Importance of Both Clinical and Dermoscopic Findings in Predicting High-Risk Histopathological Subtype in Facial Basal Cell Carcinomas

Introduction: Being able to recognize high-risk facial basal cell carcinoma (BCC) may lead to fewer incomplete excisions and inappropriate treatments. Objectives: We sought to investigate clinical and dermoscopic criteria for predicting facial BCC subtypes, analyze the interobserver agreement between readers, and develop a diagnostic algorithm to predict high-risk histopathological subtype. Methods: In this single-center, retrospective investigation, 6 independent readers evaluated predefined clinical and dermoscopic criteria in images of histopathologically verified primary facial BCCs including: topography, border demarcation, vessels, ulceration, white porcelain areas, shiny white blotches and strands, and pigmented structures and vessels within ulceration. Results: Overall, 297 clinical and dermoscopic image pairs were analyzed. The strongest associations with high-risk subtype were: “bumpy” topography (OR 3.8, 95% CI, 3.1-4.7), ill-defined borders (OR 3.4, 95% CI 3.1-4.7), white porcelain area (OR 3.5, 95% CI 2.8-4.5), and vessels within ulceration (OR 3.1, 95% CI 2.4-4.1). Predominantly focused vessels were a positive diagnostic criterium for either nodular (OR 1.7, 95% CI 1.3-2.2) or high-risk (OR 2.0, 95% CI 1.6-2.5) subtypes and a strong negative diagnostic criterium for superficial BCC (OR 14.0, 95% CI 9.6-20.8). Interobserver agreement ranged from fair to substantial (κ=0.36 to 0.72). A diagnostic algorithm based on these findings demonstrated a sensitivity of 81.4% (95% CI, 78.9-83.7%) and a specificity of 53.3% (95% CI, 49.7-56.9%) for predicting high-risk BCC subtype. Conclusions: Integration of both clinical and dermoscopic features (including novel features such as topography and vessels within ulceration) are essential to improve subtype prediction of facial BCCs and management decisions

A New, Efficient Stellar Evolution Code for Calculating Complete Evolutionary Tracks

We present a new stellar evolution code and a set of results, demonstrating its capability at calculating full evolutionary tracks for a wide range of masses and metallicities. The code is fast and efficient, and is capable of following through all evolutionary phases, without interruption or human intervention. It is meant to be used also in the context of modeling the evolution of dense stellar systems, for performing live calculations for both normal star models and merger-products. The code is based on a fully implicit, adaptive-grid numerical scheme that solves simultaneously for structure, mesh and chemical composition. Full details are given for the treatment of convection, equation of state, opacity, nuclear reactions and mass loss. Results of evolutionary calculations are shown for a solar model that matches the characteristics of the present sun to an accuracy of better than 1%; a 1 Msun model for a wide range of metallicities; a series of models of stellar populations I and II, for the mass range 0.25 to 64 Msun, followed from pre-main-sequence to a cool white dwarf or core collapse. An initial final-mass relationship is derived and compared with previous studies. Finally, we briefly address the evolution of non-canonical configurations, merger-products of low-mass main-sequence parents.Comment: MNRAS, in press; several sections and figures revise

Generation of Duchenne muscular dystrophy patient-specific induced pluripotent stem cell line lacking exons 45–50 of the dystrophin gene (IITi001-A)

Duchenne muscular dystrophy (DMD) is an X-linked progressive muscle degenerative disease caused by mutations in the dystrophin gene. We generated induced pluripotent stem cells (iPSCs) from a 13-year-old male patient carrying a deletion mutation of exons 45–50; iPSCs were subsequently differentiated into cardiomyocytes. iPSCs exhibit expression of the pluripotent markers (SOX2, NANOG, OCT4), differentiation capacity into the three germ layers, normal karyotype, genetic identity to the skin biopsy dermal fibroblasts and the patient-specific dystrophin mutation

TVP1022 Protects Neonatal Rat Ventricular Myocytes against Doxorubicin-Induced Functional Derangements

Our recent studies demonstrated that propargylamine derivatives such as rasagiline (Azilect, Food and Drug Administration-approved anti-Parkinson drug) and its S-isomer TVP1022 protect cardiac and neuronal cell cultures against apoptotic-inducing stimuli. Studies on structure-activity relationship revealed that their neuroprotective effect is associated with the propargylamine moiety, which protects mitochondrial viability and prevents apoptosis by activating Bcl-2 and protein kinase C-ε and by down-regulating the proapoptotic protein Bax. Based on the established cytoprotective and neuroprotective efficacies of propargylamine derivatives, as well as on our recent study showing that TVP1022 attenuates serum starvation-induced and doxorubicin-induced apoptosis in neonatal rat ventricular myocytes (NRVMs), we tested the hypothesis that TVP1022 will also provide protection against doxorubicin-induced NRVM functional derangements. The present study demonstrates that pretreatment of NRVMs with TVP1022 (1 μM, 24 h) prevented doxorubicin (0.5 μM, 24 h)-induced elevation of diastolic [Ca2+]i, the slowing of [Ca2+]i relaxation kinetics, and the decrease in the rates of myocyte contraction and relaxation. Furthermore, pretreatment with TVP1022 attenuated the doxorubicin-induced reduction in the protein expression of sarco/endoplasmic reticulum calcium (Ca2+) ATPase, Na+/Ca2+ exchanger 1, and total connexin 43. Finally, TVP1022 diminished the inhibitory effect of doxorubicin on gap junctional intercellular coupling (measured by means of Lucifer yellow transfer) and on conduction velocity, the amplitude of the activation phase, and the maximal rate of activation (dv/dtmax) measured by the Micro-Electrode-Array system. In summary, our results indicate that TVP1022 acts as a novel cardioprotective agent against anthracycline cardiotoxicity, and therefore potentially can be coadmhence, the inistered with doxorubicin in the treatment of malignancies in humans

BCN20000: Dermoscopic Lesions in the Wild

Abstract Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation