A recombinant protein containing influenza viral conserved epitopes and superantigen induces broad-spectrum protection (preprint)

Influenza pandemic poses public health threats annually for lacking vaccine which provides cross-protection against novel and emerging influenza viruses. Combining conserved antigens inducing cross-protective antibody response with epitopes activating cross-protective cytotoxic T-cells would offer an attractive strategy for developing universal vaccine. In this study, we constructed a recombinant protein NMHC consisting of influenza viral conserved epitopes and superantigen fragment. NMHC promoted the mature of bone marrow-derived dendritic cells and induced CD4+ T cells to differentiate into Th1, Th2 and Th17 subtypes. Mice vaccinated with NMHC produced high level of immunoglobulins which cross-bound to HA fragments from six influenza virus subtypes with high antibody titers. Anti-NMHC serum showed potent hemagglutinin inhibition effects to highly divergent group 1 (H1 subtypes) and group 2 (H3 subtype) influenza virus strains. And purified anti-NMHC antibodies could bind to multiple HAs with high affinities. NMHC vaccination effectively protected the mice from infection and lung damage challenged by two subtypes of H1N1 influenza virus. Moreover, NMHC vaccination elicited CD4+ and CD8+ T-cell responses to clear the virus from infected tissue and prevent virus spreading. In conclusion, this study provided proof of concept for triggering both B cells and T cells immune responses against multiple influenza virus infection, and NMHC may be a potential candidate of universal broad-spectrum vaccine for various influenza virus prevention and therapy.

Problem-Solving of the Discrimination Against COVID-19 Recovered

According to reports, many people who have recovered from the COVID-19 suffer from discrimination, which has caused them to feel hurt. This article believes that people who have recovered from COVID-19, like other people with disabilities, need social welfare not just economic security, protection services, community and residential life support services, but anti-discrimination, community participation, and the provision of barrier-free environments, and the vast total empathy in society. Based on the problem-solving oriented legal policy research philosophy, this article proposes: In order to protect the equal rights and interests granted by the constitution for people who have recovered from COVID-19 and to eliminate social prejudice and discrimination, it is advisable to pass the amendment of "People with Disabilities Rights Protection ActTNR or "Communicable Disease Control Act" to authorize the competent authority to list certain patients who are affected by the epidemic period of severe infectious diseases as the scope of the "disability" defined in the People with Disabilities Rights Protection Act within an appropriate period after recovery for gaining fully protection from the disability rights protection legal system.

Real-Time Neural Network Scheduling of Emergency Medical Mask Production during COVID-19 (preprint)

During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that are beyond its capability. Therefore, it is of critical importance for the manufacturer to schedule mask production tasks as efficiently as possible. However, existing scheduling methods typically require a considerable amount of computational resources and, therefore, cannot effectively cope with the surge of orders. In this paper, we propose an end-to-end neural network for scheduling real-time production tasks. The neural network takes a sequence of production tasks as inputs to predict a distribution over different schedules, employs reinforcement learning to optimize network parameters using the negative total tardiness as the reward signal, and finally produces a high-quality solution to the scheduling problem. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value (i.e., the total weighted tardiness) produced by the neural network scheduler is significantly better than those of existing constructive heuristics, and is very close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

An increased pretreatment neutrophil-to-lymphocyte ratio predicts severe novel coronavirus-infected pneumonia (preprint)

Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19).Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 at the Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of severe COVID-19.Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021; OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively).Conclusion An increased NLR can serve as an early warning sign of severe COVID-19.

Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019 (preprint)

Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.