A bibliometric analysis of international publication trends in brain atrophy research (2008-2023).

Background: Brain atrophy is a type of neurological and psychiatric disorder characterized by a decrease in brain tissue volume and weight for various reasons and can have a serious impact on the quality of life of patients. Although there are many studies on brain atrophy, there is a lack of relevant bibliometric studies. Therefore, this study aims to provide a visual analysis of global trends in brain atrophy research over the past 16 years. Methods: CiteSpace and VOSviewer were used to visually analyze publication output, scientific collaborations, cocitations, publishing journals, and keywords to determine the current status and future trends of brain atrophy research. Materials published from 2008 to 2023 were collected from the Web of Science Core Collection (WoSCC) database. This study placed no restrictions on the types of literature and focused on English language publications. Results: A total of 3,371 publications were included in the analysis. From 2008 to 2023, the number of publications increased annually. In terms of national and academic institutions, universities in the United States and University College London rank first in publication out. Barkhof Frederik and Zivadinov Robert are the most prolific researchers in this field. The publication with the highest cocitation strength is "Deep gray matter volume loss drives disability worsening in multiple sclerosis." Keyword clustering analysis showed that "Alzheimer's disease" and "multiple sclerosis" are current popular topics. The analysis of emergent words indicates that "cerebral small vessel disease," "neurodegeneration," and "cortex/gray matter volume" may become hot research topics in the coming years. Conclusion: This study analyses papers on brain atrophy from the past 16 years, providing a new perspective for research in this field. In the past 16 years, research on brain atrophy has received increasing attention. The quality of articles in this field is generally high. Extensive national cooperation already exists. The statistical results indicate that a stable core author group in the field of brain atrophy has almost formed.

Assessing the role of combination of stem cell and light-based treatments on skin wound repair: A meta-analysis.

The meta-analysis aims to evaluate and compare the impact of the combination of stem cells (SCs) and light-based treatments (LBTs) on skin wound (SW) repair. Examinations comparing SCs to LBT with SCs for SW repair was among the meta-analysis from various languages that met the inclusion criteria. Using continuous random-effect models, the results of these investigations were examined, and the mean difference (MD) with 95% confidence intervals was computed (CIs). Seven examinations from 2012 to 2022 were recruited for the current analysis including 106 animals with SWs. Photobiomodulation therapy (PBT) plus SCs had a significantly higher wound closure rate (WCR) (MD, 9.08; 95% CI, 5.55-12.61, p < 0.001) compared to SCs in animals with SWs. However, no significant difference was found between PBT plus SCs and SCs on wound tensile strength (WTS) (MD, 2.01; 95% CI, -0.42 to 4.44, p = 0.10) in animals with SWs. The examined data revealed that PBT plus SCs had a significantly higher WCR, however, no significant difference was found in WTS compared to SCs in animals with SWs. Nevertheless, caution should be exercised while interacting with its values since all the chosen examinations were found with a low sample size and a low number of examinations were found for the comparisons studied for the meta-analysis.

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning.

PURPOSE: To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a polyp segmentation method has been presented. METHODS: An ensemble model of pretrained convolutional neural networks was proposed, using Unet-VGG, SegNet-VGG, and PSPNet. Firstly, the Unet-VGG is obtained by the first 10 layers of VGG16 as the contraction path of the left half of the Unet. Then, the SegNet-VGG is acquired by fine-tuned transfer learning VGG16, using the first 13 layers of VGG16 as the encoder of the SegNet and combined the original decoder of the SegNet. By adjusting the input size of the Unet-VGG, SegNet-VGG, and PSPNet, the preprocessed data can be correctly fed to the three network models. The three models are used as the basic trainer to train and segment the datasets. Based on the ensemble learning algorithm, the weight voting method is used to ensemble the segmentation results corresponding to single basic trainer. RESULTS: Both IoU and DICE similarity score were used to evaluate the segmentation quality for cvc300 with 300 images, CVC-ClinicDB with 612 images, and ETIS-LaribPolypDB with 196 images. From the experimental results, the IoU and DICE obtained by the proposed method for the cvc300 datasets can reach up to 96.16% and 98.04%, respectively, the IoU and DICE for the CVC-ClinicDB datasets can reach up to 96.66% and 98.30%, respectively, whereas the IoU and DICE for the ETIS-LaribPolypDB datasets can reach up to 96.95% and 98.45%, respectively. Evaluation of the IoU and DICE in our methods shows higher accuracy than previous methods. CONCLUSIONS: The experimental results show that the proposed method improved correspondingly in IoU and DICE compared to a single basic trainer. The range of improvement is 1.98%-6.38%. The proposed ensemble learning succeeds in automatic polyp segmentation, which potentially helps to establish more polyp datasets.

ATP Binding Cassette Subfamily A Member 2 (ABCA2) Expression and Methylation are Associated with Alzheimer's Disease.

BACKGROUND ABCA2 has been genetically linked to Alzheimer's disease (AD) risk, but its mRNA expression and epigenetics in AD have not been investigated. MATERIAL AND METHODS To explore the diagnosis value of ABCA2 mRNA expression in AD, 2 datasets GES15222 and GSE33000 containing expression profile of brain cortex tissues and 2 datasets GSE63063 (Cohort 1) and GSE63063 (Cohort 2) containing expression profile of blood were downloaded from the NCBI GEO database and analyzed by receiver operating characteristic curve (ROC) analyses and logistic regression. The ABCA2 co-expressed genes were also analyzed by GO annotation to investigate the potential molecular mechanisms. RESULTS The analyses results suggested ABCA2 mRNA expression was upregulated significantly in AD compared with controls in all datasets. ROC analysis suggested that ABCA2 was associated with AD in all datasets, which were also proved by univariate and multivariate analyses. Next, the dataset GSE80970 containing methylation profiles of prefrontal cortex tissues from AD patients were downloaded and analyzed. Methylation of 2 of 36 CpG islands in ABCA2 gene with high diagnostic accuracy of AD from controls in ROC analyses were found to be negatively associated with AD risk in univariate analysis. One was still associated with AD risk after adjustment of confounding factors. Additional analyses indicated that ACBA2 mRNA expression could be used to diagnose mild cognitive impairment (MCI) and Huntington's disease (HD) from controls and to distinguish HD from AD, but not AD from MCI. Furthermore, the genes involved in AD during ABCA2 alteration were analyzed by GO analysis. CONCLUSIONS ABCA2 mRNA expression and methylation is associated AD risk. ABCA2 may be used as a biomarker for AD diagnosis and may be a potential therapeutic target of AD.