A blockchain-based evaluation approach to analyse customer satisfaction using AI techniques.

Due to technological advancements and consumer demands, online shopping creates new features and adapts to new standards. A robust customer satisfaction prediction model concerning trust and privacy platforms can encourage an organization to make better decisions about its service and quality. This study presented an approach to predict consumer satisfaction using the blockchain-based framework combining the Multi-Dimensional Naive Bayes-K Nearest Neighbor (MDNB-KNN) and the Multi-Objective Logistic Particle Swarm Optimization Algorithm (MOL-PSOA). A regression model is employed to quantify the impact of various production factors on customer satisfaction. The proposed method yields better levels of measurement for customer satisfaction (98%), accuracy (95%), necessary time (60%), precision (95%), and recall (95%) compared to existing studies. Measuring consumer satisfaction with a trustworthy platform facilitates to development of the conceptual and practical distinctions influencing customers' purchasing decisions.

LSTM-DGWO-Based Sentiment Analysis Framework for Analyzing Online Customer Reviews.

Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer's perception of enhancing products from a business perspective.

Identification of α-enolase as a prognostic and diagnostic precancer biomarker in oral submucous fibrosis.

AIMS: Diagnostic ambiguities regarding the malignant potentiality of oral submucous fibrosis (OSF), an oral precancerous condition having dysplastic and non-dysplastic isoforms are the major failure for early intervention of oral squamous cell carcinoma (OSCC) patients. Our goal is to identify proteomic signatures from biopsies that can be used as precancer diagnostic marker for patient suffering from OSF. METHODS: The high throughput techniques adopting de novo peptide sequencing (1D SDS-PAGE coupled nanoLC MALDI tandem mass spectrometry (MS/MS)-based peptide mass fingerprint), immunohistochemistry (IHC), Western blot (WB) and real-time PCR (RT-PCR) analysis are considered for such biomarker identification and multilevel validations. RESULTS: Alpha-enolase is identified as an overexpressed protein in biopsies of oral submucous fibrosis with dysplasia (OSFWD) compared with oral submucous fibrosis without dysplasia (OSFWT) and normal oral mucosa (NOM). Total proteome analysis of an overexpressed protein band around 47 kDa of OSFWD identifies 334 peptides corresponding to 61 human proteins. Among them α-enolase is identified as a prime protein with highest number of peptides (44 out of 334 peptides) and sequence coverage (66.4%). Furthermore, RT-PCR, WB and IHC analysis also show mRNA and tissue level upregulation of α-enolase in OSFWD validating α-enolase as precancer marker. CONCLUSIONS: This study for the first time identifies and validates α-enolase as a novel biomarker for early diagnosis of malignant potentiality of OSF. Hence, the identified protein marker, α-enolase can help in early therapeutic intervention of OSF patients leading to the reduction of patient's pain, treatment cost and enhancement of patient's quality of life.

Computer-aided molecular pathology interpretation in exploring prospective markers for oral submucous fibrosis progression.

BACKGROUND: Evaluation of molecular pathology markers using a computer-aided quantitative assessment framework would help to assess the altered states of cellular proliferation, hypoxia, and neoangiogenesis in oral submucous fibrosis and could improve diagnostic interpretation in gauging its malignant potentiality. METHODS: Immunohistochemical (IHC) expression of c-Myc, hypoxia-inducible factor-1-alpha (HIF-1α), vascular endothelial growth factor (VEGF), VEGFRII, and CD105 were evaluated in 58 biopsies of oral submucous fibrosis using computer-aided quantification. After digital stain separation of original chromogenic IHC images, quantification of the diaminobenzidine (DAB) reaction pattern was performed based on intensity and extent of cytoplasmic, nuclear, and stromal expression. RESULTS: Assessment of molecular expression proposed that c-Myc and HIF-1α may be used as strong screening markers, VEGF for risk-stratification and VEGFRII and CD105 for prognosis of precancer into oral cancer. CONCLUSION: Our analysis indicated that the proposed method can help in establishing IHC as an effective quantitative immunoassay for molecular pathology and alleviate diagnostic ambiguities in the clinical decision process.

Correlated analysis of semi-quantitative immunohistochemical features of E-cadherin, VEGF and CD105 in assessing malignant potentiality of oral submucous fibrosis.

