Algae of the genus Prototheca are microorganisms involved in the occurrence of diseases in humans and animals. In bovine species, Prototheca spp. cause environmental mastitis, productive losses in dairy herds, mainly leading to the discard of infected cows. Currently, there are no effective anti-Prototheca spp. drugs to combat this infection. Thus, the search for an efficacious therapy for Prototheca spp. infections have become essential. Highly soluble polypyrrole (Ppy) is a molecule with known antimicrobial activity. This study aimed to characterize Prototheca spp. read more isolates from bovine mastitis as well as to evaluate the susceptibility profile and to verify the morphological alterations on Prototheca spp. isolates treated with Ppy. In this research, 36 Brazilian isolates of Prototheca spp. were characterized by restriction fragment length polymorphism polymerase chain reaction (RFLP-PCR) assay for the mitochondrial cytB gene. Additionally, Ppy algicidal activity against these isolates of Prototheca spp. was assessed by minimal microbicidal concentration method in microplates. Further, scanning electron microscopy (SEM) was performed in order to verify the morphological alterations on Prototheca spp. isolates in response to Ppy. The isolates were characterized as belonging to Prototheca zopfii genotype 2 (35/36) and Prototheca blaschkeae (1/36). Ppy had an algicidal effect on all isolates tested at concentrations ranging from 15.625 μg ml-1 to 62.5 μg ml-1. SEM showed changes on planktonic and sessile P. zopfii, including a decrease of the number of cells with the presence of an amorphous substance involving the cells. The algicidal activity of Ppy suggests the therapeutic potential of this molecule in the prevention and treatment of Prototheca spp. in bovine mastitis. © The Author(s) 2020. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.OBJECTIVE The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States. MATERIALS AND METHODS We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics. RESULTS We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use. DISCUSSION Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed. CONCLUSION Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use. © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email [email protected] Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the types and gene expression subtypes of non-small cell lung cancer objectively. MATERIALS AND METHODS We processed whole-slide histopathology images of lung adenocarcinoma (n = 427) and lung squamous cell carcinoma patients (n = 457) in the Cancer Genome Atlas. We built convolutional neural networks to classify histopathology images, evaluated their performance by the areas under the receiver-operating characteristic curves (AUCs), and validated the results in an independent cohort (n = 125). RESULTS To establish neural networks for quantitative image analyses, we first built convolutional neural network models to identify tumor regions from adjacent dense benign tissues (AUCs > 0.935) and recapitulated expert pathologists' diagnosis (AUCs > 0.877), with the results validated in an independent cohort (AUCs = 0.726-0.864). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P  less then  .01). DISCUSSION Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases. © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email [email protected] To study the newly adopted International Classification of Diseases 11th revision (ICD-11) and compare it to the International Classification of Diseases 10th revision (ICD-10) and International Classification of Diseases 10th revision-Clinical Modification (ICD-10-CM). MATERIALS AND METHODS Data files and maps were downloaded from the World Health Organization (WHO) website and through the application programming interfaces. A round trip method based on the WHO maps was used to identify equivalent codes between ICD-10 and ICD-11, which were validated by limited manual review. ICD-11 terms were mapped to ICD-10-CM through normalized lexical mapping. ICD-10-CM codes in 6 disease areas were also manually recoded in ICD-11. RESULTS Excluding the chapters for traditional medicine, functioning assessment, and extension codes for postcoordination, ICD-11 has 14 622 leaf codes (codes that can be used in coding) compared to ICD-10 and ICD-10-CM, which has 10 607 and 71 932 leaf codes, respectively. We identified 4037 pairs of ICD-10 and ICD-11 codes that were equivalent (estimated accuracy of 96%) by our round trip method.