Background: Delivering evidence-based interventions remains challenging, particularly for complex conditions like chronic musculoskeletal pain. Non-pharmacologic treatments are recommended for many pain conditions, but implementing these can be difficult due to their complexity and resource demands. Pragmatic trials, especially embedded designs, provide a method to see how interventions are being implemented and adapted in real-world settings throughout the trial process.
View Article and Find Full Text PDFObjective: The objective of this study is to generate an algorithm for making predictions about individual treatment responses to a lifestyle intervention for weight loss to maximize treatment effectiveness and public health impact.
Methods: Using data from Action for Health in Diabetes (Look AHEAD), a national, multisite clinical trial that ran from 2001 to 2012, and machine-learning techniques, we generated predicted individual treatment effects for each participant. We tested for heterogeneity in treatment response and computed the degree to which treatment effects could be improved by targeting individuals most likely to benefit.
Introduction: Post-stroke cognitive impairment is associated with impaired quality of life. Remote testing provides a potential avenue to measure cognitive outcomes efficiently.
Patients And Methods: Prospective cognitive outcomes were collected at 90-180 days using both telephone MoCA (T-MoCA; range 0-22; <17 impairment) and Creyos, a computerized cognitive battery.
Radiotherapy is a leading method for cancer treatment, effectively eliminating cancer cells but often causing collateral damage to surrounding healthy tissue. Radiosensitizers aim to enhance the therapeutic effects of radiotherapy while minimizing harm to normal cells. We recently reported atomically-precise gold nanoclusters, Au(Lys-Cys-Lys), synthesized via a photochemical method coupled with a novel accelerated size-focusing procedure.
View Article and Find Full Text PDFDual-phase xenon time projection chamber (TPC) detectors offer heightened sensitivities for dark matter detection across a spectrum of particle masses. To broaden their capability to low-mass dark matter interactions, we investigated the light and charge responses of liquid xenon (LXe) to sub-keV nuclear recoils. Using neutron events from a pulsed Adelphi Deuterium-Deuterium neutron generator, an in situ calibration was conducted on the LUX detector.
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