Oral submucous fibrosis, a potentially premalignant condition for oral squamous cell carcinoma, manifests both non-dysplastic and dysplastic grades. Early and specific identification of its malignant potentiality suffers from diagnostic limitations that may be addressed by correlated molecular pathology attributes having histopathological backdrop. Present study correlates expressional alteration in prime epithelial marker E-cadherin, with neo-angiogenic molecules viz. VEGF and CD105 for elucidation of malignant potentiality in different stages of oral submucous fibrosis. Sixty-eight incision biopsies from normal oral mucosa (n = 10), non-dysplastic (n = 18) and different dysplastic grades (n = 40) of oral submucous fibrosis were semi-quantitatively analyzed for immunohistochemical expressions of E-cadherin (membranous and cytoplasmic), VEGF and CD105 which were further statistically correlated. The loss of membranous E-cadherin with increase in cytoplasmic accumulation in differentiative layers of epithelium through the progression of dysplasia was noted along with up-regulation in VEGF expressions. The number of CD105(+) blood vessels and their major axis also showed significant increase from non-dysplasia toward higher grades of dysplasia. The positive correlation between deregulated expression of epithelial cell-cell adhesion molecule and increase in neo-angiogenic attributes of oral submucous fibrosis with increase in dysplastic grades indicated elucidatory potential of molecular expression features in assessment of malignant potentiality in oral submucous fibrosis.

Epithelio-mesenchymal transitional attributes in oral sub-mucous fibrosis.

Evaluating molecular attributes in association with its epithelial and sub-epithelial changes of oral sub-mucous fibrosis is meaningful in exploring the plausibility of an epithelio-mesenchymal transition (EMT) and malignant potentiality of this pathosis. In this study histopathological and histochemical attributes for basement membrane and connective tissue in biopsies of oral sub-mucous fibrosis (n = 55) and normal oral mucosa (n = 16) were assessed and expressions of p63, E-cadherin, ß-catenin, N-cadherin and TWIST were analyzed immunohistochemically. The p63 and its isoforms (TA and ∆N), PARD3, E-cadherin and ß-catenin were also assessed transcriptomically by q-PCR and EMT players like TWIST1, ZEB1, MMP9 and micro-RNA 205 were searched in gene expression microarrays. Oral epithelium demonstrating impairment in progressive maturation in oral sub-mucous fibrosis concomitantly experienced an increase in basement membrane thickness and collagen deposition along with alteration in target molecular expressions. In comparison to non-dysplastic conditions dysplastic stages exhibited significant increase in p63 and p63∆N expressions whereas, E-cadherin and ß-catenin exhibited loss from the membrane with concurrent increase in cytoplasm. Further the N-cadherin and TWIST were gained remarkably along with the appearance of nuclear accumulation features of ß-catenin. The microarray search had noticed the up-regulation of TWIST1, ZEB1 and MMP9 along with down regulation of micro-RNA 205. The simultaneous increase in basement membrane thickness and sub-epithelial collagen deposition were the plausible indicators for increased matrix stiffness with expected impact on oral epithelial functional homoeostasis. This was corroborated with the increase in expressions of epithelial master regulator p63 and its oncogenic isoform (∆N) along with membranous loss of E-cadherin (EMT hallmark) and its associate ß-catein and gain of mesenchymal markers like N-cadherin and TWIST. These also became indicative for the induction of epithelial to mesenchymal transitional mechanism in oral sub-mucous fibrosis when connoted here with the relevant modulation in expressions of EMT regulators.

Assessment of molecular events during in vitro re-epithelialization under honey-alginate matrix ambience.

Re-epithelialization is one of the most important stages of cutaneous regeneration and its success requires supportive micro-ambience which may be provided with suitable bio-matrix. Biocompatibility and efficacy of such bio-matrix in re-epithelialization could be explored by multimodal analysis of structural and functional attributes of in vitro wound healing model including evaluation of prime molecular expressions of the epithelial cells during repair. Present study examines the influence of honey-alginate and alginate matrices on re-epithelialization in keratinocyte (HaCaT) population in a 2-D wound model. Cellular viability, proliferation and cell-cell adhesion status were assessed during wound closure using live/dead cell assay and by evaluating expressions of Ki67, p63 and E-cadherin along-with % change in cellular electrical impedance. Efficacy of honey-alginate matrix in comparison to only alginate one was demonstrated by a quicker reduction in wound gap, improved cellular viability, enhanced expressions of Ki67, p63 and its isoforms (TAp63, ΔNp63) as well as E-cadherin. Faster restoration of electrical attribute (% of impedance change) after wounding also indicated better impact of honey-alginate matrix in re-epithelialization.

Finding an optimum immuno-histochemical feature set to distinguish benign phyllodes from fibroadenoma.

