Publications by authors named "J M Dana"
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
Bosniak classification version 2019 (v2019) was a major revision to version 2005 (v2005) that defined cystic renal mass subclasses based on wall or septa features. To determine the proportion of malignancy within cystic renal masses stratified by Bosniak classification v2019 class and feature-based subclass. MEDLINE and EMBASE databases were searched on July 24, 2023 for studies published in 2019 or later that reported cystic renal masses that underwent renal-mass CT or MRI, were assessed using Bosniak v2019, and had a reference standard (histopathology indicating benignity or malignancy or ≥5-year imaging follow-up indicating benignity).
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- The study explores stable complexes formed between colloidal CdTe quantum dots and two different cobalt porphyrin derivatives, highlighting their potential in photocatalytic applications.
- Researchers found that the binding of the porphyrins is stronger to the quantum dots than originally thought, with significant differences in electron transfer rates due to structural variations in the porphyrins.
- The findings suggest that porphyrin alignment changes upon excitation enhance the charge-separated state's lifetime and propose that these complexes could be effective for CO reduction catalysis.
Objective: To develop, deploy, and evaluate a national, electronic health record (EHR)-based dashboard to support safe prescribing of biologic and targeted synthetic disease-modifying agents (b/tsDMARDs) in the United States Veterans Affairs Healthcare System (VA).
Data Sources And Study Setting: We extracted and displayed hepatitis B (HBV), hepatitis C (HCV), and tuberculosis (TB) screening data from the EHR for users of b/tsDMARDs using PowerBI (Microsoft) and deployed the dashboard to VA facilities across the United States in 2022; we observed facilities for 44 weeks post-deployment.
Study Design: We examined the association between dashboard engagement by healthcare personnel and the percentage of patients with all screenings complete (HBV, HCV, and TB) at the facility level using an interrupted time series.