Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Isolation, purification, characterization and stability analysis of melanin pigment from Mesona chinensis

In this study, the isolation and purification of melanin pigment from Mesona chinensis (MCM) were conducted, and the structural characterization and stability evaluation of MCM were performed. The results indicate that MCM is consistent with the spectral features of catechins and polyphenols, identified the stretching vibrations of functional groups such as OH, CH, CO, and CO. It is inferred that the structure of MCM is consistent with that of theophylline and it is mainly composed of phenolic acids, terpenoids, and organic acids. Stability evaluations indicate that MCM exhibits stability under white light, ultraviolet (UV) light, neutral, and alkaline environments, and it shows low sensitivity to reducing agents.

Advances in human papillomavirus detection for cervical cancer screening and diagnosis: challenges of conventional methods and opportunities for emergent tools

Human papillomavirus (HPV) infection is the main cause of cervical cancer and other cancers such as anogenital and oropharyngeal cancers. The prevention screening and treatment of cervical cancer has remained one of the top priorities of the World Health Organization (WHO). In 2020, the WHO came up with the 90-70-90 strategy aimed at eliminating cervical cancers as a public health problem by the year 2030. One of the key priorities of this strategy is the recommendation for countries to ensure that 70% of their women are screened using a high-performance test by the age of 35, and again by the age of 45. Over the years, several traditional methods (notably, Pap smear and nucleic acid-based techniques) have been used for the detection of cervical cancer. While these methods have significantly reduced the incidence of cervical cancer and death, they still come short of excellence for the total eradication of HPV infection. The challenges include low sensitivity, low specificity, poor reproducibility, the need for high-level specialists, and the high cost of access to the facilities, to mention a few. Interestingly, however, several efforts are being made today to mitigate these challenges. In this review, we discussed the pros and cons of the traditional screening and testing of HPV infections, the efforts being made to improve their performances, and the emergent tools (especially, the electrochemical methods) that promise to revolutionize the screening and testing of HPV infections. The main aim of the review is to provide some novel clues to researchers that would allow for the development of high-performance, affordable, and triage-suitable electrochemical-based diagnostic tools for HPV and cervical cancer.

Formulation and characterization of antibiotic drug loaded aquasome for the topical application

Aim: This study aimed to develop a topical antibiotic drug delivery system using aquasomes for enhanced treatment of skin and soft tissue infections (SSTIs). Materials & methods: Cephalothin was loaded into aquasomes using a multi-step process and optimized using design of experiment. The aquasomes were characterized for FT-IR, SEM and zeta potential analysis. Entrapment efficacy, In vitro drug release studies, antibacterial assays and stability study was performed to evaluate the efficacy of the formulated aquasomes. Results & conclusion: The formulated cephalothin-loaded aquasomes exhibited stable properties, controlled drug release and significant antibacterial activity against bacteria. This proves that the developed aquasome-based delivery system has the potential for sustained treatment of SSTIs.

Application of Fourier transform infrared spectroscopy to exhaled breath analysis for detecting helicobacter pylori infection

Helicobacter pylori (H. pylori) is one of the most globally prevalent bacteria, closely associated with gastrointestinal diseases such as gastric ulcers and chronic gastritis. Current clinical methods primarily involve Carbon-13 and Carbon-14 urea breath test, both carrying potential safety risks. Fourier-transform infrared (FTIR) spectroscopy can detect human exhaled gases, which may change under disease conditions. This preliminary study aims to explore the application value of FTIR-based breath analysis in detecting H. pylori infection, providing theoretical basis and clinical references for new clinical detection methods. A cross-sectional survey was conducted from August 2021 to May 2022 at Renmin Hospital of Wuhan University. Breath samples were collected before and half an hour after ingesting unlabeled urea. Gas samples were analyzed using FTIR breath spectra. Individual exhalation spectral data after deducting baseline spectral data were used as the basis for the training and test sets through K-center clustering algorithm. Results: A total of 278 samples were collected (63 H. pylori infection cases, 215 healthy controls). There were no statistically significant differences in general data (age, gender, smoking history, alcohol consumption history, comorbidities, etc.) between the two groups. The predictive model was successfully established, with recognition rates of 94.12%, 98.39%, and 91.30% for the training set, test set, and validation set, respectively. Exhaled gas analysis based on Fourier-transform infrared spectroscopy has the potential to diagnose H. pylori infection.

