Association of interleukin-6 and CD4+ T cells and two-week prognosis of patients with COVID-19: a predictive role.

OBJECTIVE:  The aim of this study was to determine the association of inflammation and immune responses with the outcomes of patients at various stages, and to develop risk stratification for improving clinical practice and reducing mortality. PATIENTS AND METHODS: We included 77 patients with primary outcomes of either death or survival. Demographics, clinical features, comorbidities, and laboratory tests were compared. Linear, logistic, and Cox regression analyses were performed to determine prognostic factors. RESULTS: The average age was 59 years (35-87 years). There were 12 moderate cases (16.2%), 42 severe cases (54.5%), and 23 critical cases (29.9%); and 41 were male (53.2%). Until March 20, 68 cases were discharged (88.3%), and nine critically ill males (11.7%) died. Interleukin-6 (IL-6) levels on the 1st day were compared with IL-6 values on the 14th day in the severe and the critically ill surviving patients (F=4.90, p=0.034, ß=0.35, 95% CI: 0.00-0.10), and predicted death in the critically ill patients (p=0.028, ß=0.05, OR: 1.05, 95% CI: 1.01-1.10). CD4+ T-cell counts at admission decreased the hazard ratio of death (p=0.039, ß=-0.01, hazard ratio=0.99, 95% CI: 0.98-1.00, and median survival time 13.5 days). CONCLUSIONS: The present study demonstrated that IL-6 levels and CD4+ T-cell count at admission played key roles of predictors in the prognosis, especially for critically ill patients. High levels of IL-6 and impaired CD4+t cells are seen in severe and critically ill patients with COVID-19.

Focus and Concentrate! Exploring the Use of Conversational Robot to Improve Self-Learning Performance during Pandemic Isolation by Closed-Loop Brainwave Neurofeedback

Recently, Human-Robot Interaction (HRI) researchers have paid lots of attentions on the conversational robots that can provide didactic or pedagogy teachings, especially for the children and young adolescents with cognitive disabilities, such as autism and epilepsy. The research and developments of such robots for educational purposes are investigated intensively. Above all the difficult challenges, how to evaluate the effectiveness of a conversational robot, which mimics a teacher communicating with a student, to improve the performance of learning and studying, is the key factor to deploy such robots in our society and be widely adopted. However, we haven't seen much investigation in previous literatures so far. In order to bridge the gap, this preliminary study had explored the use of conversational robot with electroencephalogram (EEG) biosignals as evidence measurements to improve the self-learning performance during COVID-19 pandemic crisis, while the schools are forced to close, and the students are inevitably segregated in social isolations. We had collected 10 student participants' EEG data, which were calculated to find concentration levels, and then the robot had conversations with the students adaptively according to his/her concentration levels. The result showed that the conversations between robot and humans, who are constantly not concentrating on the learning tasks, could effectively increase his/her level of concentrations.

A quick data mining and analysis of lopinavir/ritonavir (LPV/r) adverse events based on Eudravigilance Medicines Safety Database

Objective: This study used data mining to conduct real-world research on adverse drug events (ADEs) after LPV/r was launched on the market, providing a reference for clinical identification and treatment of early ADEs. Methods: MedDRA terminology in different levels of the reproductive system were selected, and all LPV/r-related ADE reports were extracted from the European Drug Adverse Event Reporting Database (Eudravigilance). Results: Until February 1, 2020, 3 753 reports of LPV/r-related ADEs were collected in Eudravigilance, with 5 833 drug-related adverse events. In terms of gender composition, males (54.5%) are higher than females (37.5%), and the age is concentrated between 18~64 years (69.0%). The reporting population is mainly "medical staff reporting" (82.8%). The ADE reports are focused on the system classification, much involving the following systems: gastrointestinal system, hepatobiliary system, nervous system, nutritional metabolism system, and cardiovascular system, etc. This research uses PT terminology to query each system report, showing that LPV/r-related ADEs invaded the gastrointestinal system and often manifested as diarrhea 205 (22.4%), vomiting 135 (14.7%), nausea124 (13.5%), and severe of pancreatitis 89 (9.7%) and gastrointestinal bleeding 57 (6.2%), etc. Liver and gallbladder system mainly showed abnormal liver function 93 (14.7%), changes in liver function indicators and jaundice 67 (10.6%). Nervous system mainly involved symptoms of mild headache 199 (31.6%), dizziness 70 (11.1%). It is worth noting that LPV/r induced seizures 54 (8.6%) and peripheral neuropathy 33 (5.2%). Other severe system effects: 21 (3.3%) decrease in blood glucose, 163 (28.5%) hypotension, 50 (8.7%) increase in blood pressure, 10 (1.6%) hyperkalemia, 36 (6.3%) AV block, deep vein thrombosis 17 (3.0%), electrocardiogram QT interval prolongation 16 (2.8%), anemia 168 (37.3%), acute kidney injury 129 (32.3%), etc. Conclusion: In the clinical use of LPV/r, we should be alert to gastrointestinal bleeding, seizures, hypotension, thrombosis, prolonged QT interval, acute serious adverse events such as kidney injury and severe liver injury, in addition to some mild adverse events such as diarrhea, rash, and headache, etc. © 2020, Chinese Journal of New Drugs Co. Ltd. All right reserved.

[Predictive value of neutrophil/lymphocyte ratio on myocardial injury in severe COVID-19 patients].

Objective: To explore the predictive value of neutrophil/lymphocyte ratio (NLR) on myocardial injury in severe COVID-19 patients. Methods: In this single-center retrospective cohort study, we collected and analyzed data form 133 severe COVID-19 patients admitted to Renmin Hospital of Wuhan University (Eastern District) from January 30 to February 18, 2020. Patients were divided into myocardial injury group (n=29) and non-myocardial injury group (n=104) according the presence or absence of myocardial injury. The general information of patients was collected by electronic medical record database system. All patients were followed up for 30 days, the organ injury and/or dysfunction were monitored, the in-hospital death was compared between the two groups, and the disease progression was reevaluated and classified at 14 days after initial hospitalization. Logistic regression analysis was performed to identify risk factors of myocardial injury in severe COVID-19 patients. The ROC of NLR was calculated, and the AUC was determined to estimate the optimal cut-off value of NLR for predicting myocardial injury in severe cases of COVID-19. Results: There was statistical significance in age, respiratory frequency, systolic blood pressure, symptoms of dyspnea, previous chronic obstructive pulmonary disease, coronary heart disease history, white blood cells, neutrophils, lymphocytes, platelets, C-reactive protein, platelet counting, aspartate transaminase, albumin, total bilirubin, direct bilirubin, urea, estimated glomerular filtration rate, total cholesterol, low-density lipoprotein cholesterol, D-dimer, CD3+, CD4+, partial pressure of oxygen, partial pressure of CO2, blood oxygen saturation, other organ injury, clinical outcome and prognosis between patients with myocardial injury and without myocardial injury (all P<0.05). Multivariate logistic regression analysis showed that NLR was a risk factor for myocardial injury (OR=1.066，95%CI 1.021-1.111，P=0.033). ROC curve showed that NLR predicting AUC of myocardial injury in severe COVID-19 patients was 0.774 (95%CI 0.694-0.842), the optimal cut-off value of NLR was 5.768, with a sensitivity of 82.8%, and specificity of 69.5%. Conclusion: NLR may be used to predict myocardial injury in severe COVID-19 patients.