Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer

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

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

A biospectroscopic approach toward colorectal cancer diagnosis from bodily fluid samples via ATR-MIR spectroscopy combined with multivariate data analysis

In this study, a biospectroscopic approach was reported for the detection of spectral changes and biomarkers for the diagnosis of colorectal cancer (CC) cases from different bodily fluids (blood plasma, blood serum, saliva and colonoscopy disinfection/wash fluids) by using attenuated total reflection-mid infrared (ATR-MIR) spectroscopy. To recognize the molecular level changes in the spectral characteristics of CC and their healthy/control (CH) groups, different multivariate data analyses (HCA, LDA, PCA and SIMCA) were successfully performed over the data of ATR-MIR spectroscopy. Two hundred specimens were characterized in detail over the data of spectral regions (4000-650 cm-1 and regions V-XXII). The findings revealed that significant changes were clearly observed in the concentrations of lipid, protein, nucleic acid and carbohydrate biomolecules for cancer cases based upon their necessity to overcome energy requirements. Supervised multivariate data methodology SIMCA, presented an excellent classification for the studied groups; similarly 100% of the specimens from different bodily fluids were correctly classified by supervised methodology LDA. As a result, the developed ATR-MIR methodology for the classification of CC and their healthy groups highlighted a rapid cancer diagnosis approach from different bodily fluids; therefore, it could be guide to make well decision before histopathological assessment and to screen CC populations existing in society.

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy to mitigate this problem is to embed high-dimensional pixel spectra in lower dimensions, aiming to preserve molecular information in a more compact manner, which reduces the amount of data and promises to make subsequent disease classification more accessible for machine learning procedures. In this study, we compare several dimensionality reduction approaches and their effect on identifying cancer in the context of a colon carcinoma study. We observe surprisingly small differences between convolutional neural networks trained on dimensionality reduced spectra compared to utilizing full spectra, indicating a clear tendency of the convolutional networks to focus on spatial rather than spectral information for classifying disease status.

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

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

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.

Prediction of malignant transformation in oral epithelial dysplasia using machine learning

A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.

Machine-Learning Applications in Oral Cancer: A Systematic Review

Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.

Oral lichen planus identification by mid-infrared spectroscopy of oral biofluids: A case-control study

BACKGROUND AND AIMS

This study aims to access the effectiveness of mid-infrared (MIR) spectroscopy on the identification of the reticular form of OLP, following the assessment of gingival crevicular fluid (GCF) and oral mucosa transudate (OMT).

MATERIAL AND METHODS

The trial follows a case-control design. Samples were characterized through MIR spectroscopy and chemometric tools were applied to distinguish between case and control participants, further identifying the spectral regions with the highest contribution to the developed models.

RESULTS

MIR spectroscopy was capable to discriminate between OLP patients and controls with 95.1% and 85.4% of correct predictions, regarding GCF and OMT samples, respectively. Additionally, the spectral regions mostly contributing to the successful prediction were identified, and possibly related with the distinctive presence of amino acids/proteins and oxidative stress mediators in oral biofluids, supporting the role of the immune-inflammatory activation on OLP etiology and disease course.

CONCLUSION

MIR spectroscopy analysis of GCF and OMT may be regarded as an innovative, non-invasive, low cost and sensitive technique, contributing to the identification of the reticular from of OLP.

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

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

A rapid diagnostic approach for gastric and colon cancers via Fourier transform mid-infrared spectroscopy coupled with chemometrics from paraffin-embedded tissues

This paper describes the feasibility of Attenuated total reflection-Fourier transform mid-infrared (ATR-MIR) spectroscopy method coupled with chemometrics for the rapid diagnostic approach and screening spectral changes for gastric and colon cancers from paraffin-embedded tissues. A total number of 82 tissue samples were analyzed by a simple ATR-MIR method combined with PCA, HCA, SIMCA and LDA methodologies. Spectral analyses showed significant differences for the molecular contents particularly about the lipid, nucleic acid, protein and other biomolecules in the samples of gastric cancer (GC) and colon cancer (CC) groups from their control/healthy groups. Significant changes in the characteristic of these molecules were only observed for cancer groups based upon the increment in their biosynthesis, and they could be utilized as diagnostic spectral biomarkers. Under the optimum conditions, SIMCA provided excellent classification for diseased and control groups, with 5% significance level. As well, 97.75% of the studied tissue samples were correctly discriminated on the basis of their origin by LDA. Consequently, the findings of this study highlighted the rapid diagnosis of gastric and colon cancer cases from paraffin-embedded tissues via ATR-MIR spectroscopy complemented with chemometrics.

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.