Sensing the Future-Frontiers in Biosensors: Exploring Classifications, Principles, and Recent Advances

Biosensors are transforming healthcare by delivering swift, precise, and economical diagnostic solutions. These analytical instruments combine biological indicators with physical transducers to identify and quantify biomarkers, thereby improving illness detection, management, and patient surveillance. Biosensors are widely utilized in healthcare for the diagnosis of chronic and infectious diseases, tailored treatment, and real-time health monitoring. This thorough overview examines several categories of biosensors and their uses in the detection of numerous biomarkers, including glucose, proteins, nucleic acids, and infections. Biosensors are commonly classified based on the type of transducer employed or the specific biorecognition element utilized. This review introduces a novel classification based on substrate morphology, offering a comprehensive perspective on biosensor categorization. Considerable emphasis is placed on the advancement of point-of-care biosensors, facilitating decentralized diagnostics and alleviating the strain on centralized healthcare systems. Recent advancements in nanotechnology have significantly improved the sensitivity, selectivity, and downsizing of biosensors, rendering them more efficient and accessible. The study examines problems such as stability, reproducibility, and regulatory approval that must be addressed to enable the widespread implementation of biosensors in clinical environments. The study examines the amalgamation of biosensors with wearable devices and smartphones, emphasizing the prospects for ongoing health surveillance and individualized medical care. This viewpoint clarifies the distinct types of biosensors and their particular roles, together with recent developments in the "smart biosensor" sector, facilitated by artificial intelligence and the Internet of Medical Things (IoMT). This novel approach seeks to deliver a comprehensive evaluation of the present condition of biosensor technology in healthcare, recent developments, and prospective paths, emphasizing their significance in influencing the future of medical diagnostics and patient care.

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

BACKGROUND & AIM

Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples.

METHODS & MATERIALS

Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy.

RESULTS

The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively.

CONCLUSION

The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Comparative Blood Profiling Based on ATR-FTIR Spectroscopy and Chemometrics for Differential Diagnosis of Patients with Amyotrophic Lateral Sclerosis-Pilot Study

Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70-0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS.

Optimizing near infrared laser irradiation and photosensitizer accumulation period for indocyanine green-mediated photodynamic therapy in breast cancer xenografts: a focus on treatment and characterization

Photodynamic therapy (PDT) is a promising cancer treatment approach. Indocyanine green (ICG) is a water-soluble tricarbocyanine dye with a peak absorption wavelength of around 800 nm and possesses the capacity to produce reactive oxygen species. FTIR spectroscopy is rarely used and offers insights into molecular changes in cancer studies. MCF-7 cells were injected into Nude mouse. Once the tumor had grown to a size of 3-4 mm, mice were randomized into the 12 PDT groups. After each mouse received 5 mg/kg of ICG, they were photo-irradiated with a diode laser emitting light at 809 nm, followed by waiting intervals of 0, 30, 60, and 90 min. Laser irradiation parameters were 150, 250, 500 mW/cm2 and irradiation duration was 1200s. The tumor size was measured every day for four days. The FTIR spectroscopy was used to perform spectral analysis on tumor tissue samples. Four distinct regions (3600-2800 cm-1, 1750-1550 cm-1, 1540-1450 cm-1, and 1700-1100 cm-1) were analyzed, and Hierarchical Cluster study was carried out. A decrease in tumor volume was observed with all PDT applications, except, increases in tumor volume was observed at 150mW 90-minute group. PDT administered after 90 min revealed variations in 150mW and 250mW laser powers in the 3600 cm-1-2800 cm-1 range. The 250mW and 500mW applications resulted in a considerable reduction in fibroadenoma and carcinoma tissues, according to an analysis comparing the A1695 / A1635 ratio. It is proposed that the ideal treatments for further investigation have a power output of 250 mW.

Upregulation of LY6K induced by FTO-mediated demethylation promotes the tumorigenesis and metastasis of oral squamous cell carcinoma via CAV-1-mediated ERK1/2 signaling activation

Lymphocyte antigen 6 complex locus K (LY6K) has been demonstrated to play a significant role in cancers and identified as a therapeutic biomarker for head and neck squamous cell carcinoma. However, the role of LY6K in oral squamous cell carcinoma (OSCC) has not been explored. The current study discovered that LY6K was aberrantly upregulated in OSCC cell lines and tissues and that high LY6K expression significantly correlated with poorer survival of OSCC patients. Through stable knockdown of LY6K, we found that the growth, colony formation, migration, and invasion of OSCC cells were substantially suppressed. In addition, tumor growth and lung metastasis in vivo were effectively inhibited by LY6K depletion. Mechanically, LY6K binds with CAV-1 and activates CAV-1-mediated MAPK/ERK signaling to exert its oncogenic effects on OSCC. In addition, LY6K expression in OSCC was discovered to be regulated by FTO-mediated RNA N6-methyladenosine (m6A) modification in an IGF2BP1-dependent manner. Generally, LY6K expression was upregulated by FTO-mediated demethylation in OSCC, which promoted the tumorigenesis and metastasis of OSCC via activating the CAV-1-mediated ERK1/2 signaling pathway.

Oral squamous cell carcinoma identification by FTIR spectroscopy of oral biofluids

OBJECTIVES

This case study evaluated the efficacy of mid-infrared spectroscopy on the identification of oral squamous cell carcinoma, following the assessment of unstimulated whole saliva.

