Strategies for the Development of Small Molecule Inhibitors of Ebola Viral Infection

The recent outbreak of Ebola viral disease (EVD) in West Africa reminded us that an effective anti-viral treatment still does not exist, despite the significant progress that has recently been made in understanding biology and pathology of this lethal disease. Currently, there are no approved vaccine and/or prophylactic medication for the treatment of EVD in the market. However, the serious pandemic potential of EVD mobilized research teams in the academy and the pharmaceutical industry in the effort to find an Ebola cure as fast as possible. In this chapter, we are giving the condensed review of different approaches and strategies in search of a drug against Ebola. We have been focusing on the review of the targets that could be used for in silico, in vitro, and/or in vivo drug design of compounds that interact with the targets in different phases of the Ebola virus life cycle

Induced Vitiligo due to Talimogene Laherparepvec Injection for Metastatic Melanoma Associated with Long-term Complete Response

Talimogene laherparepvec (T-VEC) (Imlygic, Amgen) is the first oncolytic virus approved for use in therapy for metastatic melanoma. T-VEC provides a treatment option for patients with limited metastatic disease. T-VEC is a genetically modified, live, attenuated herpes simplex virus type 1 designed to replicate in tumour cells and promote an enhanced anti-tumour response (1) T-VEC is administered by injection into cutaneous, subcutaneous or nodal lesions, which are visible and/or palpable and/ or visualized by ultrasonography (2). Other local management options have been used to control metastatic disease in stage IIIB, but almost all have shown only a local effect and rapid disease relapse (3, 4). With T-VEC, responses occurred in injected and uninjected lesions, including a greater than 50% decrease in size in 15% of uninjected visceral lesions. The appearance of vitiligo has been described as an adverse event after administration of immune checkpoint inhibitors (5, 6). It has been reported as a marker of activity of the drug and long-term results, inducing clinicians to use it as a predictor of drug response (7). A T-VEC phase II study has reported 85% adverse events, all of which were grade 1 or 2. The appearance of vitiligo has been described in 3 patients out of 50 (8), although no details regarding duration and appearance have been reported

Vitiliginous alopecia masquerading as frontal fibrosing alopecia

A 73‑year‑old female presented at the Dermatology Department with a white shiny band‑like patch on the temporal and forehead zones [Figure 1]. She had a 4‑year history of vulvar lichen scleroatrophicus (LSA) [Figure 2]. Polarized dermoscopy examination revealed follicular ostium preservation, yellow dots and poliosis of vellus hair [Figure 3]..

Inherited MC1R variants in patients with melanoma are associated with better survival in women

Background: Women have a better melanoma prognosis, and fairer skin/hair colour. The presence of inherited MC1R variants has been associated with a better melanoma prognosis, but its interaction with sex is unknown. Objectives: To evaluate the relationship between germline MC1R status and survival, and determine any association with sex. Methods: This was a cohort study including 1341 patients with melanoma from the Melanoma Unit of the Hospital Clinic of Barcelona, between January 1996 and April 2018. We examined known sex‐related prognosis factors as they relate to features of melanoma and evaluated the sex‐specific role of MC1R in overall and melanoma‐specific survival. Hazard ratios (HRs) were calculated using univariate and multivariate Cox logistic regression. Results: Men showed lower overall survival than women (P < 0·001) and the presence of inherited MC1R variants was not associated with better survival in our cohort. However, in women the presence of MC1R variants was associated with better overall survival in the multivariate analysis [HR 0·57, 95% confidence interval (CI) 0·38-0·85; P = 0·006] but not in men [HR 1·26, 95% CI 0·89-1·79; P = 0·185 (P‐value for interaction 0·004)]. Analysis performed for melanoma‐specific survival showed the same level of significance. Conclusions: Inherited MC1R variants are associated with improved overall survival in women with melanoma but not in men. Intrinsic sex‐dependent features can modify the role of specific genes in melanoma prognosis. We believe that survival studies of patients with melanoma should include analysis by sex and MC1R genotype

Microblotches on dermoscopy of melanocytic lesions are associated with melanoma: A cross-sectional study

Numerous dermoscopic structures for the early detection of melanoma have been described. The aim of this study was to illustrate the characteristics of dermoscopic structures that are similar to blotches, but smaller (termed microblotches), and to evaluate their association with other well-known dermoscopic structures. A cross-sectional study design, including 165 dermoscopic images of melanoma was used to define microblotches, and 241 consecutive images of naevi from the HAM10000 database, were studied to evaluate the prevalence of this criterion in both groups. Microblotches were defined as sharply demarcated structures ≤1 mm, with geographical borders visible only with dermoscopy. Microblotches were present in 38.7% of the melanomas and 6.7% of the naevi. Moreover, microblotches were associated with an odds ratio (OR) of malignancy of 5.79, and were more frequent in invasive melanoma than in the in-situ subtype (OR 2.92). Histologically, they correspond to hyperpigmented parakeratosis or epidermal consumption. In conclusion, microblotches are related to melanomas. This finding could help dermatologists to differentiate between naevi and melanomas

