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Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.

Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.

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Importance: A comprehensive lipid panel is recommended by guidelines to evaluate atherosclerotic cardiovascular disease risk, but uptake is low.

Objective: To evaluate whether direct outreach including bulk orders with and without text messaging increases lipid screening rates.

Design, Setting, And Participants: Pragmatic randomized clinical trial conducted from June 6, 2023, to September 6, 2023, at 2 primary care practices at an academic health system among patients aged 20 to 75 years with at least 1 primary care visit in the past 3 years who were overdue for lipid screening.

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The concept of inert matrix fuel (IMF) has been proposed to utilize the energetic value of Pu and transmute minor actinides in nuclear reactors. In order to offset the initial reactivity of nuclear fuel, gadolinium (Gd) is employed as a burnable poison, owing to its high neutron absorption cross-section. To gain insights into the radiation stability and influence of grain boundaries on irradiation behaviour, 5 mol% Gd-doped ceria samples, sintered at varying temperatures, were subjected to irradiation using 400 Kr ions.

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Charge detection mass spectrometry (CD-MS) is an emerging single-particle technique where both the / and charge are measured individually to determine each ion's mass. It is particularly well-suited for analyzing high mass and heterogeneous samples. With conventional MS, the loss of charge state resolution with high mass samples has hindered the direct coupling of MS to separation techniques like size exclusion chromatography (SEC) and forced the use of lower resolution detectors.

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Background: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.

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