to three sections of a 1st year general biology course. Students were taught the concept of a spectral fingerprint and to identify carbohydrate and protein based materials by looking for key vibrational bands. The success of that effort as determined by the results of an assessment quiz became the motivation for developing an advanced 4th year exercise involving four macromolecules. In a trial lab (Spring 2019) students gathered reference spectra from materials homogeneous in a single biopolymer followed by spectra of whole tissues which they were expected to fully characterize. Assessment data suggest that 1st year students benefited most from the experience. A detailed discussion of reference and sample spectra (as obtained by students) and relevant bond vibrations along with suggestions for instructors are presented.

Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development

The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution.

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

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

Calculation of the infrared spectra of organophosphorus compounds and prediction of new types of nerve agents

IR prediction of Novichok candidates is performed by establishing an accurate DFT calculation method on organophosphorus compounds.

FTIR microspectroscopic study of biomacromolecular changes in As2O3 induced MGC803 cells apoptosis

It is well-known that As₂O₃ has significant anticancer effects, however, little is known regarding its mechanism for treating gastric cancer. Thus, we investigated biomacromolecular (DNA, proteins and lipids) changes of human gastric cancer cell line MGC803 to further understand As₂O₃-induced apoptosis. Conventional methods showed the increase of the apoptosis rate, the decrease of mitochondrial membrane potential (MMP), the accumulation of reactive oxygen species (ROS) and the changes of apoptotic proteins, etc. Fourier transform infrared (FTIR) microspectroscopy sensitively recognized overall biomacromolecular changes caused by the above: Peak-area ratios indicated the content/structure changes in DNA, proteins and lipids. Principle component analysis (PCA) revealed significant changes in intracellular DNA concentration and structure. This study suggests that As₂O₃ may exert anti-gastric cancer effect by altering intracellular biomacromolecules especially DNA.

Infrared spectroscopic imaging study of BV-2 microglia altering tumor cell biological activity and cellular fraction

Tumor brain metastasis is a severe threat to patients' neurological function, in which microglia may be involved in the process of tumor cell metastasis among nerve cells. Our study focused on the interaction between microglia and breast and lung cancer cells. Changes in the proliferation and migration ability of cocultured tumor cells were examined; synchrotron radiation-based fourier transform infrared microspectroscopy (SR-FTIR) was used to detect changes in the structures and contents of biomolecules within the tumor cells. The experimental results showed that the proliferation and migration ability of tumor cells increased after coculture, and the structures and contents of biological macromolecules in tumor cells changed. The absorption peak positions of the amide Ⅱ and amide Ⅰ bands observed for the four kinds of tumor cells changed, and the absorption intensities were significantly enhanced, indicating changes in the secondary structures and contents of proteins in tumor cells, which may be the root cause of the change in tumor cell characteristics. Therefore, the metabolites of microglia may be involved in the progression of tumor cells in the nervous system. In this study, we focused on the interaction between microglia and tumor cells by using SR-FTIR and provided a new understanding of the mechanism of brain metastasis.

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. Graphical Abstract.

Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy

Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm^-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva for Breast Cancer Diagnosis

Saliva biomarkers using reagent-free biophotonic technology have not been investigated as a strategy for early detection of breast cancer (BC). The attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy has been proposed as a promising tool for disease diagnosis. However, its utilization in cancer is still incipient, and currently saliva has not been used for BC screening. We have applied ATR-FTIR onto saliva from patients with breast cancer, benign breast disease, and healthy matched controls to investigate its potential use in BC diagnosis. Several salivary vibrational modes have been identified in original and second-derivative spectra. The absorbance levels at wavenumber 1041 cm⁻¹ were significantly higher (p < 0.05) in saliva of breast cancer patients compared with those of benign patients, and the ROC curve analysis of this peak showed a reasonable accuracy to discriminate breast cancer from benign and control patients. The 1433–1302.9 cm⁻¹ band area was significantly higher (p < 0.05) in saliva of breast cancer patients than in control and benign patients. This salivary ATR-FTIR spectral area was prevalidated as a potential diagnostic biomarker of BC. This spectral biomarker was able to discriminate human BC from controls with sensitivity and specificity of 90% and 80%, respectively. Besides, it was able to differentiate BC from benign disease with sensitivity and specificity of 90% and 70%, respectively. Briefly, for the first time, saliva analysis by ATR-FTIR spectroscopy has demonstrated the potential use of salivary spectral biomarkers (1041 cm⁻¹ and 1433–1302.9 cm⁻¹) as a novel alternative for noninvasive BC diagnosis, which could be used for screening purposes.