PURPOSE: Benign phyllodes and fibroadenoma are two well-known breast tumors with remarkable diagnostic ambiguity. The present study is aimed at determining an optimum set of immuno-histochemical features to distinguish them by analyzing important observations on expressions of important genes in fibro-glandular tissue. METHODS: Immuno-histochemically, the expressions of p63 and α-SMA in myoepithelial cells and collagen I, III and CD105 in stroma of tumors and their normal counterpart were studied. Semi-quantified features were analyzed primarily by ANOVA and ranked through F-scores for understanding relative importance of group of features in discriminating three classes followed by reduction in F-score arranged feature space dimension and application of inter-class Bhattacharyya distances to distinguish tumors with an optimum set of features. RESULTS: Among thirteen studied features except one all differed significantly in three study classes. F-Ranking of features revealed highest discriminative potential of collagen III (initial region). F-Score arranged feature space dimension and application of Bhattacharyya distance gave rise to a feature set of lower dimension which can discriminate benign phyllodes and fibroadenoma effectively. CONCLUSIONS: The work definitely separated normal breast, fibroadenoma and benign phyllodes, through an optimal set of immuno-histochemical features which are not only useful to address diagnostic ambiguity of the tumors but also to spell about malignant potentiality.

Computational analysis of p63(+) nuclei distribution pattern by graph theoretic approach in an oral pre-cancer (sub-mucous fibrosis).

BACKGROUND: Oral submucous fibrosis (OSF) is a pre-cancerous condition with features of chronic, inflammatory and progressive sub-epithelial fibrotic disorder of the buccal mucosa. In this study, malignant potentiality of OSF has been assessed by quantification of immunohistochemical expression of epithelial prime regulator-p63 molecule in correlation to its malignant (oral squamous cell carcinoma [OSCC] and normal counterpart [normal oral mucosa [NOM]). Attributes of spatial extent and distribution of p63(+) expression in the epithelium have been investigated. Further, a correlated assessment of histopathological attributes inferred from H&E staining and their mathematical counterparts (molecular pathology of p63) have been proposed. The suggested analytical framework envisaged standardization of the immunohistochemistry evaluation procedure for the molecular marker, using computer-aided image analysis, toward enhancing its prognostic value. SUBJECTS AND METHODS: In histopathologically confirmed OSF, OSCC and NOM tissue sections, p63(+) nuclei were localized and segmented by identifying regional maxima in plateau-like intensity spatial profiles of nuclei. The clustered nuclei were localized and segmented by identifying concave points in the morphometry and by marker-controlled watersheds. Voronoi tessellations were constructed around nuclei centroids and mean values of spatial-relation metrics such as tessellation area, tessellation perimeter, roundness factor and disorder of the area were extracted. Morphology and extent of expression are characterized by area, diameter, perimeter, compactness, eccentricity and density, fraction of p63(+) expression and expression distance of p63(+) nuclei. RESULTS: Correlative framework between histopathological features characterizing malignant potentiality and their quantitative p63 counterparts was developed. Statistical analyses of mathematical trends were evaluated between different biologically relevant combinations: (i) NOM to oral submucous fibrosis without dysplasia (OSFWT) (ii) NOM to oral submucous fibrosis with dysplasia (OSFWD) (iii) OSFWT-OSFWD (iv) OSFWD-OSCC. Significant histopathogical correlates and their corroborative mathematical features, inferred from p63 staining, were also investigated into. CONCLUSION: Quantitative assessment and correlative analysis identified mathematical features related to hyperplasia, cellular stratification, differentiation and maturation, shape and size, nuclear crowding and nucleocytoplasmic ratio. It is envisaged that this approach for analyzing the p63 expression and its distribution pattern may help to establish it as a quantitative bio-marker to predict the malignant potentiality and progression. The proposed work would be a value addition to the gold standard by incorporating an observer-independent framework for the associated molecular pathology.

Breast fine needle aspiration cytology practices and commonly perceived diagnostic significance of cytological features: A pan- India survey.

