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Background: Metastatic spine tumor surgery (MSTS) is often complex and extensive leading to significant blood loss. Allogeneic blood transfusion (ABT) is the mainstay of blood replenishment but with immune-mediated postoperative complications. Alternative blood management techniques (salvaged blood transfusion [SBT]) allow us to overcome such complications.

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Background: In Germany, digital transformation and legal regulations are leading to the need to integrate digital technologies into the nursing profession. In addition, to nursing practice, they are also being incorporated into nursing training. Despite comprehensive regulations regarding the use of digital teaching and learning media in nursing education, their specific applicability and implementation vary.

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Background: The centralization of decision-making power in the public health care system has a negative impact on the practice of professionals and the quality of home care services (HCS) for seniors. To improve HCS, decentralized management could be a particularly promising approach. To be effective, strategies designed to incorporate this management approach require attention to 3 elements: autonomy of local stakeholders, individual and organizational capacities, and accountability for actions and decisions.

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Computational Methods for Predicting Chemical Reactivity of Covalent Compounds.

J Chem Inf Model

January 2025

Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities.

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Objective: To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.

Materials And Methods: A theoretical framework of EHR systems' implementation was developed using interdisciplinary literature from healthcare, information systems, and management science focusing on the systems approach, clinical decision-making, and interface terminologies.

Observations: Healthcare professionals balance personal norms of narrative and data-driven communication in knowledge creation for EHRs by integrating detailed patient stories with structured data.

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