Objective:Wallerian degeneration is a pathological process closely related to peripheral nerve regeneration following injury,and includes the disintegration and phagocytosis of peripheral nervous system cells.Traditio...Objective:Wallerian degeneration is a pathological process closely related to peripheral nerve regeneration following injury,and includes the disintegration and phagocytosis of peripheral nervous system cells.Traditionally,morphological changes are observed by performing immunofluorescence staining after sectioning,which results in the loss of some histological information.The purpose of this study was to explore a new,nondestmetive,and systematic method for observing axonal histological changes during Wallerian degeneration.Methods:Thirty male Thy1-YFP-16 mice(SPF grade,6 weeks old,20±5 g)were randomly selected and divided into clear,unobstructed brain imaging cocktails and computational analysis(CUBIC)optical clearing(n=15)and traditional method groups(n=15).Five mice in each group were sacrificed at 1st,3rd,and 5th day following a crush operation.The histological axon changes were observed by CUBIC light optical clearing treatment,direct tissue section imaging,and HE staining.Results:The results revealed that,compared with traditional imaging methods,there was no physical damage to the samples,which allowed for three-dimensional and deep-seated tissue imaging through CUBIC.Local image information could be nicely obtained by direct fluorescence imaging and HE staining,but it was difficult to obtain image information of the entire sample.At the same time,the image information obtained by fluorescence imaging and HE staining was partially lost.Conclusion:The combining of CUBIC and Thy1-YFP transgenic mice allowed for a clear and comprehensive observation of histological changes of axons in Wallerian degeneration.展开更多
Self-supervised denoising has emerged as a promising approach for enhancingthe quality of medical imaging,particularly in modalities such as MagneticResonance Imaging(MRI),Computed Tomography(CT),and optical micro-sco...Self-supervised denoising has emerged as a promising approach for enhancingthe quality of medical imaging,particularly in modalities such as MagneticResonance Imaging(MRI),Computed Tomography(CT),and optical micro-scopy.Traditional supervised methods often require large datasets of pairednoisy and clean images,which are challenging to acquire in clinical practice.In contrast,self-supervised strategies exploit the inherent redundancy andstructure within the data itself,enabling effective noise reduction without theneed for explicitly labeled training pairs.This Perspective synthesizes recentadvances in self-supervised denoising techniques,outlining their underlyingprinciples,algorithmic innovations,and practical applications across differentimaging modalities.In MRI,these methods have been shown to improvecontrast and detail resolution,while in CT,they contribute to reducing radi-ation dose by allowing lower signal acquisitions without compromising imagequality.In optical microscopy,self-supervised denoising facilitates extractinghigh-fidelity cellular information from inherently low-light environments.Furthermore,these techniques have also proven effective in imaging ofbiomedical materials,such as tissue engineering scaffolds,drug delivery sys-tems,and implants,improving the evaluation of their interactions with bio-logical tissues.Collectively,the integration of these advanced denoisingframeworks holds significant promise for improving diagnostic accuracy,streamlining clinical workflows,and ultimately enhancing patient outcomes.展开更多
基金supported by grants from the National Key Research and Development Program of China(No.2016YFC1101604)the Fundamental Research Funds for the Central Universities+2 种基金Clinical Medicine Plus X-Young Scholars Project of Peking University China(No.PKU2020LCXQ020)Guangdong Basic and Applied Basic Research Foundation(No.2019A1515110983,No.2021A1515012586)Bethune Charitable Foundation and CSPC Osteoporosis Research Foundation Project(No.G-X-2020-1107-21).
文摘Objective:Wallerian degeneration is a pathological process closely related to peripheral nerve regeneration following injury,and includes the disintegration and phagocytosis of peripheral nervous system cells.Traditionally,morphological changes are observed by performing immunofluorescence staining after sectioning,which results in the loss of some histological information.The purpose of this study was to explore a new,nondestmetive,and systematic method for observing axonal histological changes during Wallerian degeneration.Methods:Thirty male Thy1-YFP-16 mice(SPF grade,6 weeks old,20±5 g)were randomly selected and divided into clear,unobstructed brain imaging cocktails and computational analysis(CUBIC)optical clearing(n=15)and traditional method groups(n=15).Five mice in each group were sacrificed at 1st,3rd,and 5th day following a crush operation.The histological axon changes were observed by CUBIC light optical clearing treatment,direct tissue section imaging,and HE staining.Results:The results revealed that,compared with traditional imaging methods,there was no physical damage to the samples,which allowed for three-dimensional and deep-seated tissue imaging through CUBIC.Local image information could be nicely obtained by direct fluorescence imaging and HE staining,but it was difficult to obtain image information of the entire sample.At the same time,the image information obtained by fluorescence imaging and HE staining was partially lost.Conclusion:The combining of CUBIC and Thy1-YFP transgenic mice allowed for a clear and comprehensive observation of histological changes of axons in Wallerian degeneration.
基金Hong Kong Research Grants Council,Grant/Award Numbers:GRF14204621,GRF14207419,GRF14207920,GRF14208523,GRF14211223。
文摘Self-supervised denoising has emerged as a promising approach for enhancingthe quality of medical imaging,particularly in modalities such as MagneticResonance Imaging(MRI),Computed Tomography(CT),and optical micro-scopy.Traditional supervised methods often require large datasets of pairednoisy and clean images,which are challenging to acquire in clinical practice.In contrast,self-supervised strategies exploit the inherent redundancy andstructure within the data itself,enabling effective noise reduction without theneed for explicitly labeled training pairs.This Perspective synthesizes recentadvances in self-supervised denoising techniques,outlining their underlyingprinciples,algorithmic innovations,and practical applications across differentimaging modalities.In MRI,these methods have been shown to improvecontrast and detail resolution,while in CT,they contribute to reducing radi-ation dose by allowing lower signal acquisitions without compromising imagequality.In optical microscopy,self-supervised denoising facilitates extractinghigh-fidelity cellular information from inherently low-light environments.Furthermore,these techniques have also proven effective in imaging ofbiomedical materials,such as tissue engineering scaffolds,drug delivery sys-tems,and implants,improving the evaluation of their interactions with bio-logical tissues.Collectively,the integration of these advanced denoisingframeworks holds significant promise for improving diagnostic accuracy,streamlining clinical workflows,and ultimately enhancing patient outcomes.
