Voluntary Exercise to Reduce Anxiety Behaviour in Traumatic Brain Injury Shown to Alleviate Inflammatory Brain Response in Mice.

Traumatic brain injury is a leading cause of neuroinflammation and anxiety disorders in young adults. Immune-targeted therapies have garnered attention for the amelioration of TBI-induced anxiety. A previous study has indicated that voluntary exercise intervention following TBI could reduce neuroinflammation. It is essential to determine the effects of voluntary exercise after TBI on anxiety via inhibiting neuroinflammatory response. Mice were randomly divided into four groups (sham, TBI, sham + voluntary wheel running (VWR), and TBI + VWR). One-week VWR was carried out on the 2nd day after trauma. The neurofunction of TBI mice was assessed. Following VWR, anxiety behavior was evaluated, and neuroinflammatory responses in the perilesional cortex were investigated. Results showed that after one week of VWR, neurofunctional recovery was enhanced, while the anxiety behavior of TBI mice was significantly alleviated. The level of pro-inflammatory factors decreased, and the level of anti-inflammatory factors elevated. Activation of nucleotide oligomerization domain-like thermal receptor protein domain associated protein 3 (NLRP3) inflammasome was inhibited significantly. All these alterations were consistent with reduced microglial activation at the perilesional site and positively correlated with the amelioration of anxiety behavior. This suggested that timely rehabilitative exercise could be a useful therapeutic strategy for anxiety resulting from TBI by targeting neuroinflammation.

Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.

Breast cancer was the fourth leading cause of cancer-related death worldwide, and early mammography screening could decrease the breast cancer mortality. Artificial intelligence (AI)-assisted diagnose system based on machine learning (ML) methods can help improve the screening accuracy and efficacy. This study aimed to systematically review and make a meta-analysis on the diagnostic accuracy of mammography diagnosis of breast cancer through various ML methods. Springer Link, Science Direct (Elsevier), IEEE Xplore, PubMed and Web of Science were searched for relevant studies published from January 2000 to September 2021. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42021284227). A Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the included studies, and reporting was evaluated using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The pooled summary estimates for sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) for three ML methods (convolutional neural network [CNN], artificial neural network [ANN], support vector machine [SVM]) were calculated. A total of 32 studies with 23,804 images were included in the meta-analysis. The overall pooled estimate for sensitivity, specificity and AUC was 0.914 [95% CI 0.868-0.945], 0.916 [95% CI 0.873-0.945] and 0.945 for mammography diagnosis of breast cancer through three ML methods. The pooled sensitivity, specificity and AUC of CNN were 0.961 [95% CI 0.886-0.988], 0.950 [95% CI 0.924-0.967] and 0.974. The pooled sensitivity, specificity and AUC of ANN were 0.837 [95% CI 0.772-0.886], 0.894 [95% CI 0.764-0.957] and 0.881. The pooled sensitivity, specificity and AUC of SVM were 0.889 [95% CI 0.807-0.939], 0.843 [95% CI 0.724-0.916] and 0.913. Machine learning methods (especially CNN) show excellent performance in mammography diagnosis of breast cancer screening based on retrospective studies. More rigorous prospective studies are needed to evaluate the longitudinal performance of AI.