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm⁻¹ and 1285 cm⁻¹. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 μm. The image formed from the ratio of the SNOM images obtained at 1252 cm⁻¹ and 1285 cm⁻¹ shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm⁻¹ signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 μm wide and the SNOM topography shows that the core centre is raised by ∼1 μm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 μm wide, roughly the width of two cancer cells.

A SNOM image (a) provides chemical insight into a metastatic tumour identified by H&E staining (b).

Saliva based non invasive screening of Oral Submucous Fibrosis using ATR-FTIR spectroscopy

Oral Submucous Fibrosis (OSMF) is a type of precancerous condition of Oral cancer and considered to have the greatest malignant potential. Biopsy is an ultimate option for the conformation of the malignancy. But the invasiveness of the procedure makes it interdict. Therefore, there is an urgent need to identify effective screening and diagnostic methods which would be less invasive, rapid, more accurate and cost effective. Here, we used Attenuated Total Reflection- Fourier transform infrared spectroscopy (ATR-FTIR) with Chemometric analysis coupled with estimation of total salivary protein to discriminate OSMF and Healthy Control (HC). The present study showed the specific Infrared spectrum for OSMF patients, which was specifically differentiated from HC based on the spectral shift of proteins/amide II, carbohydrate and nucleic acid using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) with small data sets. ATR-FTIR spectroscopy of saliva coupled with total protein estimation can be used to discriminate between OSMF and HC. However, large sample size should be needed to evaluate the ATR-FTIR for consideration as a screening tool for an early diagnosis OSMF.

Infrared Microspectroscopy With Multivariate Analysis to Differentiate Oral Hyperplasia From Squamous Cell Carcinoma: A Proof of Concept for Early Diagnosis

BACKGROUND AND OBJECTIVES

Despite having numerous advances in therapeutics, mortality and morbidity due to oral cancer incidence are still very high. Early detection can improve the chances of survival in most patients. However, diagnosis at early stages can be challenging as premalignant conditions are usually asymptomatic. Currently, histological assessment remains the gold standard for diagnosis. Early diagnosis poses challenges to pathologists due to less severe morphological changes associated with early stages. Therefore, a fast and robust method of detection based on molecular changes is needed for early diagnosis. © 2021 Wiley Periodicals LLC.

STUDY DESIGN/MATERIAL AND METHODS

In the present study, Fourier transform infrared (FTIR) spectroscopic imaging has been used to differentiate early-stage oral hyperplasia from adjacent normal (AN) and oral squamous cell carcinoma (OSCC). Hyperplasia is often considered as an initial event in the pathogenesis of oral cancer and OSCC is the most common advanced stage of malignancy. Differentiating normal versus hyperplasia and hyperplasia versus OSCC can remain quite challenging on occasion using conventional staining as the histological assessment is based on morphological changes.

RESULTS

Unsupervised hierarchical cluster analysis (UHCA) has been performed on FTIR images of multiple tissues together that provided some degree of classification among tissue groups. The AN epithelium clustered distinctively using UHCA from both hyperplasia and grades 1 and 2 of OSCC. An increase in the content of DNA, denaturation of protein, and altered lipid structures were more clearly elucidated with spectral analysis.

CONCLUSION

This study demonstrates a simple strategy to differentiate early-stage oral hyperplasia from AN and OSCC using UHCA. This study also proposes a future alternative method where FTIR imaging can be used as a diagnostic tool for cancer at early stages.

The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer

Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high rates of disease-related morbidity and mortality due to advanced loco-regional stage at diagnosis. Early detection and prompt treatment offer the best outcomes to patients, yet the majority of OC lesions are detected at late stages with 45% survival rate for 2 years. The primary cause of poor OC outcomes is unavailable or ineffective screening and surveillance at the local point-of-care level, leading to delays in specialist referral and subsequent treatment. Lack of adequate awareness of OC among the public and professionals, and barriers to accessing health care services in a timely manner also contribute to delayed diagnosis. As image analysis and diagnostic technologies are evolving, various artificial intelligence (AI) approaches, specific algorithms and predictive models are beginning to have a considerable impact in improving diagnostic accuracy for OC. AI based technologies combined with intraoral photographic images or optical imaging methods are under investigation for automated detection and classification of OC. These new methods and technologies have great potential to improve outcomes, especially in low-resource settings. Such approaches can be used to predict oral cancer risk as an adjunct to population screening by providing real-time risk assessment. The objective of this study is to (1) provide an overview of components of delayed OC diagnosis and (2) evaluate novel AI based approaches with respect to their utility and implications for improving oral cancer detection.

