With the booming development of medical informatization and the ubiquitous connections in the fifth generation mobile communication technology (5G) era, the heterogeneity and explosive growth of medical data have brought huge challenges to data access, security and privacy, as well as information processing in Internet of Medical Things (IoMT). This article provides a comprehensive review of how to realize the timely processing and analysis of medical big data and the sinking of high-quality medical resources under the constraints of the existing medical environment and medical-related equipment. We mainly focus on the advantages brought by the cloud computing, edge computing and artificial intelligence technologies to the IoMT. We also explore how to rationalize the use of medical resources and the security and privacy of medical data, so that high-quality medical services can be provided to patients. Finally, we discuss the current challenges and possible future research directions in the edge-cloud computing and artificial intelligence related IoMT.

Background: High-frequency (HF) repetitive transcranial magnetic stimulation (rTMS) over the left dorsolateral prefrontal cortex (L-DLPFC) is the most widely applied treatment protocol for major depressive disorder (MDD), while low-frequency (LF) rTMS over the right DLPFC (R-DLPFC) also exhibits similar, if not better, efficacy for MDD. Therefore, a meta-analysis is warranted to compare the efficacy of the two protocols for MDD. Method: We searched the literature from 1990 through to August 1, 2017 using MEDLINE, and the literature from 1995 through to August 1, 2017 using EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials (CENTRAL), SCOPUS, and ProQuest Dissertations and Theses (PQDT). We included randomized controlled trials (RCT) comparing the efficacy of HF rTMS over the L-DLPFC and LF rTMS over the R-DLPFC for MDD, which used response and/or remission rates as the primary endpoints, with and without sham-controlled. Results: (1) The meta-analysis of the response rates was based on 12 studies, including 361 patients with MDD (175 for HF (> 5 Hz) over the L-DLPFC, and 186 for LF (<5 Hz) over the R-DLPFC; odds ratio = 1.08; 95%, confidence interval = 0.88–1.34). (2) The meta-analysis of the remission rate was based on 5 studies, including 131 MDD patients (64 for HF over the L-DLPFC and 67 for LF over the R-DLPFC; odds ratio = 1.29; 95% confidence interval = 0.54–3.10). Conclusion: Both HF rTMS over the L-DLPFC and LF over the R-DLPFC demonstrated similar therapeutic efficacy for the treatment of patients with MDD. The results suggested that further investigation on treatment efficacy indicators before/during treatment is necessary and helpful for optimizing a personalized protocol for patients.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

Neuroinflammation is tightly associated with onset of depression. The nuclear receptor related 1 protein (Nurr1, also called Nr4a2), its roles in dopaminergic neurons is well understood, which can alleviate inflammation. Nevertheless, potential effects of Nr4a2 on neuroinflammation associated with depression still remains unclear. Chronic lipopolysaccharides (LPS) stress induced depressive-behaviors were confirmed via behavioral tests. Differentially expressed genes were detected by using RNA-sequencing. The anterior cingulate cortex (ACC) tissues were collected for biochemical experiments. The Golgi-Cox staining and virus labeling were used to evaluate the dendritic spines. We applied fluoxetine (FLX) and amodiaquine dihydrochloride (AQ, a highly selective agonist of Nr4a2) in mice. Overexpression experiments were performed by injecting with AAV-Nr4a2-EGFP into ACC. Chemogenetic activation of CamkII neurons via injecting the hM3Dq virus. Mice treated with LPS displayed depressive- and anxiety-like behaviors. The reduction of Nr4a2 and FosB induced by LPS were rescued by pretreatment with FLX or AQ. More importantly, LPS-induced behavior deficits in mice were also alleviated via fluoxetine treatment and pharmacological activation the expression of Nr4a2. Meanwhile, enhancing the level of Nr4a2 could improve dendritic spines loss of neuron and morphological changes in microglia. Overexpression of Nr4a2 in ACC reversed the depressive- and anxiety-like behaviors caused by LPS administration. Activation of CamkII neurons in ACC could robustly increase the expression of Nr4a2 and improve LPS-induced behavior deficits. Our findings demonstrate that the Nr4a2 may regulate depressive-like behaviors via alleviating the impairment of morphology and function on microglia and CamkII neurons induced by chronic neuroinflammation.