Stem Cell Transplantation for Amyotrophic Lateral Sclerosis

Zhu, Qiang; Lu, Paul

doi:10.1007/978-981-15-4370-8_6

Cited by 27 publications

(6 citation statements)

References 133 publications

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“…With these output results, we evaluated our developed method using the DS, Matthew’s correlation coefficient (MCC) ( Zhu, 2020 ), sensitivity (SEN) ( Wang et al, 2019 ), and Hausdorff distance (HSD, in pixel).

where

and

denote the true positive, false positive, true negative and false negative, respectively.…”

Section: Methodsmentioning

confidence: 99%

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation

Yang,

Zheng,

Mei

et al. 2024

Front. Physiol.

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Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).

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where

and

denote the true positive, false positive, true negative and false negative, respectively.…”

Section: Methodsmentioning

confidence: 99%

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation

Yang,

Zheng,

Mei

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…Another used metric was the Matthews correlation coefficient (MCC). MCC indicates the quality of binary classification and is generally recognized as a reliable metric, although it deteriorates seriously when the TS is imbalanced ( Zhu, 2020 ). MCC ranges between −1 and +1.…”

Section: Methodsmentioning

confidence: 99%

“…MCC ranges between −1 and +1. A value of +1 means a perfect classification, 0 indicates a random classification, and −1 is a complete misclassification ( Zhu, 2020 ).

…”

Section: Methodsmentioning

confidence: 99%

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

et al. 2022

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Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

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“…For example, newborn neurons are used to replace dopaminergic neurons in the striatum and participate in the reconstruction of the nervous system ( Lindvall, 2015 ; Bjorklund and Parmar, 2020 ). Zhu et al found that stem cells also have great potential in the treatment of amyotrophic lateral sclerosis (ALS) ( Zhu and Lu, 2020 ). Implanted neural stem cells survive well in a damaged spinal cord.…”

Section: The Role Of Neural Stem Cells In Other Neurodegenerative Disease Treatmentmentioning

confidence: 99%

“…Implanted neural stem cells survive well in a damaged spinal cord. They not only replace lost motor neurons, but also act as a neuronal relay to establish connections between regenerating axons, and between their own axons and host axons so as to rebuild the body’s innervation of voluntary muscles ( Zhu and Lu, 2020 ). The main pathological feature of Alzheimer’s disease (AD) is that amyloid β (Aβ) plaques accumulate in the degenerated neurons of the aging brain.…”

Section: The Role Of Neural Stem Cells In Other Neurodegenerative Disease Treatmentmentioning

confidence: 99%

Stem Cell-Based Therapies in Hearing Loss

Ding

et al. 2021

Front. Cell Dev. Biol.

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In recent years, neural stem cell transplantation has received widespread attention as a new treatment method for supplementing specific cells damaged by disease, such as neurodegenerative diseases. A number of studies have proved that the transplantation of neural stem cells in multiple organs has an important therapeutic effect on activation and regeneration of cells, and restore damaged neurons. This article describes the methods for inducing the differentiation of endogenous and exogenous stem cells, the implantation operation and regulation of exogenous stem cells after implanted into the inner ear, and it elaborates the relevant signal pathways of stem cells in the inner ear, as well as the clinical application of various new materials. At present, stem cell therapy still has limitations, but the role of this technology in the treatment of hearing diseases has been widely recognized. With the development of related research, stem cell therapy will play a greater role in the treatment of diseases related to the inner ear.

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Stem Cell Transplantation for Amyotrophic Lateral Sclerosis

Cited by 27 publications

References 133 publications

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

Stem Cell-Based Therapies in Hearing Loss

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