BACKGROUND: Participants in research trials often disclose severe depression symptoms, including thoughts of self-harm and suicidal ideation, in validated self-administered questionnaires such as the Patient Health Questionnaire (PHQ-9). However, there is no standard protocol for responding to such disclosure, and the opportunity to support people at risk is potentially missed. We developed and evaluated a… →
BACKGROUND: Social jetlag is a chronic disruption of sleep timing that is characterized by different sleep timing during workdays and free days. Social jetlag has been associated with disturbed glucose metabolism, insulin resistance, and increased risk of metabolic syndrome and type 2 diabetes. In this study, we aim to investigate whether a combination of bright… →
CONCLUSIONS: This study found high levels of malaria prevalence, vector density and transmission in the Zakpota sub-district despite the wide use of insecticide-treated nets. The vector population was mostly indoor resting and showed a high intensity of pyrethroid resistance but was generally fully susceptible to broflanilide. These findings demonstrated the suitability of the study area… →
CONCLUSIONS: Dapagliflozin caused a significant reduction in CRP compared to placebo. There were correlations between reductions in inflammatory markers including IL-1β and improvements in global longitudinal strain (but not reduced LV mass). Reductions in systemic inflammation might play a contributory role in the cardiovascular benefits of dapagliflozin. →
The aim of this study was to shed light on a crucial issue through a comprehensive evaluation of the cost-effectiveness and cost-utility of a cutting-edge web-based foot-ankle therapeutic exercise program (SOPeD) designed for treating modifiable risk factors for ulcer prevention in individuals with diabetes-related peripheral neuropathy (DPN). In this randomized controlled trial, 62 participants diagnosed… →
Large language models (LLMs) have been crucial for driving artificial intelligence and natural language processing to new heights. These models have demonstrated remarkable abilities in understanding and generating human language, with applications spanning, but not limited to, healthcare, education, and social interactions. However, LLMs need to improve in the effectiveness and control of in-context learning… →
Scientific discovery has been a cornerstone of human advancement for centuries, traditionally relying on manual processes. However, the emergence of large language models (LLMs) with advanced reasoning capabilities and the ability to interact with external tools and agents has opened up new possibilities for autonomous discovery systems. The challenge lies in developing a fully autonomous… →
Generative models of tabular data are key in Bayesian analysis, probabilistic machine learning, and fields like econometrics, healthcare, and systems biology. Researchers have developed methods to learn probabilistic models for such data automatically. To leverage these models for complex tasks, users must seamlessly integrate operations accessing data records and probabilistic models. This includes generating synthetic… →
Creating datasets for training custom AI models can be a challenging and expensive task. This process typically requires substantial time and resources, whether it’s through costly API services or manual data collection and labeling. The complexity and cost involved can make it difficult for individuals and smaller organizations to develop their own AI models. There… →
Text-to-image generation models have gained traction with advanced AI technologies, enabling the generation of detailed and contextually accurate images based on textual prompts. The rapid development in this field has led to numerous models, such as DALLE-3 and Stable Diffusion, designed to translate text into visually coherent images. A significant challenge in text-to-image generation is… →