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Background: The burden of hospital-acquired infections (HAIs) equates to 3.5 million cases, resulting in more than 90 000 deaths and 2.5 million disability-adjusted life years (DALYs) across Europe.

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Artificial Intelligence (AI) has the disruptive potential to transform patients' lives via innovations in pharmaceutical sciences, drug development, clinical trials, and manufacturing. However, it presents significant challenges, ethical concerns, and risks across sectors and societies. AI's rapid advancement has revealed regulatory gaps as existing public policies struggle to keep pace with the challenges posed by these emerging technologies.

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Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process.

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Both postural orthostatic tachycardia syndrome (POTS) and Long COVID are currently viewed as heterogeneous disorders with complex, multi-factorial and multi-systemic pathophysiology. POTS, one of the most common autonomic disorders, is a frequent sequela of SARS-CoV-2 infection. Both POTS and autonomic dysfunction, in general, are major pathophysiologic mechanisms of Long COVID.

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