STUDY DESIGN AND METHODS

The trial follows a matched case-control design. Saliva samples were characterized through mid-infrared spectroscopy, and chemometric tools were applied to distinguish between case and control participants, further identifying the spectral regions that played a pivotal role in the successful identification of oral squamous cell carcinoma.

RESULTS

Mid-infrared spectroscopy was capable to discriminate between cancer patients and matched controls with 100% of correct predictions. Additionally, the spectral regions mostly contributing to the successful prediction were identified and found to be potentially associated with significant molecular changes crucial to the carcinogenic process.

CONCLUSION

The application of mid-infrared spectroscopy in saliva analysis may be regarded as an innovative, noninvasive, low cost, and sensitive technique contributing to the identification of oral squamous cell carcionma.

The utilization of blood serum ATR-FTIR spectroscopy for the identification of gastric cancer

Gastric cancer represents a significant public health challenge, necessitating advancements in early diagnostic methodologies. This investigation employed attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to conduct a multivariate analysis of human serum. The study encompassed the examination of blood samples from 96 individuals diagnosed with gastric cancer and 96 healthy volunteers. Principal component analysis (PCA) was utilized to interpret the infrared spectral data of the serum samples. Specific spectral bands exhibiting intensity variations between the two groups were identified. The infrared spectral ranges of 3500 ~ 3000 cm⁻1, 1700 ~ 1600 cm⁻1, and 1090 ~ 1070 cm⁻1 demonstrated significant diagnostic value for gastric cancer, likely attributable to differences in protein conformation and nucleic acids. By employing machine learning algorithms to differentiate between gastric cancer patients (n = 96) and healthy controls (n = 96), we achieved a sensitivity of up to 89.7% and a specificity of 87.2%. Receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.901. These findings underscore the potential of our serum-based ATR-FTIR spectroscopy examination method as a straightforward, minimally invasive, and reliable diagnostic test for the detection of gastric cancer.

Diagnosis of common intraosseous lesions of the dentomaxillofacial region by chemometry-assisted FT-IR spectroscopy in dental tissue samples

PURPOSES

This study aimed to determine the differential diagnosis of three intraosseous lesions (odontogenic keratocyst (OKC), central giant cell granuloma (CGCG), and dentigerous cyst (DC)) of the dentomaxillofacial region with very similar radiological and clinical features by using chemometrics assisted FT-IR (Fourier transform infrared) spectroscopy in tissue samples.

METHODS

Tissue samples (exposed to formaldehyde for a similar time) of 20-micron thickness belonging to 19 intraosseous lesions diagnosed histopathologically were obtained from the pathology laboratory. The samples were analyzed by FT-IR spectroscopic method using the 400-4000 cm-1 wavenumber range, and the obtained spectra of the samples were evaluated using the orthogonal partial least squares discriminant analysis (OPLS-DA) algorithm.

RESULTS

The intraosseous lesions with different histopathological diagnoses were accurately and precisely clustered with different FT-IR bands corresponding to the main molecular vibrations, especially the phosphodiester region, of the tissue components using the proposed model with 3 latent variables.

CONCLUSIONS

The model showed high sensitivity and specificity. The present study is the first to report the elucidation of clear spectral differences between similar lesions in the maxillofacial region. In the future, the FT-IR method may be used in the non-destructive classification of similar lesions in the maxillofacial region as an alternative to histopathological evaluation.

An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research

INTRODUCTION

Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis.

MATERIALS AND METHODS

We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded.

RESULTS

A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations.

CONCLUSION

ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.

Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning

OBJECTIVE

The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

METHODS

ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.

RESULTS

A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.

CONCLUSION

Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Perspectives of FTIR as Promising Tool for Pathogen Diagnosis, Sanitary and Welfare Monitoring in Animal Experimentation Models: A Review Based on Pertinent Literature

Currently, there is a wide application in the literature of the use of the Fourier Transform Infrared Spectroscopy (FTIR) technique. This basic tool has also proven to be efficient for detecting molecules associated with hosts and pathogens in infections, as well as other molecules present in humans and animals' biological samples. However, there is a crisis in science data reproducibility. This crisis can also be observed in data from experimental animal models (EAMs). When it comes to rodents, a major challenge is to carry out sanitary monitoring, which is currently expensive and requires a large volume of biological samples, generating ethical, legal, and psychological conflicts for professionals and researchers. We carried out a survey of data from the relevant literature on the use of this technique in different diagnostic protocols and combined the data with the aim of presenting the technique as a promising tool for use in EAM. Since FTIR can detect molecules associated with different diseases and has advantages such as the low volume of samples required, low cost, sustainability, and provides diagnostic tests with high specificity and sensitivity, we believe that the technique is highly promising for the sanitary and stress and the detection of molecules of interest of infectious or non-infectious origin.

Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy

BACKGROUND

Estimation of prognosis of oral squamous cell carcinoma (OSCC) is inaccurate prior to surgery, only being effected following subsequent pathological analysis of the primary tumour and excised lymph nodes. Consequently, a proportion of patients are overtreated, with an increase in morbidity, or undertreated, with inadequate margins and risk of recurrence. We hypothesise that it is possible to accurately characterise clinical outcomes from infrared spectra arising from diagnostic biopsies. In this first step, we correlate survival with IR spectra derived from the primary tumour.

METHODS

Infrared spectra were collected from tumour tissue from 29 patients with OSCC and subject to classification modelling.

RESULTS

The model had a median AUROC of 0.89 with regard to prognosis, a median specificity of 0.83, and a hazard ratio of 6.29 in univariate Cox proportional hazard modelling.

CONCLUSION

The data suggest that FTIR spectra may be a useful early biomarker of prognosis in OSCC.

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy

Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

Detection of Sialic Acid to Differentiate Cervical Cancer Cell Lines Using a Sambucus nigra Lectin Biosensor

Pap smear screening is a widespread technique used to detect premalignant lesions of cervical cancer (CC); however, it lacks sensitivity, leading to identifying biomarkers that improve early diagnosis sensitivity. A characteristic of cancer is the aberrant sialylation that involves the abnormal expression of α2,6 sialic acid, a specific carbohydrate linked to glycoproteins and glycolipids on the cell surface, which has been reported in premalignant CC lesions. This work aimed to develop a method to differentiate CC cell lines and primary fibroblasts using a novel lectin-based biosensor to detect α2,6 sialic acid based on attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and chemometric. The biosensor was developed by conjugating gold nanoparticles (AuNPs) with 5 µg of Sambucus nigra (SNA) lectin as the biorecognition element. Sialic acid detection was associated with the signal amplification in the 1500-1350 cm-1 region observed by the surface-enhanced infrared absorption spectroscopy (SEIRA) effect from ATR-FTIR results. This region was further analyzed for the clustering of samples by applying principal component analysis (PCA) and confidence ellipses at a 95% interval. This work demonstrates the feasibility of employing SNA biosensors to discriminate between tumoral and non-tumoral cells, that have the potential for the early detection of premalignant lesions of CC.

Countermeasure to cell dehydration caused terahertz near-field scanning image deterioration

Most biomedical applications of terahertz (THz) imaging are based on distinguishing the dielectric differences of tissues or cells in the THz band. But changes in bio-sample dehydration during the point-scanning process can lead to time-varying deviations in the imaging results. To eliminate the deviations, we proposed a time-varying dehydration kinetic model by analyzing the water diffusion process. The model is verified by experiments and applied to restore each point close to the initial imaging starting state of fresh cellular samples, compensating for the impact of slow speed point-scanning on image deterioration. This methodology has significantly improved the THz super-resolution imaging quality of fresh cellular samples, and successfully restored the cell contours that had been obscured by the cell dehydration over time. Although the dehydration model is developed in THz near-filed imaging, it also pertains to other spectral systems that operate in the raster-scan mode on fresh bio-samples.

Providing potential solutions by using FT-IR spectroscopy for biofluid analysis: Clinical impact of optical screening and diagnostic tests

BACKGROUND

Currently, the potential of FT-IR spectroscopy for rapid diagnosis of many pathologies has been demonstrated by numerous research studies including those targeting COVID-19 detection. However, the number of clinicians aware of this potential and who are willing to use spectroscopy in their clinics and hospitals is still negligible. In addition, lack of awareness creates a huge gap between clinicians and researchers involved in clinical translation of current FT-IR technology hence hindering initiatives to bring basic and applied research together for the direct benefit of patients.

METHODS

Knowledge and medical training on FT-IR on the side of clinicians should be one of the first steps to be able to integrate it into the list of complementary exams which may be requested by health professionals. Countless FT-IR applications could have a life-changing impact on patients' lives, especially screening and diagnostic tests involving biofluids such as blood, saliva and urine which are routinely non-invasively or minimally-invasively.

RESULTS

Blood may be the most difficult to obtain by the invasive method of collection, but much can be evaluated in its components, and areas such as hematology, infectiology, oncology and endocrinology can be directly benefited. Urine with a relatively simple collection method can provide pertinent information from the entire urinary system, including the actual condition of the kidneys. Saliva collection can be simpler for the patient and can provide information on diseases affecting the mouth and digestive system and can be used to diagnose diseases such as oral cancer in its early-stages. An unavoidable second step is the active involvement of industries to design robust and portable instruments for specific purposes, as the medical community requires user-friendly instruments of advanced computational algorithms. A third step resides in the legal situation involving the global use of the technique as a new diagnostic modality.

