Traumatic brain injuries are serious clinical incidents associated with some of the poorest outcomes in neurological practice.Coupled with the limited regenerative capacity of the brain,this has significant implicatio...Traumatic brain injuries are serious clinical incidents associated with some of the poorest outcomes in neurological practice.Coupled with the limited regenerative capacity of the brain,this has significant implications for patients,carers,and healthcare systems,and the requirement for life-long care in some cases.Clinical treatment currently focuses on limiting the initial neural damage with longterm care/support from multidisciplinary teams.Therapies targeting neuroprotection and neural regeneration are not currently available but are the focus of intensive research.Biomaterial-based interventions are gaining popularity for a range of applications including biomolecule and drug delive ry,and to function as cellular scaffolds.Experimental investigations into the development of such novel therapeutics for traumatic brain injury will be critically underpinned by the availability of appropriate high thro ughput,facile,ethically viable,and pathomimetic biological model systems.This represents a significant challenge for researchers given the pathological complexity of traumatic brain injury.Specifically,there is a concerted post-injury response mounted by multiple neural cell types which includes microglial activation and astroglial scarring with the expression of a range of growth inhibito ry molecules and cytokines in the lesion environment.Here,we review common models used for the study of traumatic brain injury(ranging from live animal models to in vitro systems),focusing on penetrating traumatic brain injury models.We discuss their relative advantages and drawbacks for the developmental testing of biomaterial-based therapies.展开更多
The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently...The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.展开更多
基金funded by awards from the EPSRC Doctoral Training Centre in Regenerative Medicine and an NHS bursary。
文摘Traumatic brain injuries are serious clinical incidents associated with some of the poorest outcomes in neurological practice.Coupled with the limited regenerative capacity of the brain,this has significant implications for patients,carers,and healthcare systems,and the requirement for life-long care in some cases.Clinical treatment currently focuses on limiting the initial neural damage with longterm care/support from multidisciplinary teams.Therapies targeting neuroprotection and neural regeneration are not currently available but are the focus of intensive research.Biomaterial-based interventions are gaining popularity for a range of applications including biomolecule and drug delive ry,and to function as cellular scaffolds.Experimental investigations into the development of such novel therapeutics for traumatic brain injury will be critically underpinned by the availability of appropriate high thro ughput,facile,ethically viable,and pathomimetic biological model systems.This represents a significant challenge for researchers given the pathological complexity of traumatic brain injury.Specifically,there is a concerted post-injury response mounted by multiple neural cell types which includes microglial activation and astroglial scarring with the expression of a range of growth inhibito ry molecules and cytokines in the lesion environment.Here,we review common models used for the study of traumatic brain injury(ranging from live animal models to in vitro systems),focusing on penetrating traumatic brain injury models.We discuss their relative advantages and drawbacks for the developmental testing of biomaterial-based therapies.
文摘The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.