Comparison of standard and HD FT-IR with multimodal CARS/TPEF/SHG/FLIMS imaging in the detection of the early stage of pulmonary metastasis of murine breast cancer

The comparison of the potential of FT-IR in standard and high definition modes with multimodal CARS/TPEF/SHG/FLIMS imaging for detection of the early stage of pulmonary metastasis of murine breast cancer is presented.

New approach to investigate Common Variable Immunodeficiency patients using spectrochemical analysis of blood

Common variable immune deficiency (CVID) is a primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and various complications. The clinical heterogeneity of CVID has hindered identification of an underlying immune defect; diagnosis relies on clinical judgement, alongside evidence-based criteria. The lack of pathognomonic clinical or laboratory features leads to average diagnostic delays of 5 years or more from the onset. Vibrational spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy have recently gained increasing clinical importance, being rapid-, non-invasive and inexpensive methods to obtain information on the content of biological samples. This has led us to apply FTIR spectroscopy to the investigation of blood samples from a cohort of CVID patients; revealing spectral features capable of stratifying CVID patients from healthy controls with sensitivities and specificities of 97% and 93%, respectively for serum, and 94% and 95%, respectively for plasma. Furthermore we identified several discriminating spectral biomarkers; wavenumbers in regions indicative of nucleic acids (984 cm⁻¹, 1053 cm⁻¹, 1084 cm⁻¹, 1115 cm⁻¹, 1528 cm⁻¹, 1639 cm⁻¹), and a collagen-associated biomarker (1528 cm⁻¹), which may represent future candidate biomarkers and provide new knowledge on the aetiology of CVID. This proof-of-concept study provides a basis for developing a novel diagnostic tool for CVID.

Diagnosis of malignant pleural mesothelioma from pleural fluid by Fourier transform-infrared spectroscopy coupled with chemometrics

This study was conducted to differentiate malignant pleural mesothelioma (MPM) from lung cancer (LC) and benign pleural effusion (BPE) from pleural fluids using the diagnostic power of Fourier transform-infrared spectroscopy with attenuated total reflectance mode coupled with chemometrics. Infrared spectra of MPM (n  =  24), LC (n  =  20), and BPE (n  =  25) were collected, and hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to their spectra. HCA results indicated that MPM was differentiated from LC with 100% sensitivity and 100% specificity and from BPE, with 100% sensitivity and 88% specificity, which were also confirmed by PCA score plots. PCA loading plots indicated that these separations originated mainly from lipids, proteins, and nucleic acids-related spectral bands. There was significantly higher lipid, protein, nucleic acid, and glucose contents in the MPM and LC. However, the significant changes in triglyceride and cholesterol ester content, protein and nucleic acid structure, a lower membrane fluidity, and higher membrane order were only observed in the MPM. To check the classification success of some test samples/each group, soft independent modeling of class analogies was performed and 96.2% overall classification success was obtained. This approach can provide a rapid and inexpensive methodology for the efficient differentiation of MPM from other pleural effusions.

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

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

Advantages of infrared transflection micro spectroscopy and paraffin-embedded sample preparation for biological studies

Fourier-Transform Infrared micro-spectroscopy is an excellent method for biological analyses. In this paper, series metal coating films on ITO glass were prepared by the electrochemical method and the different thicknesses of paraffin embedding rat's brain tissue on the substrates were studied by IR micro-spetroscopy in attenuated total reflection (ATR) mode and transflection mode respectively. The Co-Ni-Cu alloy coating film with low cost is good reflection substrates for the IR analysis. The infrared microscopic transflection mode needs not to touch the sample at all and can get the IR spectra with higher signal to noise ratios. The Paraffin-embedding method allows tissues to be stored for a long time for re-analysis to ensure the traceability of the sample. Also it isolates the sample from the metal and avoids the interaction of biological tissue with the metals. The best thickness of the tissues is 4 μm.

