Extraction of eco-friendly natural dyes from Pina leaves and their application on wool fabrics

Natural dyes comprise of colorants that are obtained from animals or vegetable matters without any chemical processing. Natural dyes can substitute synthetic dye and promotes green technology initiatives in the field of textile dyeing. This study was carried out by extracting dyes from pineapple leaves (Ananas Comosus) using three stage mordanting methods using different mordants namely premordanting, post-mordanting and simultaneous dyeing-mordanting. The mordants used were aluminium potassium sulphate, white vinegar and sodium chloride (NaCl). Wool fabrics were used for dyeing. The strength of colour and K/S values of the dyed fabrics were measured before and after washing. The colourfastness to washing, rubbing and light fastness of the fabrics were conducted to investigate the performance of the dye and mordants. The results indicate that the washing, rubbing and light fastness properties of dyed samples were between good to excellent grades

Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images

Oral dysplasia is a pre-malignant stage of oral epithelial carcinomas, e.g., oral squamous cell carcinoma, where significant changes in tissue layers and cells can be observed under the microscope. However, malignancy can be reverted or cured using proper medication or surgery if the grade of malignancy is assessed properly. The assessment of correct grade is therefore critical in patient management as it can change the treatment decisions and prognosis for the dysplastic lesion. This assessment is highly challenging due to considerable inter- and intraobserver variability in pathologists’ agreement, which highlights the need for an automated grading system that can predict more accurate and reliable grade. Recent advancements have made it possible for digital pathology (DP) and artificial intelligence (AI) to join forces from the digitization of tissue slides into images and using those images to train and predict more accurate grades using complex AI models. In this regard, we propose a novel morphometric approach exploiting the architectural features in dysplastic lesions i.e., irregular epithelial stratification where we measure the widths of different layers of the epithelium from the boundary layer i.e., keratin projecting inwards to the epithelium and basal layers to the rest of the tissue section from a clinically significant viewpoint

Radiation‐induced osteosarcoma involving the mandible – report of a rare diagnosis

Background Radiation-induced osteosarcomas of the jaws are a rare but often fatal consequence of radiation therapy in the head and neck region. Here we present a case of radiation-induced osteosarcoma of the mandible. Case Presentation A male presented with severe trismus and marked left lingual alveolus expansion 1 year following extractions in the lower left quadrant. Four years previously, he had radical radiotherapy (70Gy) and chemotherapy for a p16-positive T1N2cM0 squamous cell carcinoma of the left tonsil with a positive left level II node. Initial bone biopsies of the left mandible showed a bony sequestrum suggestive of osteoradionecrosis with the presence of abnormal osteoid suspicious of osteosarcoma. Imaging demonstrated an abnormal exophytic bone-forming lesion in the left mandibular body and parasymphysis and ruled out a metastatic lesion. The patient underwent bilateral neck dissection, left mandibulectomy with fibula flap reconstruction and full-thickness skin graft. Conclusions Osteosarcoma should be considered if changes are seen in previously stable irradiated bone with bony destruction and a soft tissue mass. This case highlights the importance of synthesis of clinical, radiological and pathological findings in the diagnosis of such lesions, especially where the histology initially suggested a benign process

Prognostic importance of mitosis quantification and PHH3 expression in oral epithelial dysplasia

Oral epithelial dysplasia (OED) is diagnosed and graded using a range of histological features, making grading subjective and challenging. Mitotic counting and phosphohistone-H3 (PHH3) staining have been used for the prognostication of various malignancies; however, their importance in OED remains unexplored. This study conducts a quantitative analysis of mitotic activity in OED using both haematoxylin and eosin (H&E)-stained slides and immunohistochemical (IHC) staining for PHH3. Specifically, the diagnostic and prognostic importance of mitotic number, mitotic type and intra-epithelial location is evaluated. Whole slide images (WSI) of OED (n = 60) and non-dysplastic tissue (n = 8) were prepared for analysis. Five-year follow-up data was collected. The total number of mitosis (TNOM), mitosis type and intra-epithelial location was manually evaluated on H&E images and a digital mitotic count performed on PHH3-stained WSI. Statistical associations between these features and OED grade, malignant transformation and OED recurrence were determined. Mitosis count increased with grade severity (H&E: p < 0.005; IHC: p < 0.05), and grade-based differences were seen for mitosis type and location (p < 0.05). The ratio of normal-to-abnormal mitoses was higher in OED (1.61) than control (1.25) and reduced with grade severity. TNOM, type and location were better predictors when combined with histological grading, with the most prognostic models demonstrating an AUROC of 0.81 for transformation and 0.78 for recurrence, exceeding conventional grading. Mitosis quantification and PHH3 staining can be an adjunct to conventional H&E assessment and grading for the prediction of OED prognosis. Validation on larger multicentre cohorts is needed to establish these findings

