Alzheimer’s disease (AD) is caused by synaptic failure and the excessive accumulation of misfolded proteins especially Aβ and tau, and associated with memory loss and cognitive impairment. Treatment of AD mainly con...Alzheimer’s disease (AD) is caused by synaptic failure and the excessive accumulation of misfolded proteins especially Aβ and tau, and associated with memory loss and cognitive impairment. Treatment of AD mainly consists of symptomatic therapy and disease-modifying therapy (DMT). Several monotherapies including small molecules or antibodies have been evaluated through multiple clinical trials, but a very few have been approved by the USFDA to intervene the disease’s pathogenesis. Past research has shown multifactorial nature of AD, therefore, multi-target drugs were proposed to target different pathways at the same time, however, currently no rationally designed multi-target directed ligand (MTDL) has been clinically approved. Different combinations and bispecific antibodies are also under development. Novel approaches like stem cell-based therapies, microRNAs, peptides, ADCs and vaccines cast a new hope for AD treatment, however, a number of questions remained to be answered prior to their safe and effective clinical translation. This review explores the small molecules, MTDL, and antibodies (monospecific and bispecific) for the treatment of AD. Finally, future perspectives (stem cell therapy, PROTAC approaches, microRNAs, ADC, peptides and vaccines) are also discussed with regard to their clinical applications and feasibility.展开更多
Okadaic acid： Okadaic acid （OKA）, a polyether （C38 fatty acid） toxin, is a potent and selective inhibitor of protein phosphatase, PP1 and protein phosphatase 2A （PP2A）. It is mainly extracted from a black spong...Okadaic acid： Okadaic acid （OKA）, a polyether （C38 fatty acid） toxin, is a potent and selective inhibitor of protein phosphatase, PP1 and protein phosphatase 2A （PP2A）. It is mainly extracted from a black sponge Hallichondria okadaii and has been suggested to play a potent probe for studying the various molecular, cellular, biochemical and mechanism of neurotoxicity. It is known as a selective and potent in- hibitor of serine/threonine phosphatases 1 and 2A induces hyperphosphorylation of tau in vitro and in vivo. It has been reported that Alzheimer＇s disease （AD） is a complex multi- factorial neurodegenerative disorder and hyperphosphor- ylated tau protein is a major pathological hallmark of AD. The reduced activity of phosphatases like, PP2A has been implicated in the brain of AD patients. OKA also induced inhibition of protein phosphatases cause neurofibrillary tangles （NFTs） like pathological changes and tau hyperphos- phorylation seen in AD pathology. Our and others reports inferred that OKA induces neurodegeneration along with tau hyperphosphorylation, GSK3β activation, oxidative stress, neuroinflammation and neurotoxicity which are char- acteristic of AD pathology （Figure 1）.展开更多
The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irre...The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs.It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image fusion.In this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images.First,the preprocessing was performed on the data.Then,the data augmentation techniques are used to handle overfitting.Also,the skull is removed to lead to good classification.In the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of each.Magnetic resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific organs.Positron emission tomography images can provide functional information and the metabolisms of particular tissues.This characteristic helps clinicians detect diseases and tumor progression at an early stage.Third,the feature extraction of fused images is extracted using a convolutional neural network.In the case of late fusion,the features are extracted first and then fused.Finally,the proposed system performs XGB to classify Alzheimer’s disease.The system’s performance was evaluated using accuracy,specificity,and sensitivity.All medical data were retrieved in the 2D format of 256×256 pixels.The classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was 2.The best number of trees for the random forest was 60;for the support vector machine,the maximum depth was 4,and the kernel gamma was 0.01.The system achieved an accuracy of 98.06%,specificity of 94.32%,and sensitivity of 97.02%in the case of early fusion.Also,if the system achieved late fusion,accuracy was 99.22%,specificity was 96.54%,and sensitivity was 99.54%.展开更多
Alzheimer＇s disease（AD）is a fatal progressive neurodegenerative disorder characterized by loss in memory,cognition,and executive function and activities of daily living.AD pathogenesis has been shown to involve los...Alzheimer＇s disease（AD）is a fatal progressive neurodegenerative disorder characterized by loss in memory,cognition,and executive function and activities of daily living.AD pathogenesis has been shown to involve loss of neurons and synapses,cholinergic deficits,amyloid-beta protein（Aβ）deposition,tau protein hyperphosphorylation, and neuroinflammation.展开更多
The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture t...The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.展开更多
Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our prop...Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.展开更多
文摘Alzheimer’s disease (AD) is caused by synaptic failure and the excessive accumulation of misfolded proteins especially Aβ and tau, and associated with memory loss and cognitive impairment. Treatment of AD mainly consists of symptomatic therapy and disease-modifying therapy (DMT). Several monotherapies including small molecules or antibodies have been evaluated through multiple clinical trials, but a very few have been approved by the USFDA to intervene the disease’s pathogenesis. Past research has shown multifactorial nature of AD, therefore, multi-target drugs were proposed to target different pathways at the same time, however, currently no rationally designed multi-target directed ligand (MTDL) has been clinically approved. Different combinations and bispecific antibodies are also under development. Novel approaches like stem cell-based therapies, microRNAs, peptides, ADCs and vaccines cast a new hope for AD treatment, however, a number of questions remained to be answered prior to their safe and effective clinical translation. This review explores the small molecules, MTDL, and antibodies (monospecific and bispecific) for the treatment of AD. Finally, future perspectives (stem cell therapy, PROTAC approaches, microRNAs, ADC, peptides and vaccines) are also discussed with regard to their clinical applications and feasibility.
基金supported in part by Council of Scientific and Industrial Research(CSIR),IndiaNational Institute of Health,USA
文摘Okadaic acid： Okadaic acid （OKA）, a polyether （C38 fatty acid） toxin, is a potent and selective inhibitor of protein phosphatase, PP1 and protein phosphatase 2A （PP2A）. It is mainly extracted from a black sponge Hallichondria okadaii and has been suggested to play a potent probe for studying the various molecular, cellular, biochemical and mechanism of neurotoxicity. It is known as a selective and potent in- hibitor of serine/threonine phosphatases 1 and 2A induces hyperphosphorylation of tau in vitro and in vivo. It has been reported that Alzheimer＇s disease （AD） is a complex multi- factorial neurodegenerative disorder and hyperphosphor- ylated tau protein is a major pathological hallmark of AD. The reduced activity of phosphatases like, PP2A has been implicated in the brain of AD patients. OKA also induced inhibition of protein phosphatases cause neurofibrillary tangles （NFTs） like pathological changes and tau hyperphos- phorylation seen in AD pathology. Our and others reports inferred that OKA induces neurodegeneration along with tau hyperphosphorylation, GSK3β activation, oxidative stress, neuroinflammation and neurotoxicity which are char- acteristic of AD pathology （Figure 1）.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICT Creative Consilience Program(IITP-2021-2020-0-01821)supervised by the IITP(Institute for Information&communications Technology Planning&evaluation)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1011198).
文摘The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs.It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image fusion.In this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images.First,the preprocessing was performed on the data.Then,the data augmentation techniques are used to handle overfitting.Also,the skull is removed to lead to good classification.In the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of each.Magnetic resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific organs.Positron emission tomography images can provide functional information and the metabolisms of particular tissues.This characteristic helps clinicians detect diseases and tumor progression at an early stage.Third,the feature extraction of fused images is extracted using a convolutional neural network.In the case of late fusion,the features are extracted first and then fused.Finally,the proposed system performs XGB to classify Alzheimer’s disease.The system’s performance was evaluated using accuracy,specificity,and sensitivity.All medical data were retrieved in the 2D format of 256×256 pixels.The classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was 2.The best number of trees for the random forest was 60;for the support vector machine,the maximum depth was 4,and the kernel gamma was 0.01.The system achieved an accuracy of 98.06%,specificity of 94.32%,and sensitivity of 97.02%in the case of early fusion.Also,if the system achieved late fusion,accuracy was 99.22%,specificity was 96.54%,and sensitivity was 99.54%.
文摘Alzheimer＇s disease（AD）is a fatal progressive neurodegenerative disorder characterized by loss in memory,cognition,and executive function and activities of daily living.AD pathogenesis has been shown to involve loss of neurons and synapses,cholinergic deficits,amyloid-beta protein（Aβ）deposition,tau protein hyperphosphorylation, and neuroinflammation.
基金supported by China Postdoctoral Science Foundation(No.2022M720434)。
文摘The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.
文摘Our purpose in this study was to develop an automated method for measuring three-dimensional (3D) cerebral cortical thicknesses in patients with Alzheimer’s disease (AD) using magnetic resonance (MR) images. Our proposed method consists of mainly three steps. First, a brain parenchymal region was segmented based on brain model matching. Second, a 3D fuzzy membership map for a cerebral cortical region was created by applying a fuzzy c-means (FCM) clustering algorithm to T1-weighted MR images. Third, cerebral cortical thickness was three- dimensionally measured on each cortical surface voxel by using a localized gradient vector trajectory in a fuzzy membership map. Spherical models with 3 mm artificial cortical regions, which were produced using three noise levels of 2%, 5%, and 10%, were employed to evaluate the proposed method. We also applied the proposed method to T1-weighted images obtained from 20 cases, i.e., 10 clinically diagnosed AD cases and 10 clinically normal (CN) subjects. The thicknesses of the 3 mm artificial cortical regions for spherical models with noise levels of 2%, 5%, and 10% were measured by the proposed method as 2.953 ± 0.342, 2.953 ± 0.342 and 2.952 ± 0.343 mm, respectively. Thus the mean thicknesses for the entire cerebral lobar region were 3.1 ± 0.4 mm for AD patients and 3.3 ± 0.4 mm for CN subjects, respectively (p < 0.05). The proposed method could be feasible for measuring the 3D cerebral cortical thickness on individual cortical surface voxels as an atrophy feature in AD.