CONCLUSIONS

It is important to note that decentralized funds for variety of technologies hinders the training of clinical and medical professionals for the use of newly arising technologies and affect the engagement of these professionals with technology developers. As a result of decentralized funding, research efforts are spread out over a range of technologies which take a long time to get validated and translated to the clinic. Partnership over similar groups of technologies and efforts to test the same technologies while overcoming barriers posed to technology validation in different areas around the globe may benefit the clinical/medical, research and industry community globally.

Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines

Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.

Probing Aberrantly Glycosylated Mucin 1 in Breast Cancer Extracellular Vesicles

Aberrantly glycosylated mucin 1 is a critical prognostic biomarker in breast epithelial cancers. Hypoglycosylated mucin 1 coats the surface of the cancer cells, where O-glycans are predominantly linked via an N-acetylgalactosamine moiety (GalNAc). Cancer cell-derived extracellular vesicles (EVs) carry biomarkers from parent cancer cells to the recipient cells and, therefore, could potentially be leveraged for diagnostics and noninvasive disease monitoring. We devised a label-free approach for identifying glycoprotein mucin 1 overexpression on breast cancer EVs. While exploring a plethora of biochemical (enzyme-linked immunosorbent assay, flow cytometry, and SDS-PAGE) and label-free biophysical techniques (circular dichroism and infrared spectroscopy (IR)) along with multivariate analysis, we discovered that mucin 1 is significantly overexpressed in breast cancer EVs and aberrant glycosylation in mucin 1 could be critically addressed using IR and multivariate analysis targeting the GalNAc sugar. This approach emerges as a convenient and comprehensive method of distinguishing cancer EVs from normal samples and holds potential for nonintrusive breast cancer liquid biopsy screening.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].

Identification of Healthy Tissue from Malignant Tissue in Surgical Margin Using Raman Spectroscopy in Oral Cancer Surgeries

Accurate identification of tissue types in surgical margins is essential for ensuring the complete removal of cancerous cells and minimizing the risk of recurrence. The objective of this study was to explore the clinical utility of Raman spectroscopy for the detection of oral squamous cell carcinoma (OSCC) in both tumor and healthy tissues obtained from surgical resection specimens during surgery. This study enrolled a total of 64 patients diagnosed with OSCC. Among the participants, approximately 50% of the cases were classified as the most advanced stage, referred to as T4. Raman experiments were conducted on cryopreserved tissue samples collected from patients diagnosed with OSCC. Prominent spectral regions containing key oral biomarkers were analyzed using the partial least squares-support vector machine (PLS-SVM) method, which is a powerful multivariate analysis technique for discriminant analysis. This approach effectively differentiated OSCC tissue from non-OSCC tissue, achieving a sensitivity of 95.7% and a specificity of 93.3% with 94.7% accuracy. In the current study, Raman analysis of fresh tissue samples showed that OSCC tissues contained significantly higher levels of nucleic acids, proteins, and several amino acids compared to the adjacent healthy tissues. In addition to differentiating between OSCC and non-OSCC tissues, we have also explored the potential of Raman spectroscopy in classifying different stages of OSCC. Specifically, we have investigated the classification of T1, T2, T3, and T4 stages based on their Raman spectra. These findings emphasize the importance of considering both stage and subsite factors in the application of Raman spectroscopy for OSCC analysis. Future work will focus on expanding our tissue sample collection to better comprehend how different subsites influence the Raman spectra of OSCC at various stages, aiming to improve diagnostic accuracy and aid in identifying tumor-free margins during surgical interventions.

Rapid detection of serological biomarkers in gallbladder carcinoma using fourier transform infrared spectroscopy combined with machine learning

Gallbladder cancer (GBC) is the most common malignant tumour of the biliary tract. GBC is difficult to diagnose and treat at an early stage because of the lack of effective serum markers and typical symptoms, resulting in low survival rates. This study aimed to investigate the applicability of dried serum Fourier-transform infrared (FTIR) spectroscopy combined with machine learning algorithms to correctly differentiate patients with GBC from patients with gallbladder disease (GBD), cholangiocarcinoma (CCA), hepatocellular carcinoma (HCC) and healthy individuals. The differentiation between healthy individuals and GBC serum was better using principal component analysis (PCA) and linear discriminant analysis (LDA) for six spectral regions, especially in the protein (1710-1475 cm^-1) and combined (1710-1475 + 1354-980 cm^-1) region. However, the PCA-LDA model poorly differentiated GBC from GBD, CCA, and HCC in serum spectra. We evaluated the PCA- LDA, PCA-support vector machine (SVM), and radial basis kernel function support vector machine (RBF-SVM) models for GBC diagnosis and found that the RBF-SVM model performed the best, with 88.24-95% accuracy, 95.83% sensitivity, and 78.38-94.44% specificity in the 1710-1475 + 1354-980 cm^-1 region. This study demonstrated that serum FTIR spectroscopy combined with the RBF-SVM algorithm has great clinical potential for GBC screening.