Comparison of Soot Particle Movement based on Crank Angle

In a diesel engine, soot was produced due to incomplete fuel combustion in a combustion chamber. Some of this soot sticks to the cylinder wall and interferes with lubricant oil. This soot causes the lubricant oil to contaminate and this increases its viscosity. Contamination of lubricant oil is one of the major causes of engine wear. Therefore, the focus of this study is on soot movement in diesel engine that is the initial step to avoid contamination of lubricant oil. This work uses the data of the formation of soot particles from Kiva-3 v obtained from previous investigation and then simulated it by a Matlab routine. Kiva-3 v produced velocity vectors of the soot, fuel, temperature, pressure and others. Matlab routine uses trilinear interpolation and fourth order Runge Kutta method in order to calculate soot movement in a combustion chamber. In addition, the influence of drag force is considered in the calculation to achieve a higher accuracy. The objective of this study is to compare soot particle movement between 8° ATDC and 18° ATDC. Results show that 8° ATDC has a high risk to contaminate lubrication oil in certain location compare to 18° ATDC

Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models

Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma (OSCC). Histological grading is the current gold standard for OED prognostication but is subjective and variable with unreliable outcome prediction. We explore if individual OED histological features can be used to develop and evaluate prognostic models for malignant transformation and recurrence prediction. Digitised tissue slides for a cohort of 109 OED cases were reviewed by three expert pathologists, where the prevalence and agreement of architectural and cytological histological features was assessed and association with clinical outcomes analysed using Cox proportional hazards regression and Kaplan–Meier curves. Within the cohort, the most prevalent features were basal cell hyperplasia (72%) and irregular surface keratin (60%), and least common were verrucous surface (26%), loss of epithelial cohesion (30%), lymphocytic band and dyskeratosis (34%). Several features were significant for transformation (p < 0.036) and recurrence (p < 0.015) including bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses and nuclear pleomorphism. This led us to propose two prognostic scoring systems including a ‘6-point model’ using the six features showing a greater statistical association with transformation and recurrence (bulbous rete pegs, hyperchromatism, loss of epithelial cohesion, loss of stratification, suprabasal mitoses, nuclear pleomorphism) and a ‘two-point model’ using the two features with highest inter-pathologist agreement (loss of epithelial cohesion and bulbous rete pegs). Both the ‘six point’ and ‘two point’ models showed good predictive ability (AUROC ≥ 0.774 for transformation and 0.726 for recurrence) with further improvement when age, gender and histological grade were added. These results demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and aid patient management. Validation on larger multicentre cohorts with prospective analysis is needed to establish their usefulness in clinical practice

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review

This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slide images (WSI) of human tissue slides. Electronic databases MEDLINE via OVID, Scopus and Web of Science were searched between October 2009 – April 2020. Tailored search-strings were developed using database-specific terms. Studies were selected using a strict inclusion criterion following PRISMA Guidelines. Risk of bias assessment was conducted using a tailored QUADAS-2 tool. Out of 315 records, 11 fulfilled the inclusion criteria. AI-based methods were employed for analysis of specific histological features for oral epithelial dysplasia (n = 1), oral submucous fibrosis (n = 5), oral squamous cell carcinoma (n = 4) and oropharyngeal squamous cell carcinoma (n = 1). A combination of heuristics, supervised and unsupervised learning methods were employed, including more than 10 different classification and segmentation techniques. Most studies used uni-centric datasets (range 40–270 images) comprising small sub-images within WSI with accuracy between 79 and 100%. This review provides early evidence to support the potential application of supervised machine learning methods as a diagnostic aid for some oral potentially malignant and malignant lesions; however, there is a paucity of evidence using AI for diagnosis of other head and neck pathologies. Overall, the quality of evidence is low, with most studies showing a high risk of bias which is likely to have overestimated accuracy rates. This review highlights the need for development of state-of-the-art deep learning techniques in future head and neck research

A digital score of peri‐epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia

Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as “suspicious” and “normal” by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method’s feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis

Complete Genome Sequence of Mycobacterium fortuitum subsp. fortuitum Type Strain DSM46621

Mycobacterium fortuitum is a member of the rapidly growing nontuberculous mycobacteria (NTM). It is ubiquitous in water and soil habitats, including hospital environments. M. fortuitum is increasingly recognized as an opportunistic nosocomial pathogen causing disseminated infection. Here we report the genome sequence of M. fortuitum subsp. fortuitum type strain DSM46621