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Se estudio el comportamiento fisicoquímico y fisiologico del fruto de uchuva durante la poscosecha. El fruto se cosecho en los grados de madurez 4 (color verde amarrillo) y 5 (amarillo), según norma 4850 de Icontec (Instituto de Normas Tecnicas y Certificacion), y se almaceno a 12 oC y 85% humedad relativa durante 30 d. Previamente, los calices se secaron a temperaturas de 18 oC y 24 oC durante 6 h. Se determino la intensidad respiratoria por cromatografia de gases y el contenido de acidos organicos y azucares por cromatografía liquida de alto desempeno. Los resultados mostraron que la uchuva es un fruto climaterico, que presenta el maximo de respiracion a los 12 d de almacenamien to. Los tratamientos con secado del caliz a 24 oC fueron mas eficientes, ya que el fruto respiro menos, comparado con las uchuvas cuyo caliz fue secado a 18 oC. Entre los acidos organicos evaluados, el acido citrico resulto predominante en el fruto, seguido por los acidos malico, ascorbico, tartarico y oxalico, respectivamente. La perdida de acido as corbico en el fruto fue total despues de12 d de almacenamiento. Los azucares predominantes en la uchuva fueron, en su orden, sacarosa, glucosa y fructosa. Frutos cosechados en grado de madurez (GM) 4 con secado del caliz a 24 oC y aquellos en GM 5 y secados a 18 °C conservaron mejor la concentración de sacarosa. Se presento Botrytis cinerea en frutos con secado del caliz a 18 oC.

Cutaneous larva migrans (CLM) is a dermo-epidermal parasitic infection with a disproportionate incidence in developing countries, particularly in, and near tropical areas. It is characterized by erythematous, twisting, and linear plaques that can migrate to adjacent skin. Herein, we present an otherwise healthy 45-year-old woman who acquired a pruritic, erythematous, and serpiginous rash localized to her right medial ankle during a trip to New England. Oral ivermectin, the preferred first-line treatment for cutaneous larva migrans, was administered in combination with triamcinolone. This was followed by removal of the papular area via punch biopsy; treatment was successful with a one-week recovery. Although cutaneous larva migrans has traditionally been considered a tropical disease, clinicians should be cognizant of its expanding geographic spread.

Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Leishmaniasis is a widespread disease caused by species of the genus Leishmania. In Colombia, this zoonosis is endemic in rural areas with a high prevalence in the departments of Antioquia, Santander, Meta, Tolima and Nariño. Dogs are the most important domestic reservoirs of the pathogen, given the epidemiological importance of dogs in the control of leishmaniasis is needed to determine the prevalence of Leishmania spp. in canine population of the rural area of Ibagué and to identify potential risk factors related to the presence of this parasite. A cross-sectional study was carried out in 173 dogs from the rural area of Ibagué. Leishmania spp. was detected by amplifying the Internal Transcribed Spacer (ITS-1) and two regions of the hsp70 gene through PCR. Factor associations were calculated through the Chisquare and odds ratio. Prevalence of Leishmania spp. infection in dogs was of 91.33% (158/173), where 36.71% (58/158) of the Leishmania spp. positive dogs showed one or more clinical signs of canine leishmaniasis and 63.29% (100/158) of the dogs were asymptomatic. Factors associated with the presence of the parasite did not show significance. In addition, hsp70D-PCR was proved to be highly efficient for the detection of Leishmania spp.

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.

