A Global and Local Feature fused CNN architecture for the sEMG-based hand gesture recognition.

Deep learning methods have been widely used for the classification of hand gestures using sEMG signals. Existing deep learning architectures only captures local spatial information and has limitations in extracting global temporal dependency to enhance the model's performance. In this paper, we propose a Global and Local Feature fused CNN (GLF-CNN) model that extracts features both globally and locally from sEMG signals to enhance the performance of hand gestures classification. The model contains two independent branches extracting local and global features each and fuses them to learn more diversified features and effectively improve the stability of gesture recognition. Besides, it also exhibits lower computational cost compared to the present approaches. We conduct experiments on five benchmark databases, including the NinaPro DB4, NinaPro DB5, BioPatRec DB1-DB3, and the Mendeley Data. The proposed model achieved the highest average accuracy of 88.34% on these databases, with a 9.96% average accuracy improvement and a 50% reduction in variance compared to the models with the same number of parameters. Moreover, the classification accuracies for the BioPatRec DB1, BioPatRec DB3 and Mendeley Data are 91.4%, 91.0% and 88.6% respectively, corresponding to an improvement of 13.2%, 41.5% and 12.2% over the respective state-of-the-art models. The experimental results demonstrate that the proposed model effectively enhances robustness, with improved gesture recognition performance and generalization ability. It contributes a new way for prosthetic control and human-machine interaction.

Novel insight into the underlying dysregulation mechanisms of immune cell-to-cell communication by analyzing multitissue single-cell atlas of two COVID-19 patients.

How does SARS-CoV-2 cause lung microenvironment disturbance and inflammatory storm is still obscure. We here performed the single-cell transcriptome sequencing from lung, blood, and bone marrow of two dead COVID-19 patients and detected the cellular communication among them. Our results demonstrated that SARS-CoV-2 infection increase the frequency of cellular communication between alveolar type I cells (AT1) or alveolar type II cells (AT2) and myeloid cells triggering immune activation and inflammation microenvironment and then induce the disorder of fibroblasts, club, and ciliated cells, which may cause increased pulmonary fibrosis and mucus accumulation. Further study showed that the increase of T cells in the lungs may be mainly recruited by myeloid cells through ligands/receptors (e.g., ANXA1/FPR1, C5AR1/RPS19, and CCL5/CCR1). Interestingly, we also found that certain ligands/receptors (e.g., ANXA1/FPR1, CD74/COPA, CXCLs/CXCRs, ALOX5/ALOX5AP, CCL5/CCR1) are significantly activated and shared among lungs, blood and bone marrow of COVID-19 patients, implying that the dysregulation of ligands/receptors may lead to immune cell's activation, migration, and the inflammatory storm in different tissues of COVID-19 patients. Collectively, our study revealed a possible mechanism by which the disorder of cell communication caused by SARS-CoV-2 infection results in the lung inflammatory microenvironment and systemic immune responses across tissues in COVID-19 patients.

3044 Cases reveal important prognosis signatures of COVID-19 patients.

Critical patients and intensive care unit (ICU) patients are the main population of COVID-19 deaths. Therefore, establishing a reliable method is necessary for COVID-19 patients to distinguish patients who may have critical symptoms from other patients. In this retrospective study, we firstly evaluated the effects of 54 laboratory indicators on critical illness and death in 3044 COVID-19 patients from the Huoshenshan hospital in Wuhan, China. Secondly, we identify the eight most important prognostic indicators (neutrophil percentage, procalcitonin, neutrophil absolute value, C-reactive protein, albumin, interleukin-6, lymphocyte absolute value and myoglobin) by using the random forest algorithm, and find that dynamic changes of the eight prognostic indicators present significantly distinct within differently clinical severities. Thirdly, our study reveals that a model containing age and these eight prognostic indicators can accurately predict which patients may develop serious illness or death. Fourthly, our results demonstrate that different genders have different critical illness rates compared with different ages, in particular the mortality is more likely to be attributed to some key genes (e.g. ACE2, TMPRSS2 and FURIN) by combining the analysis of public lung single cells and bulk transcriptome data. Taken together, we urge that the prognostic model and first-hand clinical trial data generated in this study have important clinical practical significance for predicting and exploring the disease progression of COVID-19 patients.

Evolution of COVID-19 in patients with autoimmune rheumatic diseases.

The characteristics of COVID-19 patients with autoimmune rheumatic diseases (AIRD) have rarely been reported. Patients with AIRD have suppressed immune defense function, which may increase their susceptibility to COVID-19. However, the immunosuppressive agents AIRD patients routinely used may be beneficial for protecting the cytokine storm caused by SARS-CoV-2. In this retrospective study, we included all confirmed cases in Huoshenshan Hospital from February 4 to April 9. Data were extracted from electronic medical records and were analyzed for clinical and laboratory features using SPSS (version 25.0). Of 3059 patients, 21 had the comorbidities with systematic lupus erythematosus (SLE) and/or rheumatoid arthritis (RA), including 5 with SLE, 15 with RA, and 1 with Rhupus. The proportion was 57.1% for severe cases, 61.9% for either severe or critical cases, and 4.8% for critical cases. The main manifestations, ARDS and ICU admission rate, as well as the mortality and length of hospital stay of COVID-19 in AIRD patients were similar to COVID-19 patients in the general population. Our preliminary experience shows that patients with AIRD tend to have a higher risk of SARS-CoV-2 infection, and may be at risk for a severe but less likely critical disease course. Further investigation is needed to understand the immunological features of these diseases.

Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19.

Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.

Improved Glucose-Stimulated Insulin Secretion by Selective Intraislet Inhibition of Angiotensin II Type 1 Receptor Expression in Isolated Islets of db/db Mice.

Recent evidence supported the presence of a local renin-angiotensin system (RAS) in the pancreas, which is implicated in many physiological and pathophysiological processes. We utilized small interfering RNA (siRNA) to investigate the effects of angiotensin II type 1 receptor (AT1R) knockdown on glucose-stimulated insulin secretion (GSIS) in isolated islets of db/db mice and to explore the potential mechanisms involved. We found that Ad-siAT1R treatment resulted in a significant decrease both in AT1R mRNA level and in AT1R protein expression level. With downexpression of AT1R, notable increased insulin secretion and decreased glucagon secretion levels were found by perifusion. Simultaneously, significant increased protein levels of IRS-1 (by 85%), IRS-2 (by 95%), PI3K(85) (by 112.5%), and p-Akt2 (by 164%) were found by western blot. And upregulation of both GLUT-2 (by 190%) and GCK (by 121%) was achieved after AT1R inhibition by Ad-siAT1R. Intraislet AT1R expression level is a crucial physiological regulator of insulin sensitivity of ß cell itself and thus affects glucose-induced insulin and glucagon release. Therefore, the characteristics of AT1R inhibitors could make it a potential novel therapeutics for prevention and treatment of type 2 diabetes.