CONCLUSIONS: PCPRs in lay language appeared to improve patients’ knowledge of their breast cancer diagnosis, were acceptable to patients and providers, and have the potential to be broadly applied in an effort to improve patient knowledge and improve the patient experience surrounding a breast cancer diagnosis. →
CONCLUSIONS: Probiotic supplementation improved behavioural and GI symptoms in children with ASD with no adverse effects. Both symptoms were significantly correlated. However, these results need to be validated in a larger sample size. →
CONCLUSION: Regarding short-term outcomes, both subgroups of patients (shorter axSpA symptom duration (≤2 years) and longer symptom duration (>2 years)) achieved comparable results when treated with upadacitinib. →
CONCLUSIONS: For a successful implementation, involvement of nurses from day and night shifts and other stakeholders is crucial. Based on the results of this process evaluation conducted alongside an exploratory trial, the intervention can be adapted and further developed to evaluate its effectiveness in a future full trial. →
Large language models (LLMs) generate text step by step, which limits their ability to plan for tasks requiring multiple reasoning steps, such as structured writing or problem-solving. This lack of long-term planning affects their coherence and decision-making in complex scenarios. Some approaches evaluate various alternatives before making a choice, which improves prediction precision. However, they… →
CONCLUSION: This study demonstrates that AI assistance for PCa detection in PBx significantly reduces IHC costs while maintaining diagnostic safety standards, supporting the business case for AI implementation in PCa detection. →
CONCLUSIONS: A multidomain intervention is likely to be beneficial to cognition regardless of frailty status. Prefrail participants seemed particularly responsive to preventive intervention. Thus, an optimal time for a multidomain lifestyle intervention may be at the prefrailty stage. →
Modern bioinformatics research is characterized by the constant emergence of complex data sources and analytical challenges. Researchers routinely confront tasks that require the synthesis of diverse datasets, the execution of iterative analyses, and the interpretation of subtle biological signals. High-throughput sequencing, multi-dimensional imaging, and other advanced data collection techniques contribute to an environment where traditional,… →
Object-centric learning (OCL) is an area of computer vision that aims to decompose visual scenes into distinct objects, enabling advanced vision tasks such as prediction, reasoning, and decision-making. Traditional methods in visual recognition often rely on feature extraction without explicitly segmenting objects, which limits their ability to understand object relationships. In contrast, OCL models break… →
Personalizing LLMs is essential for applications such as virtual assistants and content recommendations, ensuring responses align with individual user preferences. Unlike traditional approaches that optimize models based on aggregated user feedback, personalization aims to capture the diversity of individual perspectives shaped by culture, experiences, and values. Current optimization methods, such as reinforcement learning from human… →