Parallelism error analysis and its effect on modulation depth based on a rotating parallel mirror Fourier spectrometer

The stability of the moving mirror of a Michelson Fourier transform spectrometer (M-FTS) has a non-negligible influence on its spectral quality, which limits its application. We proposed a spectrometer scheme with a pair of rotating parallel mirrors (RPM-FTS), which has advantages of fast response and high stability. The influence of the parallelism error of parallel mirrors on interference was analyzed by establishing a rotation vector model between the parallelism error, rotation angle, and optical path. The modulation depth of the RPM-FTS is more insensitive with the same installation error of the M-FTS; thus, more spectral details can be displayed easily.

Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy

Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.

Flexible Multi-Element Photothermoelectric Detectors Based on Spray-Coated Graphene/Polyethylenimine Composites for Nondestructive Testing

Photothermoelectric (PTE) detectors receive much attention owing to the superiority of self-powered, non-bias input, and friendly ambient environments, facilitating abundant prospective applications in industrial inspection, medical diagnostics, homeland security, and wearable Internet of Things. However, many drawbacks of currently applicable PTE materials, involving unstable material oxidation, an uncontrollable fabrication process, and unscalable manufacturing, hinder the development of industrial productions. Herein, we demonstrate a vertical graphene/polyethylenimine composite PTE detector fabricated with an optimized spray-coating method in compact alignment on various surfaces, achieving a significant photovoltage detectivity and responsivity of 6.05 × 10⁷ cm Hz^1/2 W^-1 and 2.7 V W^-1 response at a 973 K blackbody temperature radiation (2.98 μm peak wavelength). In addition, the long-term stability and resistible concave and convex bending flexibility are presented. Furthermore, a nondestructive testing system is established and verified through high-spatial-resolution and high-penetration illustration. Overall, the spray-coated and flexible PTE graphene/polyethylenimine multi-elements with broadband infrared absorption compatibility and stable energy conversion are promising candidates for future health monitoring and wearable electronics.

Circ_0004491 stimulates guanine nucleotide-binding protein alpha subunit to inhibit the malignant progression of oral squamous cell carcinoma by sponging miR-2278

Background/purpose

Circular RNA origin recognition complex subunit 4 (circORC4; ID: hsa_circ_0004491) have been confirmed to be a novel potential biomarker of oral squamous cell carcinoma (OSCC). This study aimed to explore the molecular mechanism of circ_0004491 in OSCC progression.

Materials and methods

Levels of circ_0004491, microRNA (miR)-2278, guanine nucleotide-binding protein alpha subunit (GNAS), Bax, Bcl-2, E-cadherin and ki-67 were detected by quantitative real-time PCR, western blotting and immunohistochemistry. The proliferation of OSCC cells was measured using colony formation assay and EdU staining. Cell apoptosis and motility were detected by flow cytometry and transwell assays respectively. Interaction between miR-2278 and circ_0004491 or GNAS was predicted by bioinformatics analysis and confirmed via luciferase reporter assay and RNA immunoprecipitation assay. Xenograft tumor model was used to analyze the role of circ_0004491 in tumor growth in vivo.

Results

Circ_0004491 was downregulated in OSCC tissues and cell lines. Circ_0004491 overexpression suppressed the proliferation, migration and invasion whereas facilitated the apoptosis of OSCC cells. Circ_0004491 acted as a molecular sponge for miR-2278, and circ_0004491 overexpression-mediated effect was partly reversed by miR-2278 mimic in OSCC cells. MiR-2278 interacted with the 3'UTR of GNAS. Circ_0004491 contributed to GNAS level by sponging miR-2278 in OSCC cells. GNAS knockdown restored miR-2278 inhibitor-mediated effect in OSCC cells. Circ_0004491 overexpression repressed xenograft tumor growth in vivo.

Conclusion

Circ_0004491 can repress OSCC progression by regulation of miR-2278/GNAS axis, providing a possible circRNA-targeted therapy for OSCC.

Breast cancer early detection by using Fourier-transform infrared spectroscopy combined with different classification algorithms

Early detection of breast cancer is of great value in improving the prognosis. The current detection methods of breast cancer have their own limitations. In this study, we investigated the feasibility of Fourier Transform Infrared (FT-IR) spectroscopy combined with different classification algorithms for the early detection of breast cancer in a large sample of 526 patients, including 308 invasive breast cancer, 101 ductal carcinoma in situ, and 117 healthy controls. The serum was measured with FT-IR spectroscopy. Kennard-Stone (KS) algorithm was used to divide the data into the training set and testing set. Support vector machine (SVM) model and back propagation neural network (BPNN) model were used to distinguish ductal carcinoma in situ, invasive breast cancer from healthy controls. The accuracies of the SVM model and BPNN model were 92.9% and 94.2%. To determine the effect of different material absorption bands on early detection, the band was divided into four parts including 900-1425 cm^-1, 1475-1710 cm^-1, 2800-3000 cm^-1, and 3090-3700 cm^-1, to be modeled and detected respectively. The final results showed that the ranges 900-1425 cm^-1 and 1475-1710 cm^-1 had superior classification accuracies. The region 900-1425 cm^-1 corresponded to the lipids, proteins, sugar, and nucleic acids, and the region 1475-1710 cm^-1 corresponded to the proteins. The biochemical substances in other bands also contributed some unique potential to the classification, so the classification accuracy was the best in the full band. The study indicates that serum FT-IR spectroscopy combined with SVM and BPNN models is an effective tool for the early detection of breast cancer.