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

Effect of cigarette smoke extract on mitochondrial heme-metabolism: An in vitro model of oral cancer progression

Tobacco smoking is considered as one of the major risk factors for development of oral cancer. In vitro studies indicate that cigarette smoke initiates transformation of epithelial cells toward development of oral cancer through altering mitochondrial metabolic pathways. However the present in vitro models need to be improved to correlate these molecular changes with epithelial transformations. In present study, we investigated the association of mitochondrial metabolic events with oral cancer progression under cigarette smoke extract (CSE). In this regard, an in vitro model of oral keratinocyte cell line (MOE1A) was developed by exposing them with different concentrations of CSE. Alterations in cellular phenomena were confirmed by Fourier-transform infrared spectroscopy (FTIR) study, which indicated changes in important functional groups of CSE-induced oral cells. Enhanced reactive oxygen species (ROS) of exposed cells altered the mitochondrial metabolic activities in terms of increased mitochondrial mass and DNA content. Further, mitochondrial heme-metabolism was investigated and real-time PCR study showed altered expression of important genes like ALAS1, ABCB6, CPOX, FECH, HO-1. Both transcriptomic and proteomic studies showed up- and down-regulation of important biomarkers related to cellular cancer progression. Overall data suggest that CSE alters mitochondrial heme metabolic pathway and initiates cancer progression through modifying cellar biomarkers in oral epithelial cells.

Biochemical Changes in Irradiated Oral Mucosa: A FTIR Spectroscopic Study

Radiation exposure during the course of treatment in head and neck cancer (HNC) patients can induce both structural and biochemical anomalies. The present study is focused on utilizing infrared imaging for the identification of the minor biochemical alterations in the oral mucosa. Chemical maps generated using glycoprotein band indicates its differential distribution along the superficial layer. Spectra extracted from this layer suggests changes in overall nucleic acid and protein content in response to the therapeutic irradiation. Discrimination among control and irradiated groups have been achieved using principal component analysis. Findings of this preliminary study further support prospective utilization of Fourier Transform InfraRed (FTIR) imaging as a non-destructive, label-free tool for objective assessment of the oral mucosa in patient groups with or without radiation therapy.

Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer

FTIR spectroscopy and Raman spectroscopy of biological analytes are increasingly explored as screening tools for early detection of cancer.

Raman and infrared spectroscopy reveal that proliferating and quiescent human fibroblast cells age by biochemically similar but not identical processes

Dermal fibroblast cells can adopt different cell states such as proliferation, quiescence, apoptosis or senescence, in order to ensure tissue homeostasis. Proliferating (dividing) cells pass through the phases of the cell cycle, while quiescent and senescent cells exist in a non-proliferating cell cycle-arrested state. However, the reversible quiescence state is in contrast to the irreversible senescence state. Long-term quiescent cells transit into senescence indicating that cells age also when not passing through the cell cycle. Here, by label-free in vitro vibrational spectroscopy, we studied the biomolecular composition of quiescent dermal fibroblast cells and compared them with those of proliferating and senescent cells. Spectra were examined by multivariate statistical analysis using a PLS-LDA classification model, revealing differences in the biomolecular composition between the cell states mainly associated with protein alterations (variations in the side chain residues of amino acids and protein secondary structure), but also within nucleic acids and lipids. We observed spectral changes in quiescent compared to proliferating cells, which increased with quiescence cultivation time. Raman and infrared spectroscopy, which yield complementary biochemical information, clearly distinguished contact-inhibited from serum-starved quiescent cells. Furthermore, the spectra displayed spectral differences between quiescent cells and proliferating cells, which had recovered from quiescence. This became more distinct with increasing quiescence cultivation time. When comparing proliferating, (in particular long-term) quiescent and senescent cells, we found that Raman as well as infrared spectroscopy can separate these three cellular states from each other due to differences in their biomolecular composition. Our spectroscopic analysis shows that proliferating and quiescent fibroblast cells age by similar but biochemically not identical processes. Despite their aging induced changes, over long time periods quiescent cells can return into the cell cycle. Finally however, the cell cycle arrest becomes irreversible indicating senescence.

Sequestration of carbon dioxide and production of biomolecules using cyanobacteria

A cyanobacterial strain, Synechococcus sp. NIT18, has been applied to sequester CO₂ using sodium carbonate as inorganic carbon source due to its efficiency of CO₂ bioconversion and high biomass production. The biomass obtained is used for the extraction of biomolecules - protein, carbohydrate and lipid. The main objective of the study is to maximize the biomass and biomolecules production with CO₂ sequestration using cyanobacterial strain cultivated under different concentrations of CO₂ (5-20%), pH (7-11) and inoculum size (5-12.5%) within a statistical framework. Maximum sequestration of CO₂ and maximum productivities of protein, carbohydrate and lipid are 71.02%, 4.9 mg/L/day, 6.7 mg/L/day and 1.6 mg/L/day respectively, at initial CO₂ concentration: 10%, pH: 9 and inoculum size: 12.5%. Since flue gas contains 10-15% CO₂ and the present strain is able to sequester CO₂ in this range, the strain could be considered as a useful tool for CO₂ mitigation for greener world.

