INTRODUCTION: Sickle cell disease (SCD) is one of the most common genetic diseases in the world, annually affecting approximately 310 000 births and causing >100 000 deaths. Vaso-occlusive crisis (VOC) is the most frequent complication of SCD, leading to bone pain, thoracic pain (acute chest syndrome) and/or abdominal spasms. It is the main cause of… →
INTRODUCTION AND AIM: Diabetes is a global health emergency with increasing prevalence and diabetes-associated morbidity and mortality. One of the challenges in optimising diabetes care is translating research advances in this heterogeneous disease into clinical care. A potential solution is the introduction of precision medicine approaches into diabetes care.We aim to develop a digital platform… →
Trailing the advances made by AI in drug discovery, one can say there is a vast amount of untapped potential. Therapeutic nanobodies, particularly, have had relatively limited breakthroughs as they require complex interdisciplinary knowledge. The COVID-19 pandemic urged the development of therapeutic nanobodies that exhibit high binding affinity and stability for the SARS-CoV-2 in a… →
Parallel computing continues to advance, addressing the demands of high-performance tasks such as deep learning, scientific simulations, and data-intensive computations. A fundamental operation within this domain is matrix multiplication, which underpins many computational workflows. Recent hardware innovations, like Tensor Core Units (TCUs), offer efficient processing by optimizing constant-size matrix multiplications. These units are now being… →
Geometry representations play a crucial role in solving complex 3D vision problems. The rapid evolution of deep learning has sparked significant interest in developing neural network-compatible geometric data representations. Recent technological advances, particularly those centered on coordinate networks, have demonstrated promising capabilities in modeling 3D geometry across diverse applications. These coordinate networks offer a functional… →
In recent years, the evolution of artificial intelligence has brought forth increasingly sophisticated large language models (LLMs). However, training these models remains a complex challenge due to their immense computational requirements. Traditionally, training such models has been possible only in centralized environments with high-bandwidth interconnects, typically within large data centers controlled by a few tech… →
Deep learning techniques are increasingly applied to neuroimaging analysis, with 3D CNNs offering superior performance for volumetric imaging. However, their reliance on large datasets is challenging due to the high cost and effort required for medical data collection and annotation. As an alternative, 2D CNNs utilize 2D projections of 3D images, which often limits volumetric… →
The rapid development of artificial intelligence (AI) has produced models with powerful capabilities, such as language understanding and vision processing. However, deploying these models on edge devices remains challenging due to limitations in computational power, memory, and energy efficiency. The need for lightweight models that can run effectively on edge devices, while still delivering competitive… →
CONCLUSION: The findings suggest a knowledge gap in the safety and effectiveness of smoking cessation medications, specifically in relation to cancer care. Addressing this knowledge gap may increase medication uptake, adherence, and quit rates. Cancer care providers are seen as instrumental in emphasizing the importance and safety of smoking cessation medications as well as patient… →
Machine learning is advancing rapidly, particularly in areas requiring extensive data processing, such as natural language understanding and generative AI. Researchers are constantly striving to design algorithms that maximize computational efficiency while improving the accuracy and performance of large-scale models. These efforts are critical for building systems capable of managing the complexities of language representation,… →