Verbenalin alleviates acute lung injury induced by sepsis and IgG immune complex through GPR18 receptor.

Acute lung injury is significantly associated with the aberrant activation and pyroptosis of alveolar macrophages. Targeting the GPR18 receptor presents a potential therapeutic approach to mitigate inflammation. Verbenalin, a prominent component of Verbena in Xuanfeibaidu (XFBD) granules, is recommended for treating COVID-19. In this study, we demonstrate the therapeutic effect of verbenalin on lung injury through direct binding to the GPR18 receptor. Verbenalin inhibits the activation of inflammatory signaling pathways induced by lipopolysaccharide (LPS) and IgG immune complex (IgG IC) via GPR18 receptor activation. The structural basis for verbenalin's effect on GPR18 activation is elucidated through molecular docking and molecular dynamics simulations. Furthermore, we establish that IgG IC induces macrophage pyroptosis by upregulating the expression of GSDME and GSDMD through CEBP-δ activation, while verbenalin inhibits this process. Additionally, we provide the first evidence that IgG IC promotes the formation of neutrophil extracellular traps (NETs), and verbenalin suppresses NETs formation. Collectively, our findings indicate that verbenalin functions as a "phytoresolvin" to promote inflammation regression and suggests that targeting the C/EBP-δ/GSDMD/GSDME axis to inhibit macrophage pyroptosis may represent a novel strategy for treating acute lung injury and sepsis.

Gut microbiota in COVID-19: new insights from inside.

The epidemic of coronavirus disease-19 (COVID-19) has grown to be a global health threat. Gastrointestinal symptoms are thought to be common clinical manifestations apart from a series of originally found respiratory symptoms. The human gut harbors trillions of microorganisms that are indispensable for complex physiological processes and homeostasis. Growing evidence demonstrate that gut microbiota alteration is associated with COVID-19 progress and severity, and post-COVID-19 syndrome, characterized by decrease of anti-inflammatory bacteria like Bifidobacterium and Faecalibacterium and enrichment of inflammation-associated microbiota including Streptococcus and Actinomyces. Therapeutic strategies such as diet, probiotics/prebiotics, herb, and fecal microbiota transplantation have shown positive effects on relieving clinical symptoms. In this article, we provide and summarize the recent evidence about the gut microbiota and their metabolites alterations during and after COVID-19 infection and focus on potential therapeutic strategies targeting gut microbiota. Understanding the connections between intestinal microbiota and COVID-19 would provide new insights into COVID-19 management in the future.

Autonomous Oropharyngeal-Swab Robot System for COVID-19 Pandemic

The outbreak of COVID-19 has led to the shortage of medical personnel and the increasing need for nucleic acid testing. Manual oropharyngeal sampling is susceptible to inconsistency caused by fatigue and close contact could also cause healthcare personnel exposure and cross infection. The innate deficiency calls for a safer and more consistent way to collect the oropharyngeal samples. Therefore a fully autonomous oropharyngeal-swab robot system is proposed in this paper. The system is installed in a negative pressure chamber and carrying out a standardized sampling process to minimize individual sampling differences. A hierarchical throat detection algorithm is presented and multiple modality sensory information are fused to safely and accurately localize the optimum sampling location. Also, a force/position hybrid control method is adopted to ensure both accurate sampling and subject comfort. The robot system described in this paper can safely and efficiently collect the oropharyngeal sample, providing a scalable solution for large-scale Polymerase Chain Reaction (PCR) Molecular sample collection for various respiratory diseases. Note to Practitioners-During the COVID-19 pandemic, pre-diagnostic is essential for both prevention and treatment. Existing approaches, including nasal swab and oropharyngeal-swab, require extensive medical worker training and increase the chance of cross-infection. The robot system introduced in this paper can take oropharyngeal-swab samples from subjects with minimum human intervention, reducing medical worker exposure, alleviating the work pressure of medical staff, and speed up large quantity of sampling plan. The robot will first guide the subject into position with vocal commands, and automatically detect the optimum sampling location with a real-time machine learning algorithm. A dedicated control strategy aiming at minimizing discomfort and uniforming sample quantity is then applied to safely collect nucleic samples from the throat. Eventually, while the swab is being stored in the culture medium, a disinfection process is carried out simultaneously to prepare the robot for the next subject. Preliminary clinical trials show that our robot system can safely and accurately collect samples from subjects.

Traditional Chinese exercise potential role as prevention and adjuvant therapy in patients with COVID-19.

The epidemic situation of COVID-19 is a great public health emergency worldwide characterized by fastest spreading, widest infection range and the mostly difficult to prevent and control in recent years. According to medical experience, traditional Chinese exercises (TCE) have been applied for COVID-19 prevention, adjuvant treatment or rehabilitation, and achieved some curative effects. They can enhance the body immunity, improve the function of organs, especially cardiopulmonary function, promote physical and mental rehabilitation by adjusting the body, regulating the breath, regulating the mind. This paper aims to investigate the potential value of TCE for health preservation in the prevention and adjuvant treatment for COVID-19 according to an overview of application and analysis of existing evidence. On this basis, this review proposed the TCE plan by visiting clinical and practice experts, so as to provide some references for the prevention and treatment of COVID-19 with TCE in the world.

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients.

OBJECTIVE: This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. MATERIALS METHODS: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume - lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error. RESULTS: Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 ± 9.89%, 82.94 ± 16.49%, and 66.19 ± 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively. CONCLUSION: CT quantification and machine-learning models show great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.