3D chemical imaging of the brain using quantitative IR spectro-microscopy

Three-dimensional (3D) histology is the next frontier for modern anatomo-pathology. Characterizing abnormal parameters in a tissue is essential to understand the rationale of pathology development. However, there is no analytical technique, in vivo or histological, that is able to discover such abnormal features and provide a 3D distribution at microscopic resolution. Here, we introduce a unique high-throughput infrared (IR) microscopy method that combines automated image correction and subsequent spectral data analysis for 3D-IR image reconstruction. We performed spectral analysis of a complete organ for a small animal model, a mouse brain with an implanted glioma tumor. The 3D-IR image is reconstructed from 370 consecutive tissue sections and corrected using the X-ray tomogram of the organ for an accurate quantitative analysis of the chemical content. A 3D matrix of 89 × 106 IR spectra is generated, allowing us to separate the tumor mass from healthy brain tissues based on various anatomical, chemical, and metabolic parameters. We demonstrate that quantitative metabolic parameters can be extracted from the IR spectra for the characterization of the brain vs. tumor metabolism (assessing the Warburg effect in tumors). Our method can be further exploited by searching for the whole spectral profile, discriminating tumor vs. healthy tissue in a non-supervised manner, which we call 'spectromics'.

"Omics" in oral cancer: New approaches for biomarker discovery

OBJECTIVES

In this review paper, we explored the application of "omics" approaches in the study of oral cancer (OC). It will provide a better understanding of how "omics" approaches may lead to novel biomarker molecules or molecular signatures with potential value in clinical practice. A future direction of "omics"-driven research in OC is also discussed.

METHODS

Studies on "omics"-based approaches [genomics/proteomics/transcriptomics/metabolomics] were investigated for differentiating oral squamous cell carcinoma,oral sub-mucous fibrosis, oral leukoplakia, oral lichen planus, oral erythroplakia from normal cases. Electronic databases viz., PubMed, Springer, and Google Scholar were searched.

RESULTS

One eighty-one studies were included in this review. The review shows that the fields of genomics, transcriptomics, proteomics, and metabolomics-based marker identification have implemented advanced tools to screen early changes in DNA, RNA, protein, and metabolite expression in OC population.

CONCLUSIONS

It may be concluded that despite advances in OC therapy, symptomatic presentation occurs at an advanced stage, where various curative treatment options become very limited. A molecular level study is essential for detecting an OC biomarker at an early stage. Modern "Omics" strategies can potentially make a major contribution to meet this need.

Fourier transform infra-red spectroscopic signatures for lung cells' epithelial mesenchymal transition: A preliminary report

Infra red (IR) spectral characterization can provide label-free cellular metabolic signatures of normal and diseased circumstances in a rapid and non-invasive manner. Present study endeavoured to enlist Fourier transform infra red (FTIR) spectroscopic signatures for lung normal and cancer cells during chemically induced epithelial mesenchymal transition (EMT) for which global metabolic dimension is not well reported yet. Occurrence of EMT was validated with morphological and immunocytochemical confirmation. Pre-processed spectral data was analyzed using ANOVA and principal component analysis-linear discriminant analysis (PCA-LDA). Significant differences observed in peak area corresponding to biochemical fingerprint (900-1800cm^-1) and high wave-number (2800-3800cm^-1) regions contributed to adequate PCA-LDA segregation of cells undergoing EMT. The findings were validated by re-analysis of data using another in-house built binary classifier namely vector valued regularized kernel approximation (VVRKFA), in order to understand EMT progression. To improve the classification accuracy, forward feature selection (FFS) tool was employed in extracting potent spectral signatures by eliminating undesirable noise. Gradual increase in classification accuracy with EMT progression of both cell types indicated prominence of the biochemical alterations. Rapid changes in cellular metabolome noted in cancer cells within first 24h of EMT induction along with higher classification accuracy for cancer cell groups in comparison to normal cells might be attributed to inherent differences between them. Spectral features were suggestive of EMT triggered changes in nucleic acid, protein, lipid and bound water contents which can emerge as the useful markers to capture EMT related cellular characteristics.

Global spectral and local molecular connects for optical coherence tomography features to classify oral lesions towards unravelling quantitative imaging biomarkers

The biopsy based diagnosis of oral precancers like leukoplakia (OLK) and submucous fibrosis (OSF) as well as squamous cell carcinoma (OSCC) suffers from observer specific variability.