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Aim: Young people with childhood-onset motor disabilities face unique challenges in understanding and managing their condition. This study explored how they learnt about their condition.

Method: A descriptive qualitative study was conducted in 2023-2024 at a Swiss paediatric neurorehabilitation unit.

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Objectives: Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery.

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Adapting biological systems for nanoparticle synthesis opens an orthogonal Green direction in nanoscience by reducing the reliance on harsh chemicals and energy-intensive procedures. This study addresses the challenge of efficient catalyst preparation for organic synthesis, focusing on the rapid formation of palladium (Pd) nanoparticles using bacterial cells as a renewable and eco-friendly support. The preparation of catalytically active nanoparticles on the bacterium VKM B-3302 represents a more suitable approach to increase the reaction efficiency due to its resistance to metal salts.

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Background: Adopting appropriate noninvasive radiological method is crucial for periodic surveillance of liver metastases in colorectal cancer (CRC) patients after surgery, which is closely related to clinical management and prognosis. This study aimed to prospectively enroll stage II-III CRC patients for the surveillance of liver metastases, and compare the diagnostic performance of contrast-enhanced CT (CE-CT) and non-enhanced abbreviated MRI (NE-AMRI) during this process.

Methods: 587 CRC patients undergoing radical resection of the primary tumor were evaluated by 1 to 3 rounds of surveillance tests, consisting of abdominal CE-CT and contrast-enhanced MRI (CE-MRI) within 7 days at 6-month intervals.

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Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.

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