The prevention effect of pulsed electromagnetic fields treatment on senile osteoporosis in vivo via improving the inflammatory bone microenvironment.

This study aimed to assess PEMF in a rat model of senile osteoporosis and its relationship with NLRP3-mediated low-grade inflammation in the bone marrow microenvironment. A total of 24 Sprague Dawley (SD) rats were included in this study. Sixteen of them were 24-month natural-aged male SD rats, which were randomly distributed into the Aged group and the PEMF group (n = 8 per group). The remaining 8 3-month -old rats were used as the Young positive control group (n = 8). Rats in the PEMF group received 12 weeks of PEMF with 40 min/day, five days per week, while the other rats received placebo PEMF intervention. Bone mineral density/microarchitecture, serum levels of CTX-1 and P1CP, and NLRP3-related signaling genes and proteins in rat bone marrow were then analyzed. The 12-week of PEMF showed significant mitigation of aging-induced bone loss and bone microarchitecture deterioration, i.e. PEMF increased the bone mineral density of the proximal femur and L5 vertebral body and improved parameters of the proximal tibia and L4 vertebral body. Further analysis showed that PEMF reversed aging-induced bone turnover, specifically, decreased serum CTX-1 and elevated serum P1CP. Furthermore, PEMF also dramatically inhibited NLRP3-mediated low-grade inflammation in the bone marrow, i.e. PEMF inhibited the levels of NLRP3, proCaspase1, cleaved Caspase1, IL-1ß, and GSDMD-N. The study demonstrated that PEMF could mitigate the aging-induced bone loss and reverses the deterioration of bone microarchitecture probably through inhibiting NLRP3-mediated low-grade chronic inflammation to improve the inflammatory bone microenvironment in aged rats.

A super-resolution network using channel attention retention for pathology images.

Image super-resolution (SR) significantly improves the quality of low-resolution images, and is widely used for image reconstruction in various fields. Although the existing SR methods have achieved distinguished results in objective metrics, most methods focus on real-world images and employ large and complex network structures, which are inefficient for medical diagnosis scenarios. To address the aforementioned issues, the distinction between pathology images and real-world images was investigated, and an SR Network with a wider and deeper attention module called Channel Attention Retention is proposed to obtain SR images with enhanced high-frequency features. This network captures contextual information within and across blocks via residual skips and balances the performance and efficiency by controlling the number of blocks. Meanwhile, a new linear loss was introduced to optimize the network. To evaluate the work and compare multiple SR works, a benchmark dataset bcSR was created, which forces a model training on wider and more critical regions. The results show that the proposed model outperforms state-of-the-art methods in both performance and efficiency, and the newly created dataset significantly improves the reconstruction quality of all compared models. Moreover, image classification experiments demonstrate that the suggested network improves the performance of downstream tasks in medical diagnosis scenarios. The proposed network and dataset provide effective priors for the SR task of pathology images, which significantly improves the diagnosis of relevant medical staff. The source code and the dataset are available on https://github.com/MoyangSensei/CARN-Pytorch.

Research status of deep learning methods for rumor detection.

To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.