Quantitative protein sensing with germanium THz-antennas manufactured using CMOS processes

The development of a CMOS manufactured THz sensing platform could enable the integration of state-of-the-art sensing principles with the mixed signal electronics ecosystem in small footprint, low-cost devices. To this aim, in this work we demonstrate a label-free protein sensing platform using highly doped germanium plasmonic antennas realized on Si and SOI substrates and operating in the THz range of the electromagnetic spectrum. The antenna response to different concentrations of BSA shows in both cases a linear response with saturation above 20 mg/mL. Ge antennas on SOI substrates feature a two-fold sensitivity as compared to conventional Si substrates, reaching a value of 6 GHz/(mg/mL), which is four-fold what reported using metal-based metamaterials. We believe that this result could pave the way to a low-cost lab-on-a-chip biosensing platform.

Molecular modeling and in vitro studies of gedunin a potent alpha-amylase inhibitor and alpha-glucosidase inhibitor

The present study explores the potential of the Azadirachta indica (Neem) plant parts (stem and bark) component gedunin for inhibition of alpha-amylase and alpha glucosidase. In contrast, Methanol at 50 mg/ml and 65 mg/ml had the lowest IC50 in alpha glucosidase and alpha amylase with noncompetitive and mixed inhibition, respectively. Azadirachta indica seeds collected from ICAR showed anti-diabetes activity in vitro and in vivo seeds collected were subjected to soxhlet and nonsoxhlet techniques followed by chromatography. HR-LCMS, HPLC, and FTIR to comprehend phytoconstituents present in the extract were used to comprehend phytoconstituents present in the extract and showed the presence gedunin. Among many hits observed, gedunin was used for docking studies using ICM software and for molecular dynamic simulation using gromacs. The results show significant alpha-amylase inhibitory activity and alpha glucosidase inhibitory activity and interaction of ligand targeting these enzymes, which can be used for cross-validation, in vitro using ligplot maps and visualization.

Identification of hepatocellular carcinoma and paracancerous tissue based on the peak area in FTIR microspectroscopy

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies across the world. The annual incidence and death rates have increased at the highest rate of all cancers in recent years. Surgical resection is a potentially curative option for solitary HCC or unilobar disease without evidence of metastases or vascular invasion. This study focuses on the molecular differences between the HCC foci and paracancerous tissues and provides some valuable biomarkers based on the vibrational spectrum. Fourier transform infrared (FTIR) spectroscopy is a non-invasive and qualitative and semi-quantitative analysis technique that has been widely applied for the identification of macromolecular changes in biological tissues. In this study, the FTIR spectra of the HCC foci and the paracancerous tissues were recorded separately, and ten areas under the absorption peaks of all the specimens were calculated. The result demonstrates that the areas of protein-related absorption peaks at 1398 cm^-1, 1548 cm^-1, 1654 cm^-1 and 3070 cm^-1 may be the key indicators of the two different regions. After coupling with the classification algorithms of k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM), it was found that SVM with an RBF kernel performed best with the AUC (area under the ROC curve) reaching 0.997, and the performance was better than the feature based on the full spectrum. This reveals that the peak area-based FTIR spectra combined with the SVM algorithm may be a promising tool in identifying the HCC foci and the paracancerous tissues.

Towards super-resolved terahertz microscopy for cellular imaging

Biomedical imaging includes the use of a variety of techniques to study organs and tissues. Some of the possible imaging modalities are more spread at clinical level (CT, MRI, PET), while others, such as light and electron microscopy are preferred in life sciences research. The choice of the imaging modalities can be based on the capability to study functional aspects of an organism, the delivered radiation dose to the patient, and the achievable resolution. In the last few decades, spectroscopists and imaging scientists have been interested in the use of terahertz (THz) frequencies (30 μm to 3 mm wavelength) due to the low photon energy associated (E∼1 meV, not causing breaking of the molecular bonds but still interacting with some vibrational modes) and the high penetration depth that is achievable. THz has been already adopted in security, quality control and material sciences. However, the adoption of THz frequencies for biological and clinical imaging means to face, as a major limitation, the very scarce resolution associated with the use of such long wavelengths. To address this aspect and reconcile the benefit of minimal harmfulness for bioimaging with the achievable resolving power, many attempts have been made. This review summarises the state-of-the-art of THz imaging applications aimed at achieving super-resolution, describing how practical aspects of optics and quasi-optics may be treated to efficaciously implement the use of THz as a new low-dose and versatile modality in biomedical imaging and clinical research.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