Background: Cutaneous T-cell lymphomas (CTCL) are a heterogeneous group of extranodal non-Hodgkin lymphomas for which diagnosis can be challenging given the potential for overlap with inflammatory dermatoses. Current diagnostic criteria for CTCL incorporate clinical and histopathologic findings as well as results of T-cell receptor (TCR) gene sequencing. Molecular interrogation of TCR genes, TRG and TRB, has proven to be a critical tool for confirming diagnoses of CTCL and for disease tracking after initiation of therapy or after stem cell transplant. Methods for confirming a diagnosis of lymphoma in the absence of TCR gene clonality are lacking. We present two patients with CTCL with pathogenic somatic mutations in the absence of TRG and TRB clonality., Case Presentations: Case 1: A 38-year-old male had a 19-year history of a diffuse skin rash with papulosquamous, granulomatous, and verrucous features and progressive ulcerated plaques and tumors demonstrating an atypical CD4+ T-cell infiltrate with expression of cytotoxic markers CD56, TIA-1, granzyme, and perforin on histopathology. No definitive evidence for T-cell clonality was detected by conventional PCR of 6 biopsies or by next-generation sequencing (NGS) of 14 biopsies. Somatic mutational profiling of a skin biopsy revealed pathogenic mutations in PIKC3D and TERT promoter hotspots, confirming the presence of a clonal process. Case 2: A 69-year-old male with a 13-year history of progressive, diffuse hypertrophic and eroded plaques showed an atypical CD4+ T-cell infiltrate with subset expression of TIA-1 and granzyme on histopathology. No TCR clonality was detected by TCR-NGS of 6 biopsies. Somatic mutational profiling of a skin biopsy detected a pathogenic mutation in TP53, confirming the presence of a clonal process., Conclusions: These cases highlight how detection of pathogenic somatic mutations can confirm a diagnosis of lymphoma in a clinically and histopathologically suspicious cutaneous lymphoid proliferation without detectable TCR clonality.

The association of malignant lymphomas with non-necrotic epithelioid granulomas has been reported rarely since 1977. Hodgkin's disease-associated widespread cutaneous granuloma annulare (GA) has been reported in only eight patients. We report the second case of subcutaneous GA associated with Hodgkin's disease. A 73-year-old man with Epstein-Barr virus-associated Hodgkin's lymphoma and paraneoplastic subcutaneous GA, presented 3 months after the diagnosis of malignancy. Examination revealed a large, broad erythematous, indurated, subcutaneous plaque spanning the majority of the left lower back and flank with no associated symptoms. Initial biopsy was suggestive of morphea. Prompted by positron emission tomography (PET) findings of increased fluorodeoxyglucose (FDG) uptake, a second, deeper biopsy was performed, revealing subcutaneous palisaded granulomatous dermatitis. After complete workup, the diagnosis most strongly suggested subcutaneous GA. This case highlights the importance of deep incisional biopsies, the fluorodeoxyglucose - positron emission tomography (FDG-PET) findings in GA and the rare association of GA with Hodgkin's disease which may signal the presence of malignancy., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2018. No commercial re-use. See rights and permissions. Published by BMJ.)

This corrects the article DOI: 10.1038/nature21056.

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

The utility of cutaneous biopsies in directing the management of post-hematopoietic cell transplantation (HCT) eruptions remains uncertain. We retrospectively analyzed 439 consecutive HCT procedures for malignant hematologic disorders performed at our institution between January 2005 and December 2012; 192 patients underwent 430 cutaneous biopsies. The clinical and dermatopathologic diagnosis differed in 240 cases (56%). Biopsy results led to a change in therapy in 69 (16%) episodes. Seventeen of 69 management changes occurred in response to a clinical diagnosis of graft-versus-host disease and resulted in augmentation of systemic immunosuppression. The management was modified with similar frequencies with respect to concordance or discordance between the clinical and histopathologic diagnosis (P = .51). We used classification and regression tree (CART) analysis, a decision-modeling technique, to predict the biopsy yield as expressed by impact on clinical management in the allogeneic and autologous setting. The models were cross-validated and then tested against a validation subset, and they maintained a high negative predictive value and high specificity. Although skin biopsies may not be mandatory for either diagnostic or therapeutic reasons, in carefully chosen circumstances, this procedure can yield extremely important data. We believe a prospective study should be undertaken to evaluate current practice data and to validate our decision tree models., (Copyright © 2013 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.)