Moxibustion for cognitive impairment: A systematic review and meta-analysis of animal studies

Aum, Sungmin; Choe, Seon; Cai, Mudan; Jerng, Ui Min; Lee, Jun-Hwan

doi:10.1016/j.imr.2020.100680

Cited by 12 publications

(10 citation statements)

References 52 publications

(38 reference statements)

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“…According to previous studies on the treatment of cognitive disorders with moxibustion (Aum et al, 2021 ), we selected six acupoints including Guanyuan, Baihui, bilateral Xuanzhong, and bilateral Zusanli in the MCI group. All MCI patients were treated with moxibustion by the same acupuncturist.…”

Section: Methodsmentioning

confidence: 99%

“…Moxibustion is a treatment that uses the heat generated by burning moxa sticks to stimulate acupoints, producing a central nervous system regulation similar to acupuncture (Bao et al, 2016 ). It is a commonly accepted treatment approach for cognitive impairment in East Asia (Aum et al, 2021 ), with the advantages of painlessness, ease of operation, and fewer adverse events. A systematic review showed that moxibustion could participate in inhibiting oxidative stress and cell apoptosis, adjusting inflammation and Aβ genesis activation of vascular endothelial growth factor, and regulating the metabolic products in the tricarboxylic acid cycle and fatty acid metabolism (Aum et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…It is a commonly accepted treatment approach for cognitive impairment in East Asia (Aum et al, 2021 ), with the advantages of painlessness, ease of operation, and fewer adverse events. A systematic review showed that moxibustion could participate in inhibiting oxidative stress and cell apoptosis, adjusting inflammation and Aβ genesis activation of vascular endothelial growth factor, and regulating the metabolic products in the tricarboxylic acid cycle and fatty acid metabolism (Aum et al, 2021 ). In addition, moxibustion could alleviate the depression-like behavior and adjust tryptophan transport and 5-HT generation (Li et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Efficacy and Mechanism of Moxibustion Treatment on Mild Cognitive Impairment Patients: An fMRI Study Using ALFF

Lai

Zhang

Liang

et al. 2022

Front. Mol. Neurosci.

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BackgroundMild Cognitive Impairment (MCI), as a high risk of Alzheimer’s disease (AD), represents a state of cognitive function between normal aging and dementia. Moxibustion may effectively delay the progression of AD, while there is a lack of studies on the treatments in MCI. This study aimed to evaluate the effect of moxibustion treatment revealed by the amplitude of low-frequency fluctuation (ALFF) in MCI.MethodWe enrolled 30 MCI patients and 30 matched healthy controls (HCs) in this study. We used ALFF to compare the difference between MCI and HCs at baseline and the regulation of spontaneous neural activity in MCI patients by moxibustion. The Mini-Mental State Examination and Montreal Cognitive Assessment scores were used to evaluate cognitive function.ResultsCompared with HCs, the ALFF values significantly decreased in the right temporal poles: middle temporal gyrus (TPOmid), right inferior temporal gyrus, left middle cingulate gyrus, and increased in the left hippocampus, left middle temporal gyrus, right lingual gyrus, and right middle occipital gyrus in MCI patients. After moxibustion treatment, the ALFF values notably increased in the left precuneus, left thalamus, right temporal poles: middle temporal gyrus, right middle frontal gyrus, right inferior temporal gyrus, right putamen, right hippocampus, and right fusiform gyrus, while decreased in the bilateral lingual gyrus in MCI patients. The Mini-Mental State Examination and Montreal Cognitive Assessment scores increased after moxibustion treatment, and the increase in Mini-Mental State Examination score was positively correlated with the increase of ALFF value in the right TPOmid, the right insula, and the left superior temporal gyrus.ConclusionMoxibustion treatment might improve the cognitive function of MCI patients by modulating the brain activities within the default mode network, visual network, and subcortical network with a trend of increased ALFF values and functional asymmetry of the hippocampus. These results indicate that moxibustion holds great potential in the treatment of MCI.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficacy and Mechanism of Moxibustion Treatment on Mild Cognitive Impairment Patients: An fMRI Study Using ALFF

Lai

Zhang

Liang

et al. 2022

Front. Mol. Neurosci.

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“…The data generated while working on a previous SR were used to train and validate the model. Two different tasks were tested for the two types of datasets [20][21][22][23][24][25]: datasetA with 3,268 articles [25,26] and datasetB with 409 case studies. The first task was to classify the included articles using datasetA and its adjusted version, whose class composition was adjusted from a ratio of 9.08:1 to 2.45:1 by generating dummy data.…”

Section: Methodsmentioning

confidence: 99%

Short Review on srBERT: Automatic Article Classification Model for Systematic Review Using BERT

Choe¹,

Aum²,

Kim³

2022

Asian J Complement Altern Med

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Systematic reviews (SRs) have been recognized as the most rigorous and reliable approach to enable evidence-based medicine. However, the considerable workload required to create SRs prevents reflecting the latest knowledge. This study automated the classification of included articles by adopting the Bidirectional Encoder Representations from Transformers (BERT) algorithm. By pretraining with abstracts of articles and a generated vocabulary fine-tuned with titles of articles, the proposed srBERTmy overcomes the training data insufficiency while improving performance in both classification and relation-extraction tasks. Despite the limitation of model vulnerabilities based on training dataset quality, the results demonstrated the feasibility of automatic article classification using machine-learning (ML) approaches to support SR tasks Keywords: Systematic review, process automation, deep learning, text mining

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“…To train the proposed algorithm, we used two types of datasets comprising documents that had been collected during SRs performed in previous works [19][20][21][22][23][24]. Data-setA comprises 3268 articles retrieved for the theme of "moxibustion for improving cognitive impairment" [24,25]. The first task using datasetA was to classify the included articles that satisfy the three theme criteria: (1) cognitive impairment as the target disease, (2) moxibustion therapy as the intervention, and (3) experimental design using animal models.…”

Section: Datasetsmentioning

confidence: 99%

srBERT: automatic article classification model for systematic review using BERT

Aum

Choe

2021

Syst Rev

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Background Systematic reviews (SRs) are recognized as reliable evidence, which enables evidence-based medicine to be applied to clinical practice. However, owing to the significant efforts required for an SR, its creation is time-consuming, which often leads to out-of-date results. To support SR tasks, tools for automating these SR tasks have been considered; however, applying a general natural language processing model to domain-specific articles and insufficient text data for training poses challenges. Methods The research objective is to automate the classification of included articles using the Bidirectional Encoder Representations from Transformers (BERT) algorithm. In particular, srBERT models based on the BERT algorithm are pre-trained using abstracts of articles from two types of datasets, and the resulting model is then fine-tuned using the article titles. The performances of our proposed models are compared with those of existing general machine-learning models. Results Our results indicate that the proposed srBERTmy model, pre-trained with abstracts of articles and a generated vocabulary, achieved state-of-the-art performance in both classification and relation-extraction tasks; for the first task, it achieved an accuracy of 94.35% (89.38%), F1 score of 66.12 (78.64), and area under the receiver operating characteristic curve of 0.77 (0.9) on the original and (generated) datasets, respectively. In the second task, the model achieved an accuracy of 93.5% with a loss of 27%, thereby outperforming the other evaluated models, including the original BERT model. Conclusions Our research shows the possibility of automatic article classification using machine-learning approaches to support SR tasks and its broad applicability. However, because the performance of our model depends on the size and class ratio of the training dataset, it is important to secure a dataset of sufficient quality, which may pose challenges.

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Moxibustion for cognitive impairment: A systematic review and meta-analysis of animal studies

Cited by 12 publications

References 52 publications

Efficacy and Mechanism of Moxibustion Treatment on Mild Cognitive Impairment Patients: An fMRI Study Using ALFF

Efficacy and Mechanism of Moxibustion Treatment on Mild Cognitive Impairment Patients: An fMRI Study Using ALFF

Short Review on srBERT: Automatic Article Classification Model for Systematic Review Using BERT

srBERT: automatic article classification model for systematic review using BERT

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