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The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global health concern. The development of vaccines with high immunogenicity and safety is crucial for controlling the global COVID-19 pandemic and preventing further illness and fatalities. Here, we report the development of a SARS-CoV-2 vaccine candidate, Nanocovax, based on recombinant protein production of the extracellular (soluble) portion of the spike (S) protein of SARS-CoV-2. The results showed that Nanocovax induced high levels of S protein-specific IgG and neutralizing antibodies in three animal models: BALB/c mouse, Syrian hamster, and a non-human primate ( Macaca leonina ). In addition, a viral challenge study using the hamster model showed that Nanocovax protected the upper respiratory tract from SARS-CoV-2 infection. Nanocovax did not induce any adverse effects in mice ( Mus musculus var. albino) and rats ( Rattus norvegicus ). These preclinical results indicate that Nanocovax is safe and effective.

The Coronavirus disease-2019 (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), has become a dire global health concern. The development of vaccines with high immunogenicity and safety is crucial for control of the global COVID-19 pandemic and prevention of further illness and fatalities. Here, we report development of SARS-CoV-2 vaccine candidate, Nanocovax, based on recombinant protein production of the extracellular (soluble) portion of the S protein of SARS-CoV-2. The results showed that Nanocovax induced high levels of S protein-specific IgG, as well neutralizing antibody in three animal models including Balb/C mice, Syrian hamsters, and non-human primate (Macaca leonina). In addition, the viral challenge study using the hamster model showed that Nanocovax protected the upper respiratory tract from SARS-CoV-2 infection. No adverse effects were induced by Nanocovax in swiss mice (Musmusculus var. Albino), Rats (Rattus norvegicus), and New Zealand rabbits. These pre-clinical results indicated that Nanocovax is safe and effective Competing Interest Statement The authors have declared no competing interest.

To evaluate the prevalence of post-traumatic stress disorder (PTSD) and other psychological disturbances in the Vietnamese healthcare workers (HCWs) at COVID-19 field hospitals. A cross-sectional study was conducted using the Impact of Event Scale-Revised (IES-R) to measure PTSD and the Depression Anxiety Stress scale (DASS) to measure other psychological disturbances. The anxiety about COVID-19 was evaluated by the fear of COVID-19 (FOC) scale. A self-developed questionnaire was used to assess work conditions and HCW's major concerns and preparedness. Ordinal logistic regression was used to identify factors associated with the severity of PTSD. A structural modeling equation (SEM) model was fitted to examine the correlation between PTSD and other psychological disturbances. A total of 542 HCWs participated in this study. The prevalence of PTSD was 21.2%, most cases were mild. In the ordinal logistic regression analysis, a history of mental illness, poor preparedness, working in a condition with poor resources, a greater number of concerns, and greater fear of COVID-19 were independently associated with higher severity of PTSD. The prevalence of depression, anxiety, and stress was 46.8%, 38.3%, and 60.2, respectively. In the SEM model, PTSD and psychological disturbances had a strong correlation (standardized covariance 0.86). The prevalence of PTSD and other psychological disturbances was alarmingly high among HCWs who worked at COVID-19 field hospitals. The reported associated factors can be useful for policymakers and health authorities in the preparation for future pandemics.

Background Tuberculosis is a severe disease, not only due to its lethality but also to a significant morbidity occurring in people living with HIV (PLWH). If factors associated to mortality, severe morbidity and unsuccessful treatment related to the host are well identified in PLWH, there is scarce knowledge on factors related to the disease itself such as bacillary load, extent of lung involvement and disease dissemination to other organs. We sought to assess whether tuberculosis-related factors were associated with key patient outcomes in PLWH using data from an international clinical trial. Methods We conducted a secondary analysis of the ANRS 12300 Reflate TB2, an international phase III open-label randomized trial that assessed different antiretroviral regimens in PLWH treated for tuberculosis. We evaluated whether bacillary load (smear positivity grade), extent of lung involvement (cavitation on chest x-ray) and disease dissemination (urine LAM positivity) were associated with mortality using Cox proportional hazard models, and to severe morbidity and unsuccessful tuberculosis treatment using logistic regressions. Results Of 457 participants included in this study, 90 (20.4%) had grade 2 + or 3 + smear positivity, 39 (10.8%) had cavitation on chest X-ray, and 147 (32.2%) had a positive urinary LAM. Overall, 19 (4.2%) participants died, 113 (24.7%) presented severe morbidity, and 33 (7.2%) had unsuccessful tuberculosis treatment. Factors that remained independently associated with mortality were cavitation on chest x-ray (aHR = 7.92, 95% CI, 1.74–35.94, p  = .0073) and LAM positivity (aHR = 5.53, 95% CI, 1.09–28.06, p  = .0389). The only factor that remained significantly associated with severe morbidity was LAM positivity (aOR = 2.04, 95% CI, 1.06–3.92, p  = .0323). No factor remained significantly associated with unsuccessful tuberculosis treatment. Conclusions In PLWH with tuberculosis enrolled in a trial, tuberculosis disease characteristics related to disease severity were cavitation on chest x-ray and urine LAM positivity. Early identification of these factors could help improve the management of PLWH with tuberculosis and improve their survival.

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector.