Safety and immunogenicity of Nanocovax, a SARS-CoV-2 recombinant spike protein vaccine: Interim results of a double-blind, randomised controlled phase 1 and 2 trial.

Background: Nanocovax is a recombinant severe acute respiratory syndrome coronavirus 2 subunit vaccine composed of full-length prefusion stabilized recombinant SARS-CoV-2 spike glycoproteins (S-2P) and aluminium hydroxide adjuvant. Methods: We conducted a dose-escalation, open label trial (phase 1) and a randomized, double-blind, placebo-controlled trial (phase 2) to evaluate the safety and immunogenicity of the Nanocovax vaccine (in 25 mcg, 50 mcg, and 75 mcg doses, aluminium hydroxide adjuvanted (0·5 mg/dose) in 2-dose regime, 28 days apart (ClinicalTrials.gov number, NCT04683484). In phase 1, 60 participants received two intramuscular injection of the vaccine following dose-escalation procedure. The primary outcomes were reactogenicity and laboratory tests to evaluate the vaccine safety. In phase 2, 560 healthy adults received either vaccine doses similar in phase 1 (25 or 50 or 75 mcg S antigen in 0·5 mg aluminium per dose) or adjuvant (0·5 mg aluminium) in a ratio of 2:2:2:1. One primary outcome was the vaccine safety, including solicited adverse events for 7 day and unsolicited adverse events for 28 days after each injection as well as serious adverse event or adverse events of special interest throughout the study period. Another primary outcome was anti-S IgG antibody response (Index unit/ml). Secondary outcomes were surrogate virus neutralisation (inhibition percentage), wild-type SARS-CoV-2 neutralisation (dilution fold), and T-cell responses by intracellular staining for interferon gamma (IFNg). Anti-S IgG and neutralising antibody levels were compared with convalescent serum samples from symptomatic Covid-19 patients. Findings: For phase 1 study, no serious adverse events were observed for all 60 participants. Most adverse events were grade 1 and disappeared shortly after injection. For phase 2 study, after randomisation, 480 participants were assigned to receive the vaccine with adjuvant, and 80 participants were assigned to receive the placebo (adjuvant only). Reactogenicity was absent or mild in the majority of participants and of short duration (mean ≤3 days). Unsolicited adverse events were mild in most participants. There were no serious adverse events related to Nanocovax. Regarding the immunogenicity, Nanocovax induced robust anti-S antibody responses. In general, there humoral responses were similar among vaccine groups which reached their peaks at day 42 and declined afterward. At day 42, IgG levels of vaccine groups were 60·48 [CI95%: 51·12-71·55], 49·11 [41·26-58·46], 57·18 [48·4-67·5] compared to 7·10 [6·32-13·92] of convalescent samples. IgG levels reported here can be converted to WHO international standard binding antibody unit (BAU/ml) by multiplying them to a conversion factor of 21·8. Neutralising antibody titre of vaccine groups at day 42 were 89·2 [52·2-152·3], 80·0 [50·8-125.9] and 95·1 [63·1-143·6], compared to 55·1 [33·4-91·0] of the convalescent group. Interpretation: Up to day 90, Nanocovax was found to be safe, well tolerated, and induced robust immune responses. Funding: This work was funded by the Coalition for Epidemic Preparedness Innovations (CEPI), the Ministry of Science and Technology of Vietnam, and Nanogen Pharmaceutical Biotechnology JSC.

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). MATERIALS AND METHODS: The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009-2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively. RESULTS: Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively). DISCUSSION AND CONCLUSIONS: Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.

Development of a highly sensitive magneto-enzyme lateral flow immunoassay for dengue NS1 detection.

BACKGROUND: Dengue infection represents a global health issue of growing importance. Dengue non-structural protein 1 (NS1) plays a central role in the early detection of the disease. The most common method for NS1 detection is testing by lateral flow immunoassays (LFIAs) with varying sensitivity. In this study, we present a highly sensitive magneto-enzyme LFIA for prompt diagnosis of dengue. METHODS: We have demonstrated the development of a magneto-enzyme LFIA combining super-paramagnetic nanoparticles as labels and Biotin-Streptavidin signal amplification strategy to detect dengue NS1. Factors affecting the test performance including antibody pair, super-paramagnetic nanoparticle size, nitrocellulose membrane type, amounts of detection and capture antibodies, and amounts of Streptavidin-polyHRP were optimized. Analytical sensitivity and cross-reactivity were determined. Clinical performance of the novel assay was evaluated using a panel of 120 clinical sera. RESULTS: This newly developed assay could detect NS1 of all four serotypes of dengue virus (DENV). The limit of detection (LOD) was found to be as low as 0.25 ng ml-1 for DENV-1 and DENV-3, 0.1 ng ml-1 for DENV-2, and 1.0 ng ml-1 for DENV-4. The LOD for DENV-2 was a 50-fold improvement over the best values previously reported. There was an absence of cross-reactivity with Zika NS1, Hepatitis B virus, Hepatitis C virus, and Japanese encephalitis virus. The sensitivity and specificity of the novel assay were 100% when tested on clinical samples. CONCLUSIONS: We have successfully developed a magneto-enzyme LFIA, allowing rapid and highly sensitive detection of dengue NS1, which is essential for proper management of patients infected with DENV.