Satarupa Banerjee

Efficient extraction strategies of tea (Camellia sinensis) biomolecules

Tea is a popular daily beverage worldwide. Modulation and modifications of its basic components like catechins, alkaloids, proteins and carbohydrate during fermentation or extraction process changes organoleptic, gustatory and medicinal properties of tea. Through these processes increase or decrease in yield of desired components are evident. Considering the varied impacts of parameters in tea production, storage and processes that affect the yield, extraction of tea biomolecules at optimized condition is thought to be challenging. Implementation of technological advancements in green chemistry approaches can minimize the deviation retaining maximum qualitative properties in environment friendly way. Existed extraction processes with optimization parameters of tea have been discussed in this paper including its prospects and limitations. This exhaustive review of various extraction parameters, decaffeination process of tea and large scale cost effective isolation of tea components with aid of modern technology can assist people to choose extraction condition of tea according to necessity.

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

Correction for ‘Global spectral and local molecular connects for optical coherence tomography features to classify oral lesions towards unravelling quantitative imaging biomarkers’ by Satarupa Banerjee et al., RSC Adv., 2016, 6, 7511–7520.

Molecular Pathology Signatures in Predicting Malignant Potentiality of Dysplastic Oral Pre-cancers

The role of dysplastic oral pre-cancers in oral squamous cell carcinoma development is well recognized, but the notion is not exclusive. Diagnostic gold standards in predicting malignant potentiality of such pre-cancers suffer from ambiguity due to inter- and intra-observer variability. In addressing such diagnostic challenges, combinatorial appraisement of molecular pathology attributes encompassing cancer hallmarks is thought to provide a wider analytical sense. Two major premalignant disorders, viz. oral leukoplakia and oral submucous fibrosis have been considered as candidate precursors of cancer here. This review highlights the molecular pathology signatures expressed in oral epithelial dysplasia and revisits the usefulness of combinatorial analysis of expressional pattern of existing molecular biomarkers in the context of proper selection of cardinal attributes from each cancer hallmark for better malignant potentiality assessment.

Transfer learning of tissue photon interaction in optical coherence tomography towardsin vivo histology of the oral mucosa.

Oral cancer evolves from different premalignant conditions and the key to save lives is through diagnosis of early symptoms. The conventional practice of post biopsy histopathology reporting is dependent on specificity of sampling site and optical coherence tomography (OCT) imaging is clinically used for guidance. Clinicians infer the tissue constitution by interpreting intensity images and are challenged by inter-and intra-observer variability. In this paper we propose transfer learning of tissue specific photon interaction statistical physics in swept-source OCT for characterizing the oral mucosa with the aim of reducing this reporting variability. The source task models statistical physics of ballistic and near-ballistic photons and its intensity attenuation and target task learns the parameters obtained by solving the source task to identify co-located heterogeneity of tissues. Performance is compared with conventional histopathology of healthy, premalignant and malignant oral lesions supporting its use towards in vivo histology of the oral mucosa for pre-biopsy screening.

Multimodal diagnostic segregation of oral leukoplakia and cancer

Oral leukoplakia (OLK) is the most common pre malignant disorder (PMD) with highest malignant potentiality. It is clinically highly correlated with oral squamous cell carcinoma (OSCC). Painful biopsy is the gold standard till date for diagnosis of these diseases. Again for specific grading of such pathological states and mitigation of inter and intra observer variability and subjective disease classification, alternative of molecular biomarkers for diseases differentiation as well as non-invasive modalities are yet to be explored. In this study role of morphometric, intensity and textural features extracted from liquid based exfoliative cytology (LBEC) and intensity and textural features extracted from ex vivo optical coherence tomography (OCT) images and spectral features from difference between mean spectra (DBMS) were evaluated for disease classification using variants of support vector machine (SVM) classifiers. Result showed that at 10 fold cross-validation OLK and OSCC could be differentiated using cellular features of LBEC data at 100% sensitivity and specificity. Spectral biomarkers were also extracted efficiently which could classify the diseases with 81.3% sensitivity and 91.3% specificity depicting role of chemical molecules responsible in pathological alteration. Considering the advantage of each modality, it can be concluded that these features can utilized as disease differentiation markers and have useful clinical implication for diagnosis of OLK and OSCC towards mitigation of inter- as well as intra observer variability faced during routine histopathological diagnostic procedure.

A reductionist approach to extract robust molecular markers from microarray data series Isolating markers to track osseointegration

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured; MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes.

Functional stratification of biomarkers selected from microarray data for understanding oral leukoplakia associated carcinogenesis

Oral carcinogenesis, a multistep phenomenon often precedes by oral pre-cancers like leukoplakia (OLK). Differentially expressed (DE) gene analysis of microarray data followed by functional classification provides an idea of alteration of biological functions associated with disease progression. In this context, microRNA (miRNA) microarray data analysis for functional classification is still a challenge, since most of the functions of miRNAs are yet to be discovered. The rationale of this study is to identify a subset of miRNA from microarray data, efficiency of which were evaluated using support vector machine for optimal supervised classification of OLK and OLK transformed oral squamous cell carcinoma (LK-OSCC). Another study was performed to identify subset of DE genes from gene expression database for OLK and LK-OSCC differentiation. This study further considered functional classification of target genes of DE miRNAs and DE genes and compared to understand OLK associated carcinogenesis mechanism, which is first of its kind. Result suggested that group of eight DE miRNAs, viz. miR-21, miR-142-3p, mir-223, miR-637, miR-142-5p, miR-1184 and miR-31* could classify OLK and LK-OSCC with 100% sensitivity and specificity. During comparative assessment of functionally classified target genes of DE miRNAs and mRNAs, it was observed that beside few common functional gene sets, the exclusive DE genes in miRNA target gene groups were TBC1 domain family and kinesin family while histone clusters, WD repeat domain genes, ribosomal proteins, transmembrane proteins and olfactory related proteins were significant in DE genes. Pathway analysis of the DE genes mainly showed to affect pathways especially for glutathione, drug and tyrosine metabolism as well as melanogenesis. The significant biological processes found to be affected by gene ontology analysis were mainly the response to lipid peroxide and altered secondary metabolite and melanin biosynthesis.