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http://dx.doi.org/10.1016/j.xcrm.2025.102083 | DOI Listing |
ACS Biomater Sci Eng
May 2024
Integrated Prosthetics and Reconstruction, Department of Head and Neck Surgery, Chris O'Brien Lifehouse, Camperdown, NSW 2050, Australia.
The present work describes a preclinical trial (, and ) protocol to assess the biomechanical performance and osteogenic capability of 3D-printed polymeric scaffolds implants used to repair partial defects in a sheep mandible. The protocol spans multiple steps of the medical device development pipeline, including initial concept design of the scaffold implant, digital twin finite element modeling, manufacturing of the device prototype, device implantation, and laboratory mechanical testing. First, a patient-specific one-body scaffold implant used for reconstructing a critical-sized defect along the lower border of the sheep mandible ramus was designed using on computed-tomographic (CT) imagery and computer-aided design software.
View Article and Find Full Text PDFEvid Based Complement Alternat Med
January 2024
Department of Medical Biochemistry, College of Health Sciences, Mekelle University, Mekele, Ethiopia.
Objective: This study aims to investigate the bacterial biofilm-inhibitory effect of mushroom extracts.
Methods: Mushrooms were collected from Arabuko-Sokoke and Kakamega forests and identified using morphological and molecular approaches. , , , , and were extracted by chloroform, 70% ethanol, and hot water.
Stud Health Technol Inform
January 2024
Centre for Health Informatics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, England, UK.
Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.
View Article and Find Full Text PDFStat Methods Med Res
August 2023
Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
In clinical prediction modelling, missing data can occur at any stage of the model pipeline; development, validation or deployment. Multiple imputation is often recommended yet challenging to apply at deployment; for example, the outcome cannot be in the imputation model, as recommended under multiple imputation. Regression imputation uses a fitted model to impute the predicted value of missing predictors from observed data, and could offer a pragmatic alternative at deployment.
View Article and Find Full Text PDF