FT-IR Spectral Signature of Sensitive and Multidrug-Resistant Osteosarcoma Cell-Derived Extracellular Nanovesicles

Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. Despite aggressive treatment regimens, the outcome is unsatisfactory, and multidrug resistance (MDR) is a pivotal process in OS treatment failure. OS-derived extracellular vesicles (EVs) promote drug resistance to chemotherapy and target therapy through different mechanisms. The aim of this study was to identify subpopulations of osteosarcoma-EVs by Fourier transform infrared spectroscopy (FT-IR) to define a specific spectral signature for sensitive and multidrug-resistant OS-derived EVs. EVs were isolated from sensitive and MDR OS cells as well as from mesenchymal stem cells by differential centrifugation and ultracentrifugation. EVs size, morphology and protein expression were characterized. FT-IR/ATR of EVs spectra were acquired in the region of 400–4000 cm⁻¹ (resolution 4 cm⁻¹, 128 scans). The FT-IR spectra obtained were consistently different in the EVs compared to cells from which they originate. A specific spectral signature, characterized by a shift and a new band (1601 cm⁻¹), permitted to clearly distinguish EVs isolated by sensitive and multidrug-resistant OS cells. Our data suggest that FT-IR spectroscopy allows to characterize and define a specific spectral signature for sensitive and MDR OS-derived EVs.

Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning

The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm⁻¹. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with highR c 2 of 0.8949 andR v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.

Methylglyoxal Adducts Levels in Blood Measured on Dried Spot by Portable Near-Infrared Spectroscopy

The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.

Comprehensive Evaluation of PAXgene Fixation on Oral Cancer Tissues Using Routine Histology, Immunohistochemistry, and FTIR Microspectroscopy

The choice of tissue fixation is critical for preserving the morphology and biochemical information of tissues. Fragile oral tissues with lower tensile strength are challenging to process for histological applications as they are prone to processing damage, such as tissue tear, wrinkling, and tissue fall-off from slides. This leads to loss of morphological information and unnecessary delay in experimentation. In this study, we have characterized the new PAXgene tissue fixation system on oral buccal mucosal tissue of cancerous and normal pathology for routine histological and immunohistochemical applications. We aimed to minimize the processing damage of tissues and improve the quality of histological experiments. We also examined the preservation of biomolecules by PAXgene fixation using FTIR microspectroscopy. Our results demonstrate that the PAXgene-fixed tissues showed significantly less tissue fall-off from slides. Hematoxylin and Eosin staining showed comparable morphology between formalin-fixed and PAXgene-fixed tissues. Good quality and slightly superior immunostaining for cancer-associated proteins p53 and CK5/6 were observed in PAXgene-fixed tissues without antigen retrieval than formalin-fixed tissues. Further, FTIR measurements revealed superior preservation of glycogen, fatty acids, and amide III protein secondary structures in PAXgene-fixed tissues. Overall, we present the first comprehensive evaluation of the PAXgene tissue fixation system in oral tissues. This study concludes that the PAXgene tissue fixation system can be applied to oral tissues to perform diagnostic molecular pathology experiments without compromising the quality of the morphology or biochemistry of biomolecules.

Probing the Anti-Cancer Potency of Sulfated Galactans on Cholangiocarcinoma Cells Using Synchrotron FTIR Microspectroscopy, Molecular Docking, and In Vitro Studies

Sulfated galactans (SG) isolated from red alga Gracilaria fisheri have been reported to inhibit the growth of cholangiocarcinoma (CCA) cells, which was similar to the epidermal growth factor receptor (EGFR)-targeted drug, cetuximab. Herein, we studied the anti-cancer potency of SG compared to cetuximab. Biological studies demonstrated SG and cetuximab had similar inhibition mechanisms in CCA cells by down-regulating EGFR/ERK pathway, and the combined treatment induced a greater inhibition effect. The molecular docking study revealed that SG binds to the dimerization domain of EGFR, and this was confirmed by dimerization assay, which showed that SG inhibited ligand-induced EGFR dimer formation. Synchrotron FTIR microspectroscopy was employed to examine alterations in cellular macromolecules after drug treatment. The SR-FTIR-MS elicited similar spectral signatures of SG and cetuximab, pointing towards the bands of RNA/DNA, lipids, and amide I vibrations, which were inconsistent with the changes of signaling proteins in CCA cells after drug treatment. Thus, this study demonstrates the underlined anti-cancer mechanism of SG by interfering with EGFR dimerization. In addition, we reveal that FTIR signature spectra offer a useful tool for screening anti-cancer drugs’ effect.