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Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.

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Objective: To evaluate the utility of three-dimensional (3D) anatomical models as an educational tool among Orthopaedic surgical trainees.

Methods: Seven types of 3D anatomical models - humerus, elbow, ankle, calcaneum, knee, femur, and pelvis- based on patients' computational tomography (CT) scans were printed in the study institution and used by surgical trainees preoperatively. Responses were collected in the form of a Likert scale questionnaire.

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Lateralisation in reverse shoulder arthroplasty - A narrative review.

J Clin Orthop Trauma

March 2025

Department of Orthopaedics, Woodend Hospital, Aberdeen, AB15 6XS, UK.

Reverse shoulder arthroplasty (RSA) has witnessed a significant advancement with the introduction of lateralisation techniques, aiming to enhance shoulder function and implant durability. Traditional medialised designs, following Grammont's principles, have encountered challenges such as scapular notching, reduced rotational strength, and instability. In contrast, lateralisation methods, which reposition the joint center of rotation laterally on the glenoid, humerus, or both, seek to improve deltoid leverage, optimize the rotator cuff muscles' length-tension relationship, and enhance joint stability.

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MTIOT: Identifying HPV subtypes from multiple infection data.

Comput Struct Biotechnol J

December 2024

Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.

Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.

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Background: Migraine represents a chronic neurological disorder characterized by high prevalence, substantial disability rates, and significant economic burden. Its pathogenesis is complex, and there is currently no cure. The rapid progress in multi-omics technologies has provided new tools to uncover the intricate pathological mechanisms underlying migraine.

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