BACKGROUND: Methodical and meticulous understanding of clinico-pathological procedures and decision making process of cancer diagnosis and identification of aspects that are well-suited for computer-aided analysis are first steps toward development of assistive computational tool for analysis of breast fine needle aspiration cytology (FNAC) slides. AIMS: To identify variables in practice of FNAC as used for diagnosis of breast lesions and commonly perceived diagnostic significance of cytological features for diagnosis of benign or malignant condition of breast lesions. MATERIALS AND METHODS: An India-wide questionnaire-based survey of cytopathologists/pathologists' breast FNAC reporting practices and their opinion on diagnostic significance of cytological features in diagnosis of benign or malignant nature of breast lesion were conducted. RESULTS: Fifty-one experts working with various medical education institutes (~52% of participants), oncological tertiary care centers (~28%) and primary care centers/private diagnostic pathology laboratories (~20%) spread over 13 states of India have participated in the survey. Constants and variables observed in clinico-cytopathological practices and combined opinion of the participants on diagnostic significance of cytological features are presented here. CONCLUSIONS: There exist analogous as well as varied components in clinico-pathological procedures and diagnostic interpretation by individuals. These constants and variables in the practice of breast FNAC should be considered, when drawing up specifications for an assistive computational tool for analysis of breast FNAC slides. The estimate for commonly perceived significance of cytological features obtained through this study will help in their selection for computer-aided analysis of breast FNAC slides and further in selection of corresponding feature quantification techniques.

Automated screening of arrhythmia using wavelet based machine learning techniques.

Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation. Here DWT is applied to extract multi-resolution coefficients followed by registration using Pan Tompkins algorithm based R point detection. Moreover, feature space is compressed using sub-band principal component analysis (PCA) and statistically validated using independent sample t-test. Thereafter, the machine learning algorithms viz., Gaussian mixture model (GMM), error back propagation neural network (EBPNN) and support vector machine (SVM) are employed for pattern classification. Results are studied and compared. It is observed that both supervised classifiers EBPNN and SVM lead to higher (93.41% and 95.60% respectively) accuracy in comparison with GMM (87.36%) for arrhythmia screening.

ApoTome to visualize E-cadherin and p63 expression in oral pre-cancer.

Precise histopathological localization of E-cadherin and p63 is of immense importance in understanding the integrity of oral mucosal stratified epithelium in normal and diseased conditions. Necessarily immunohistochemical imaging should have minimum bleaching impact on the dyes and ability to produce clear and crisp images. Here ApoTome provides an alternative with metal halide light source and structured illumination under the assistance of grids, along with integrated image processing modality to generate crisp images with digital interface. The current study demonstrates the applicability of such microscopic system in capturing fluorescence images of immunohistochemical sections of normal and precancerous biopsies in respect to the expression of p63 and E-cadherin in the epithelial cells. The ApoTome images localize the nuclear and membranous expressions of p63 and E-cadherin, respectively, with remarkable specificity. The findings on E-cadherin expression have enormous diagnostic significance as these images clearly differentiate the early and advanced stages of oral submucous fibrosis based on their cytoplasmic and membranous location. Thus, this study clearly depicts a remarkable performance of ApoTome with diagnostic significance.

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm.

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M×N×3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed "texture". We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can help the clinicians in making a faster and more objective detection of benign/malignant oral lesions.

Assessment of malignant potential of oral submucous fibrosis through evaluation of p63, E-cadherin and CD105 expression.

BACKGROUND: The assessment of malignant potential of oral submucous fibrosis grades vis-à-vis their progression towards malignancy is associated with expression of possible multiple molecular markers. AIMS: To analyse p63, E-cadherin and CD105 expression in this premalignant pathosis with a view to unravel and understand the expression of these molecules as markers. METHODS: The oral mucosal biopsies (normal, oral submucous fibrosis with and without dysplasia) were studied with routine H&E, and by immunohistochemistry for p63, E-cadherin and CD105 expression. p63 was assessed as percentage of positive nuclei. E-cadherin expression was estimated through (i) distance between basement membrane and E-cadherin expression initiation point, (ii) ratio between epithelial thickness and epithelial thickness displaying E-cadherin, and (iii) E-cadherin intensity variation along the expression path. CD105 expression was assessed qualitatively. RESULTS: The p63+ cells were highest in severely dysplastic tissues followed by other dysplastic grades, normal oral mucosa and non-dysplastic conditions. However, the p63+ cells displayed the feature of progressive maturation only in normal mucosa. There was a loss of membranous E-cadherin in basal layers of all diseased conditions; it was highest in severe dysplasia. There was significant variation (p<0.0001) in E-cadherin intensity within and between the tissues (normal and diseased). CD105 expression increased abruptly in dysplasia. CONCLUSIONS: The malignant potential of this pre-cancerous condition is likely to be correlated with an increase in p63 and CD105 expression and a concomitant loss of membranous E-cadherin. This may lead to marker identification through greater validation.