Visual SLAM for robot navigation in healthcare facility.

The COVID-19 pandemic has affected many countries, posing a threat to human health and safety, and putting tremendous pressure on the medical system. This paper proposes a novel SLAM technology using RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19. Most current visual SLAM researches assume that the environment is stationary, which makes handling real-world scenarios such as hospitals a challenge. This paper proposes a method that effectively deals with SLAM problems for scenarios with dynamic objects, e.g., people and movable objects, based on the semantic descriptor extracted from images with help of a knowledge graph. Specifically, our method leverages a knowledge graph to construct a priori movement relationship between entities and establishes high-level semantic information. Built upon this knowledge graph, a semantic descriptor is constructed to describe the semantic information around key points, which is rotation-invariant and robust to illumination. The seamless integration of the knowledge graph and semantic descriptor helps eliminate the dynamic objects and improves the accuracy of tracking and positioning of robots in dynamic environments. Experiments are conducted using data acquired from healthcare facilities, and semantic maps are established to meet the needs of robots for delivering medical services. In addition, to compare with the state-of-the-art methods, a publicly available dataset is used in our evaluation. Compared with the state-of-the-art methods, our proposed method demonstrated great improvement with respect to both accuracy and robustness in dynamic environments. The computational efficiency is also competitive.

Assigning channel weights using an attention mechanism: an EEG interpolation algorithm.

During the acquisition of electroencephalographic (EEG) signals, various factors can influence the data and lead to the presence of one or multiple bad channels. Bad channel interpolation is the use of good channels data to reconstruct bad channel, thereby maintaining the original dimensions of the data for subsequent analysis tasks. The mainstream interpolation algorithm assigns weights to channels based on the physical distance of the electrodes and does not take into account the effect of physiological factors on the EEG signal. The algorithm proposed in this study utilizes an attention mechanism to allocate channel weights (AMACW). The model gets the correlation among channels by learning from good channel data. Interpolation assigns weights based on learned correlations without the need for electrode location information, solving the difficulty that traditional methods cannot interpolate bad channels at unknown locations. To avoid an overly concentrated weight distribution of the model when generating data, we designed the channel masking (CM). This method spreads attention and allows the model to utilize data from multiple channels. We evaluate the reconstruction performance of the model using EEG data with 1 to 5 bad channels. With EEGLAB's interpolation method as a performance reference, tests have shown that the AMACW models can effectively reconstruct bad channels.

Clinical significance of tumor miR-21, miR-221, miR-143, and miR-106a as biomarkers in patients with osteosarcoma.

OBJECTIVE: To investigate the expression of miR-21, miR-221, miR-143, and miR-106a in patients' osteosarcoma samples, and to explore the correlation between these microRNAs (miRNAs) and the clinical stage of osteosarcoma. METHODS: RNA was extracted from tumor and tumor-adjacent normal bone tissues from 94 patients with osteosarcoma. RNA reverse-transcription was carried out using an miRNA reverse transcription kit. The levels of miR-21, miR-221, miR-143, and miR-106a in osteosarcoma and normal bone tissues were analyzed by real-time polymerase chain reaction using SYBR Premix Ex Taq™II. RESULTS: The expression levels of miR-21, mirR-221, and miR-106a were significantly higher in 90.42%, 84.04%, and 92.55 % of the osteosarcoma samples compared to the adjacent normal tissues (P<0.05), respectively. While the expression of miR-143 was significantly lower compared to the adjacent normal tissues (P <0.05). Moreover, the expression levels of miR-21 and miR-221 were positively correlated with the Enneking clinical stage and the presence of lung metastasis (P <0.05), while the expression levels of miR-143 and miR-106a showed a significant inverse and direct correlation respectively, with the tumor grade. CONCLUSIONS: The upregulation of miR-21, miR-221, and miR-106a, as well as the down-regulation of miR-143 were correlated with the pathological stage, tumor grade, and lung metastasis. Therefore, the levels of these miRNAs can serve as potential biomarkers for the early diagnosis of osteosarcoma, and can be used as potential therapeutic targets.

Galangin protects human rheumatoid arthritis fibroblast­like synoviocytes via suppression of the NF­κB/NLRP3 pathway.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that significantly affects patient quality of life. Galangin is an extract with multiple health benefits, including anti­oxidative, anti­proliferative, immunoprotective and cardioprotective effects. However, to the best of the authors' knowledge, no detailed studies have investigated its regulatory effects on the nuclear factor (NF)­κB/NLR family pyrin domain containing 3 (NLRP3) signaling pathway. The present study aimed to investigate the protective mechanism of galangin in RA fibroblast­like synoviocytes with regards to the NF­κB/NLRP3 signaling pathway. Human RA fibroblast­like synovium cells (RAFSCs) were treated with lipopolysaccharide (LPS) to induce inflammation. The levels of interleukin (IL)­1ß, tumor necrosis factor (TNF)­α, IL­18, inducible nitric oxide synthase (iNOS), cyclooxygenase (COX)­2, prostaglandin E2 (PGE2), and nitric oxide (NO) were measured by enzyme­linked immunosorbent assay or western blotting in the absence or presence of different concentrations of galangin. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) content were additionally evaluated. Furthermore, factors involved in the NF­κB/NLRP3 pathway, including NLRP3, apoptosis­associated speck­like protein containing A, IL­1ß, pro­caspase­1, caspase­1, phosphorylated (p)­NF­κB inhibitor α and p­NF­κB, were assessed by western blotting. The results revealed that LPS significantly stimulated IL­1ß, TNF­α, IL­18, PGE2, NO, iNOS, COX­2 and NF­κB/NLRP3 factor expression, compared with the control. SOD activity was reduced. Pre­treatment with galangin significantly attenuated the effects of LPS, and galangin was demonstrated to have effective anti­oxidative properties. In conclusion, galangin protected RAFSCs through downregulation of the NF­κB/NLRP3 signaling pathway. These findings suggested that galangin may provide a novel direction for the development of RA therapies in the future.