scholarly journals Scarring Alopecias: Pathology and an Update on Digital Developments

Biomedicines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1755
Author(s):  
Donna M. Cummins ◽  
Iskander H. Chaudhry ◽  
Matthew Harries
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Yield ◽  
Digital Pathology ◽  
Hair Follicles ◽  
Improve Working ◽  
Histological Features ◽  
Skin Biopsies ◽  
Working Practices ◽  
Communication Pathway ◽  
Scalp Skin

Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing these conditions. We provide insight from clinical and pathology practice into how diagnoses are derived and the key histological features observed across the most common PCAs seen in practice. Additionally, we highlight the opportunities that have emerged with advances in digital pathology and how these technologies may be used to develop clinicopathological relationships, improve working practices, enhance remote learning, reduce inefficiencies, optimise diagnostic yield, and harness the potential of artificial intelligence (AI).

The basis of quality of sheep pelts and fur products is morphological features of the skin of sheep

2020 ◽  
pp. 50-57
Author(s):  
I. Dmitrik ◽  
G. Zavgorodnyaya
Keyword(s):  
Mechanical Strength ◽  
Breaking Load ◽  
Hair Follicles ◽  
Morphological Features ◽  
High Density ◽  
Fat Cells ◽  
High Quality ◽  
Histological Features ◽  
Weakened Zone

The morphological and histological features of the skin and wool cover of sheep as the basis for the quality of fur sheep pelts have been studied. The most important properties of sheep pelts (uniformity, thinness and density of wool) are provide the possibility of producing high-quality fur semi-finished products from them. However, the features of the histostructure of fine-wool sheep determine the low mechanical strength of the “facial” layer of skin. As a result, the “front” layer during processing often cracks to the upper border of the reticular layer or even peels off from the latter, making the sheep pelt unsuitable for use on fur products. These defects in fur practice are called “cracking” and “peeling” of the facial layer. They are mainly peculiar to sheep pelts of fine-wooled sheep. In these animals due to the high density and tone of the coat, the roots and hair follicles, root vaginas, secretory departments, excretory ducts of the glands and other structures occupy a significant share of the volume in the thickness of the Pilar layer (up to 25–30 %). The share of fibrous structures remains less volume, and these structures themselves are relatively weakly developed, located loosely and loosely intertwined with each other. The accumulations of fat cells that occur here also cannot be attributed to skin-strengthening elements. In fine-fleece sheep the pilar layer is on average 60 % of the thickness of the dermis. Therefore, more than half of its thickness is a weakened zone. The strength of the “front” layer is not the same in different fine-wool breeds of sheep and in different animals within the breed. For example, the average breaking load for cod of the “front” layer in Soviet Merino pelts is 1,25 kg, and in Precoce is 2,49 kg.

scholarly journals The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

Healthcare ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 30
Author(s):  
Daniele Giansanti
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Mobile Health ◽  
Digital Pathology ◽  
Digital Radiology ◽  
Cellular Diagnostics ◽  
Information And Communication ◽  
Electronic Health ◽  
New Applications

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...]

An Integrated Digital Pathology Platform for Tumors Using Artificial Intelligence Analysis

2021 ◽  
Author(s):  
Changjiang Zhou ◽  
Xiaobing Feng ◽  
Hongbin Cai ◽  
Yi Jin ◽  
Harvest F. Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Intelligence Analysis

The Journey to Net Zero: Driving Down Emissions from Bp's North Sea Operations

2021 ◽  
Author(s):  
Sam Peter Coupland
Keyword(s):  
Energy Efficiency ◽  
North Sea ◽  
Emissions Reduction ◽  
Flare Activity ◽  
Improve Working ◽  
The North ◽  
Net Zero ◽  
Operational Improvement ◽  
The North Sea ◽  
Working Practices

Abstract bp's strategy sets out a decadeof delivery towards becoming a net zero company by 2050 (or sooner) with targets set for emissions from operations to fall by between 30-35% by 2030. In pursuit of this, a North Sea carbonplan has been developed to identify, track, and deliver sustainable emission reductions (SERs) activities. Proactive engagement has been essential in delivery of this plan, helping to empower colleagues to prioritize emissions reduction opportunities. To date, the plan has identified more than 80 SERs across bp's North Seaportfolio and cumulatively reduced carbonemissions by more than 400,000 tonnes from offshore operations. It is on track to reduce almost 70,000tonnes of carbon from operations in 2021 alone. Whilst it is recognised that this represents only part of bp's annual scope 1 emissions in the North Sea; this is a lasting operational improvement. The plan has also significantly reduced sources of unknown flare gas. It also contributed to a 45% reduction in flare activity in 2020 vs 2019as well as achieving zero routine flaringon two of bp's major west of Shetland installations from October 2020 The plan has more deeply embedded emissions tracking in operations on and offshore and helped further improve working practices on flaring and energy efficiency in general.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

2021 ◽  
pp. jclinpath-2020-207351
Author(s):  
Jenny Fitzgerald ◽  
Debra Higgins ◽  
Claudia Mazo Vargas ◽  
William Watson ◽  
Catherine Mooney ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Patient Outcome ◽  
Image Analysis ◽  
High Throughput Screening ◽  
Personalised Medicine ◽  
Digital Pathology ◽  
Disease Stage ◽  
Breast And Prostate Cancer ◽  
Biomarker Evaluation

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

scholarly journals Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer

2020 ◽  
pp. 367-382 ◽  
Author(s):  
Stephanie A. Harmon ◽  
Thomas H. Sanford ◽  
G. Thomas Brown ◽  
Chris Yang ◽  
Sherif Mehralivand ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Digital Pathology ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Clinicopathologic Features ◽  
Combined Model ◽  
Primary Tumors ◽  
Logistic Regression Models ◽  
Spatially Resolved ◽  
Digital Evaluation

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables ( P = 1.08 × 10−9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin–stained slides.