Applications of mid-infrared spectroscopy in the clinical laboratory setting

Fourier transform mid-infrared (MIR-FTIR) spectroscopy is a nondestructive, label-free, highly sensitive and specific technique that provides complete information on the chemical composition of biological samples. The technique both can offer fundamental structural information and serve as a quantitative analysis tool. Therefore, it has many potential applications in different fields of clinical laboratory science. Although considerable technological progress has been made to promote biomedical applications of this powerful analytical technique, most clinical laboratory analyses are based on spectroscopic measurements in the visible or ultraviolet (UV) spectrum and the potential role of FTIR spectroscopy still remains unexplored. In this review, we present some general principles of FTIR spectroscopy as a useful method to study molecules in specimens by MIR radiation together with a short overview of methods to interpret spectral data. We aim at illustrating the wide range of potential applications of the proposed technique in the clinical laboratory setting with a focus on its advantages and limitations and discussing the future directions. The reviewed applications of MIR spectroscopy include (1) quantification of clinical parameters in body fluids, (2) diagnosis and monitoring of cancer and other diseases by analysis of body fluids, cells, and tissues, (3) classification of clinically relevant microorganisms, and (4) analysis of kidney stones, nails, and faecal fat.

Vibrational Micro-Spectroscopy of Human Tissues Analysis: Review

Vibrational spectroscopy (Infrared (IR) and Raman) and, in particular, micro-spectroscopy and micro-spectroscopic imaging have been used to characterize developmental changes in tissues, to monitor these changes in cell cultures and to detect disease and drug-induced modifications. The conventional methods for biochemical and histophatological tissue characterization necessitate complex and "time-consuming" sample manipulations and the results are rarely quantifiable. The spectroscopy of molecular vibrations using mid-IR or Raman techniques has been applied to samples of human tissue. This article reviews the application of these vibrational spectroscopic techniques for analysis of biological tissue published between 2005 and 2015.

Early detection of breast cancer using total biochemical analysis of peripheral blood components: a preliminary study

Background

Most of the blood tests aiming for breast cancer screening rely on quantification of a single or few biomarkers. The aim of this study was to evaluate the feasibility of detecting breast cancer by analyzing the total biochemical composition of plasma as well as peripheral blood mononuclear cells (PBMCs) using infrared spectroscopy.

Methods

Blood was collected from 29 patients with confirmed breast cancer and 30 controls with benign or no breast tumors, undergoing screening for breast cancer. PBMCs and plasma were isolated and dried on a zinc selenide slide and measured under a Fourier transform infrared (FTIR) microscope to obtain their infrared absorption spectra. Differences in the spectra of PBMCs and plasma between the groups were analyzed as well as the specific influence of the relevant pathological characteristics of the cancer patients.

Results

Several bands in the FTIR spectra of both blood components significantly distinguished patients with and without cancer. Employing feature extraction with quadratic discriminant analysis, a sensitivity of ~90 % and a specificity of ~80 % for breast cancer detection was achieved. These results were confirmed by Monte Carlo cross-validation. Further analysis of the cancer group revealed an influence of several clinical parameters, such as the involvement of lymph nodes, on the infrared spectra, with each blood component affected by different parameters.

Conclusion

The present preliminary study suggests that FTIR spectroscopy of PBMCs and plasma is a potentially feasible and efficient tool for the early detection of breast neoplasms. An important application of our study is the distinction between benign lesions (considered as part of the non-cancer group) and malignant tumors thus reducing false positive results at screening. Furthermore, the correlation of specific spectral changes with clinical parameters of cancer patients indicates for possible contribution to diagnosis and prognosis.