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Evaluation of Gastrointestinal Stromal Tumors (GIST) during initial clinical staging, surgical intervention, and postoperative management can be challenging. Current imaging modalities ( e.g. , PET and CT scans) lack sensitivity and specificity. Therefore, advanced clinical imaging modalities that can provide clinically relevant images with high resolution would improve diagnosis. KIT is a tyrosine kinase receptor overexpressed on GIST. Here, the application of a specific DNA aptamer targeting KIT, decorated onto a fluorescently labeled porous silicon nanoparticle (pSiNP), is used for the in vitro & in vivo imaging of GIST. This nanoparticle platform provides high-fidelity GIST imaging with minimal cellular toxicity. An in vitro analysis shows greater than 15-fold specific KIT protein targeting compared to the free KIT aptamer, while in vivo analyses of GIST-burdened mice that had been injected intravenously (IV) with aptamer-conjugated pSiNPs show extensive nanoparticle-to-tumor signal co-localization (>90% co-localization) compared to control particles. This provides an effective platform for which aptamer-conjugated pSiNP constructs can be used for the imaging of KIT-expressing cancers or for the targeted delivery of therapeutics. 

Abstract Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables. 

We discuss ongoing work towards a meta-language, execution model, and compiler tool chain that promotes determinism and grants first-class citizenship to the timing aspects of computation.