Rocky road to digital diagnostics: implementation issues and exhilarating experiences

2020 ◽  
pp. jclinpath-2020-206715
Author(s):  
Nikolaos Stathonikos ◽  
Tri Q Nguyen ◽  
Paul J van Diest
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Student Teaching ◽  
Digital Pathology ◽  
Digital Archive ◽  
Teaching And Research ◽  
Multidisciplinary Meetings ◽  
Slide Scanner

Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in a full digital diagnostic workflow where we are currently integrating artificial intelligence algorithms. In this paper, we highlight the different steps in this process towards digital diagnostics, which was at times a rocky road with definitely issues in implementation, but eventually an exciting new way to practice pathology in a more modern and efficient way where patient safety has clearly gone up.

Histopathology of Cutaneous Inflammatory Disorders in Children

2018 ◽  
Vol 21 (2) ◽  
pp. 115-149 ◽  
Author(s):  
Andy C Hsi ◽  
Ilana S Rosman
Keyword(s):  
Hair Follicles ◽  
Careful Examination ◽  
Stevens Johnson Syndrome ◽  
Hypersensitivity Reactions ◽  
Large Array ◽  
Dermoepidermal Junction ◽  
Inflammatory Disorders ◽  
Inflammatory Dermatoses ◽  
Johnson Syndrome ◽  
Skin Biopsies

Inflammatory dermatoses encompass a variety of histologic patterns that affect different portions of the skin. In spongiotic, psoriasiform, lichenoid, pityriasiform, and blistering disorders, there are predominately epidermal and junctional activities with variable superficial dermal inflammation. Hypersensitivity reactions can show either epidermal or mostly dermal changes depending on whether the exposure of the exogenous allergen occurs through an external or internal route, respectively. Exceptions include erythema multiforme and Stevens-Johnson syndrome/toxic epidermal necrolysis, where the etiology is often due to infection or ingested medications, but the histologic features are almost exclusively confined to the epidermis and dermoepidermal junction. Autoimmune disorders are unique in that lesions typically incorporate a mixture of epidermal and dermal inflammatory patterns with periadnexal inflammation, while the vast majority of vasculitis/vasculopathy and alopecia have changes limited to only the vessels and hair follicles, respectively. It is critical to recognize that a relatively limited number of histologic patterns are seen in a large array of clinical entities. Therefore, clinicopathologic correlation and careful examination of histologic details are of the utmost importance when evaluating skin biopsies for inflammatory disorders.

scholarly journals Diagnostic utility of probe-based confocal laser endomicroscopy in superficial non-ampullary duodenal epithelial tumors

2019 ◽  
Vol 07 (11) ◽  
pp. E1515-E1521
Author(s):  
Tomomitsu Tahara ◽  
Noriyuki Horiguchi ◽  
Tsuyoshi Terada ◽  
Hyuga Yamada ◽  
Dai Yoshida ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Duodenal Mucosa ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Observational Trial ◽  
Histological Features ◽  
Epithelial Tumors ◽  
Neoplastic Lesions ◽  
Columnar Cells

Abstract Background and study aims Endoscopic diagnosis of superficial non-ampullary duodenal epithelial tumors (SNADETs) has not been established. Probe-based confocal laser endomicroscopy (pCLE: Cellvizio) provides real-time endomicroscopic analysis. We developed and validated a new pCLE classification of SNADET based on abnormal findings. Patients and methods pCLE scanning of 20 SNADET lesions including 16 adenomas and four carcinomas was retrospectively evaluated to explore abnormal pCLE findings in relation to histological features. Diagnostic yield of pCLE findings was prospectively evaluated in an additional 20 SNADET lesions including 16 adenomas and four carcinomas. Results In a retrospective study, we identified four abnormal pCLE findings of SNADETs: (1) dark epithelium, (2) columnar cells irregularly extending to the lumen, (3) distorted crypt structure, and (4) fluorescein leakage. Dark epithelium distinguished neoplastic lesions (adenomas and carcinomas) from non-neoplastic duodenal mucosa with a sensitivity of 90 % and a specificity of 100 %. Distorted crypt structure distinguished carcinomas from adenomas and non-neoplastic duodenal mucosa with a sensitivity of 100% and a specificity of 94 %. In the prospective study, the sensitivity and the specificity of the dark epithelium for the diagnosis of neoplastic lesions (adenomas + carcinomas) was 75% and 100 %. Sensitivity and the specificity of the distorted crypt structure for discrimination of carcinoma from adenoma were 100 % and 94 %, respectively. Conclusions The pCLE findings correlated with the histopathology of the SNADETs. Dark epithelium and distorted crypt structure were informative pCLE findings to predict presence of neoplasia and cancer in the SNADET, respectively.UMIN-CTR UMIN000013857 TRIAL REGISTRATION: Single-Center, prospective observational trial UMIN000013857 at upload.umin.ac.jp

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