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1.
Open Forum Infect Dis ; 8(2): ofaa642, 2021 Feb.
Article in English | MEDLINE | ID: mdl-33575423

ABSTRACT

Reactivation of Chagas disease has been described in immunosuppressed patients, but there is a paucity of literature describing reactivation in patients on immunosuppressive therapies for the treatment of autoimmune rheumatic diseases. We describe a case of Chagas disease reactivation in a woman taking azathioprine and prednisone for limited cutaneous systemic sclerosis (lcSSc). Reactivation manifested as indurated and erythematous cutaneous nodules. Sequencing of a skin biopsy specimen confirmed the diagnosis of Chagas disease. She was treated with benznidazole with clinical improvement in the cutaneous lesions. However, her clinical course was complicated and included disseminated CMV disease and subsequent septic shock due to bacteremia. Our case and review of the literature highlight that screening for Chagas disease should be strongly considered for patients who will undergo immunosuppression for treatment of autoimmune disease if epidemiologically indicated.

2.
J Am Pharm Assoc (2003) ; 59(2S): S86-S95.e1, 2019.
Article in English | MEDLINE | ID: mdl-30745188

ABSTRACT

OBJECTIVES: To develop and test the usability and feasibility of a customizable mobile application (app) designed to help educate patients about their oral anticancer medications (OAMs) and regimens. SETTING: Outpatient cancer center and oncology pharmacy for urban, Midwestern academic health system. PRACTICE DESCRIPTION: Clinically-supervised educational intervention to support patients learning about OAMs. PRACTICE INNOVATION: With input from patient partners, our interdisciplinary team designed the first known tablet-based educational app that can interface with a patient's electronic medical record. The app is based on learning style and adherence theories and is customizable for individually prescribed OAMs. The app can accommodate multiple learning styles through text at 6th-grade reading level, pictures, animations, and audio voiceovers. Functionalities include interactive educational modules on 11 OAMs and case-based patient stories on common barriers to OAM adherence. EVALUATION: Early phase testing provided the opportunity to observe the user interface with the app and app functionality. Data were summarized descriptively from observations and comments of patient subjects. RESULTS: Thirty patient subjects provided input-19 in phase 1 usability testing and 11 in phase 2 feasibility testing. Comments provided by patient subjects during usability testing were largely positive. Responses included self-identification with patient stories, usefulness of drug information, preferences for text messages, and app limitations (e.g., perceived generational digital divide in technology use and potential patient inability to receive text messages). Using their feedback, modifications were made to the prototype app. Responses in feasibility testing demonstrated the app's usefulness across a wide range of ages. Highest opinion ratings on app usefulness were stated by patients who were newer to OAM therapy. CONCLUSION: User feedback suggests the potential benefit of the app as a tool to help patients with cancer, particularly after the first months for those starting new OAM regimens. Processes and lessons learned are transferable to other settings.


Subject(s)
Mobile Applications/trends , Neoplasms/drug therapy , Patient Education as Topic/trends , Adult , Aged , Electronic Health Records , Feedback , Female , Humans , Male , Middle Aged , Patient-Centered Care , Self-Management , Software Design
3.
PLoS One ; 11(2): e0148879, 2016.
Article in English | MEDLINE | ID: mdl-26867017

ABSTRACT

Discriminating between black and white spruce (Picea mariana and Picea glauca) is a difficult palynological classification problem that, if solved, would provide valuable data for paleoclimate reconstructions. We developed an open-source visual recognition software (ARLO, Automated Recognition with Layered Optimization) capable of differentiating between these two species at an accuracy on par with human experts. The system applies pattern recognition and machine learning to the analysis of pollen images and discovers general-purpose image features, defined by simple features of lines and grids of pixels taken at different dimensions, size, spacing, and resolution. It adapts to a given problem by searching for the most effective combination of both feature representation and learning strategy. This results in a powerful and flexible framework for image classification. We worked with images acquired using an automated slide scanner. We first applied a hash-based "pollen spotting" model to segment pollen grains from the slide background. We next tested ARLO's ability to reconstruct black to white spruce pollen ratios using artificially constructed slides of known ratios. We then developed a more scalable hash-based method of image analysis that was able to distinguish between the pollen of black and white spruce with an estimated accuracy of 83.61%, comparable to human expert performance. Our results demonstrate the capability of machine learning systems to automate challenging taxonomic classifications in pollen analysis, and our success with simple image representations suggests that our approach is generalizable to many other object recognition problems.


Subject(s)
Image Processing, Computer-Assisted/methods , Picea/physiology , Pollen/classification , Algorithms , Automation , Color , Humans , Machine Learning , Observer Variation , Pattern Recognition, Automated , Pollen/chemistry , Reproducibility of Results , Software
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