Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral pos...Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.展开更多
One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoug...One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoughts,memories,and emotions via functional magnetic resonance imaging(i.e.,fMRI)since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions.However,the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.Given the increasingly important role of machine learning in neuroscience,a great many machine learning algorithms are presented to analyze brain activities from the fMRI data.In this paper,we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects,i.e.,brain image functional alignment,brain activity pattern analysis,and visual stimuli reconstruction.In addition,online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.展开更多
Neuropathologies include the deterioration and damage of the nervous system,especially neurons present in the brain,spinal cord and peripheral nervous system.Damage or alternations in neurons makes their structure and...Neuropathologies include the deterioration and damage of the nervous system,especially neurons present in the brain,spinal cord and peripheral nervous system.Damage or alternations in neurons makes their structure and functionality abnormal.Every year over 90,000 people get affected by neurodegenerative diseases in the USA.Among all the neurological pathologies,展开更多
文摘Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.
基金This work was supported by National Natural Science Foundation of China(Nos.61876082,61861130366,6173-2006 and 61902183)National Key Research and Development Program of China(Nos.2018 YFC2001600,2018YFC 2001602)+1 种基金the Royal Society-Academy of Medical Sciences Newton Advanced Fellowship(No.NAF\R1\180371)China Postdoctoral Science Foundation funded project(No.2019M661831).
文摘One of the most significant challenges in the neuroscience community is to understand how the human brain works.Recent progress in neuroimaging techniques have validated that it is possible to decode a person′s thoughts,memories,and emotions via functional magnetic resonance imaging(i.e.,fMRI)since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions.However,the unprecedented scale and complexity of the fMRI data have presented critical computational bottlenecks requiring new scientific analytic tools.Given the increasingly important role of machine learning in neuroscience,a great many machine learning algorithms are presented to analyze brain activities from the fMRI data.In this paper,we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects,i.e.,brain image functional alignment,brain activity pattern analysis,and visual stimuli reconstruction.In addition,online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.
文摘Neuropathologies include the deterioration and damage of the nervous system,especially neurons present in the brain,spinal cord and peripheral nervous system.Damage or alternations in neurons makes their structure and functionality abnormal.Every year over 90,000 people get affected by neurodegenerative diseases in the USA.Among all the neurological pathologies,