Spaghetti Technique Versus Wide Local Excision for Lentigo Maligna Affecting the Head and Neck Regions: Surgical Outcome and Descriptive Analysis of 79 Cases From a Single Practice Cohort

Introduction: Lentigo maligna is a subtype of melanoma in situ that typically affects the head and neck region with an increasing incidence. Margin-controlled techniques, such as spaghetti technique (ST), have gained popularity over wide local excision (WLE) with a margin of 5 mm. Objectives: To evaluate the outcomes of lentigo maligna cases in the head and neck area treated by either WLE or ST in a tertiary referral hospital. The secondary goal was to describe the demographic and clinical characteristics of our series. Methods: Cohort study of patients diagnosed with lentigo maligna on the head and neck region between January 2014 and February 2022 in a tertiary hospital. Results: In total, 79 lentigo maligna were studied, corresponding to 77 patients. Fifty-three lesions (67%) were treated with WLE and 26 (33%) with ST. The mean age of the patients was 73 years and 58% were men. Most of the tumors were located on the cheek (50%) and mean lesion diameter was 2.2 cm for the ST group and 1.2 cm for the WLE group. Mean duration follow-up was 44 months. There were two local recurrences in the WLE group (2/53; 3.7%) and none in the ST group. Conclusions: Both WLE and ST are appropriate surgical approaches for lentigo maligna. ST offers an efficient alternative to Mohs surgery for treating lentigo maligna in the head and neck area, especially when guided by reflectance confocal microscopy

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice

Machine Learning in Melanoma Diagnosis. Limitations About to be Overcome

[spa] Antecedentes: La clasificación automática de imágenes es una rama prometedora del aprendi-zaje automático (de sus siglas en inglés Machine Learning [ML]), y es una herramienta útil enel diagnóstico de cáncer de piel. Sin embargo, poco se ha estudiado acerca de las limitacionesde su uso en la práctica clínica diaria.Objetivo: Determinar las limitaciones que existen en cuanto a la selección de imágenes usadaspara el análisis por ML de las neoplasias cutáneas, en particular del melanoma.Métodos: Se dise ̃nó un estudio de cohorte retrospectivo, donde se incluyeron de forma conse-cutiva 2.849 imágenes dermatoscópicas de alta calidad de tumores cutáneos para su valoraciónpor un sistema de ML, recogidas entre los a ̃nos 2010 y 2014. Cada imagen dermatoscópica fueclasificada según las características de elegibilidad para el análisis por ML.Resultados: De las 2.849 imágenes elegidas a partir de nuestra base de datos, 968 (34%) cum-plieron los criterios de inclusión. De los 528 melanomas, 335 (63,4%) fueron excluidos. Laausencia de piel normal circundante (40,5% de todos los melanomas de nuestra base de datos)y la ausencia de pigmentación (14,2%) fueron las causas más frecuentes de exclusión para elanálisis por ML.Discusión: Solo el 36,6% de nuestros melanomas se consideraron aceptables para el análisispor sistemas de ML de última generación. Concluimos que los futuros sistemas de ML deberánser entrenados a partir de bases de datos más grandes que incluyan imágenes representativasde la práctica clínica habitual. Afortunadamente, muchas de estas limitaciones están siendosuperadas gracias a los avances realizados recientemente por la comunidad científica, como seha demostrado en trabajos recientes. [eng] Background: Automated image classification is a promising branch of machine learning (ML)useful for skin cancer diagnosis, but little has been determined about its limitations for generalusability in current clinical practice.Objective: To determine limitations in the selection of skin cancer images for ML analysis,particularly in melanoma.Methods: Retrospective cohort study design, including 2,849 consecutive high-quality dermos-copy images of skin tumors from 2010 to 2014, for evaluation by a ML system. Each dermoscopyimage was assorted according to its eligibility for ML analysis.Results: Of the 2,849 images chosen from our database, 968 (34%) met the inclusion criteriafor analysis by the ML system. Only 64.7% of nevi and 36.6% of melanoma met the inclusioncriteria. Of the 528 melanomas, 335 (63.4%) were excluded. An absence of normal surroundingskin (40.5% of all melanomas from our database) and absence of pigmentation (14.2%) were themost common reasons for exclusion from ML analysis.Discussion: Only 36.6% of our melanomas were admissible for analysis by state-of-the-art MLsystems. We conclude that future ML systems should be trained on larger datasets which includerelevant non-ideal images from lesions evaluated in real clinical practice. Fortunately, many ofthese limitations are being overcome by the scientific community as recent works show

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI‐assisted smartphone apps and web‐based services for skin disease

Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer. Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI‐assisted smartphone applications (apps) and web‐based services for skin diseases with emphasis on skin cancer detection.MethodsAn initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance. Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non‐medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web‐based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users. Conclusions: The utilisation of AI‐assisted smartphone apps and web‐based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice