Cross-variant proof predictive vaccine design based on SARS-CoV-2 spike protein using immunoinformatics approach.

Background: Coronavirus Disease (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 virus is evolving continuously. The omicron variant of SARS-CoV-2 has the highest mutation in its spike protein, thus making the presently available vaccine ineffective or reducing its efficiency. Furthermore, the majority of the vaccines are constructed using a spike protein sequence from wild-type SARS-CoV-2. This raises the possibility of the virus evolving to the point where the vaccine's effectiveness is completely lost, even after booster doses. The study aims to develop a predictive vaccine as well as the epitopes for the updating of the vaccine sequences of currently available vaccines. In this study, following the immunoinformatics approach, predictive vaccine construction was done with the help of epitopes present on spike proteins of wild-type, delta, and omicron variants that encompass the majority of variants and possible new variants that arise from the combination of circulating variants. Results: The vaccine that was constructed was stable and immunogenic. The vaccine was constructed with the help of 18 B-cell epitopes, 5 MHC class I epitopes, and 6 MHC class II epitopes. The epitope conservancy analysis suggests that the vaccine will work for the previously known variant of concern. The vaccine bound to TLR4, TLR2, B-cell receptor chains A and B, and ACE2 receptors with a z score of - 1.4, - 1.7, - 1.4, - 1.7, and - 1.4, respectively, with a cluster size of 121 highest for the ACE2 receptor and 46 lowest for B-cell receptor chain A. The C-ImmSim simulation results indicate that the vaccine is generating both humoral and cell-mediated responses at a sufficient level throughout the month upon injection of the vaccine as an antigen. Conclusion: The study's findings indicate that the vaccine was both stable and immunogenic, providing a sufficient level of immunity. Following experimental validation, the vaccine can be used, and the epitopes can be employed for therapeutic purposes such as antibody synthesis. Supplementary Information: The online version contains supplementary material available at 10.1186/s43088-023-00341-4.

SARS-CoV-2 spike and nucleocapsid proteins fail to activate human dendritic cells or γδ T cells.

γδ T cells are thought to contribute to immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the mechanisms by which they are activated by the virus are unknown. Using flow cytometry, we investigated if the two most abundant viral structural proteins, spike and nucleocapsid, can activate human γδ T cell subsets, directly or in the presence of dendritic cells (DC). Both proteins failed to induce interferon-γ production by Vδ1 or Vδ2 T cells within fresh mononuclear cells or lines of expanded γδ T cells generated from healthy donors, but the same proteins stimulated CD3+ cells from COVID-19 patients. The nucleocapsid protein stimulated interleukin-12 production by DC and downstream interferon-γ production by co-cultured Vδ1 and Vδ2 T cells, but protease digestion and use of an alternative nucleocapsid preparation indicated that this activity was due to contaminating non-protein material. Thus, SARS-CoV-2 spike and nucleocapsid proteins do not have stimulatory activity for DC or γδ T cells. We propose that γδ T cell activation in COVID-19 patients is mediated by immune recognition of viral RNA or other structural proteins by γδ T cells, or by other immune cells, such as DC, that produce γδ T cell-stimulatory ligands or cytokines.

Biosimilar Insulin Aspart Premix SAR341402 Mix 70/30 Versus Originator Insulin Aspart Mix 70/30 (NovoMix 30) in People with Diabetes: A 26-Week, Randomized, Open-Label Trial (GEMELLI M).

INTRODUCTION: This study compared the efficacy, safety, and immunogenicity of biosimilar insulin aspart premix SAR341402 Mix 70/30 (SARAsp-Mix) with European-approved insulin aspart mix 70/30 - NovoMix® 30 (NN-Mix) in people with type 1 (T1D) or type 2 diabetes (T2D). METHODS: This 26-week, open-label, phase 3 trial enrolled 402 people with T1D (n = 105) or T2D (n = 297) previously treated with premix insulin, who were randomized (1:1) to SARAsp-Mix (n = 204) or NN-Mix (n = 198). RESULTS: After 26 weeks, the least squares (LS) mean [median] change in HbA1c from baseline was similar in both treatment groups (SARAsp-Mix - 0.55% [- 0.60%]; NN-Mix - 0.64% [- 0.60%]). The LS mean difference for SARAsp-Mix versus NN-Mix was 0.08%, with the upper bound of the two-sided 95% confidence interval (- 0.139 to 0.303) slightly above the prespecified noninferiority margin of 0.3%. Noninferiority of SARAsp-Mix over NN-Mix was not demonstrated in the primary intent-to-treat analysis, primarily because of one extreme outlying value impacted by the COVID-19 pandemic in the SARAsp-Mix group. Noninferiority was achieved in all secondary analyses, including prespecified per-protocol supportive and COVID-19 sensitivity analyses, as well as post hoc sensitivity analyses. Other efficacy endpoints, insulin dosages, anti-insulin aspart antibody response, hypoglycemia, and adverse events were similar between groups. CONCLUSIONS: The totality of evidence indicates that SARAsp-Mix provides effective glycemic control with a similar safety and immunogenicity profile to NN-Mix in people with diabetes treated for 26 weeks. TRIAL REGISTRATION: EudraCT number 2017-000092-84.

Fog-centric IoT based smart healthcare support service for monitoring and controlling an epidemic of Swine Flu virus

Disease detection is a time-consuming and essential task in the medical diagnosis system. Machine learning plays a vital role in predicting and identifying diseases at various stages. It is a very random and timely method for analyzing disease using clinical and laboratory signs and assists medical representatives in developing a more effective diagnostic strategy for such diseases. For example, swine flu, a contagious illness caused by influenza viruses, including the H1N1 virus, infects the respiratory tract of pigs, causing a barking cough, decreased appetite, nasal secretions, and uncontrollable behaviour. Cloud computing and the Internet of things help the medical sector by processing health information in ultra-low delay so that effective decisions can be taken timely. In this paper, a fog-centric IoT-based smart healthcare support service for monitoring and controlling the Swine Flu virus epidemic is proposed. The proposed framework utilizes the concept of fog computing for delay-sensitive applications. Furthermore, a hybrid classifier is used to classify the swine flu patient at an early stage and generate alerts to the health officials and patients' guardians. In the experimental setup, the iFogSim simulator is used to mimic the IoT devices and fog nodes for evaluating various parameters such as accuracy, energy, and Latency, whereas WEKA is used for developing a hybrid classifier. Results demonstrate the benefits of combining fog and cloud computing services to achieve higher network bandwidth reliability, a higher level of operation, and a shorter response time while generating real-time notifications, as compared to an existing cloud-only model.

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.

Productivity of rural women through entrepreneurship intervention

The review study deals with various data for developing the entrepreneurial interventions for the rural women especially in India. The world is leading for economic developments still there are many women who haven't got the same literacy as men as well as are far behind in labor participation and earnings. This study provides the insight of why women empowerment is the need of the hour, how SHGs and Micro enterprises have helped the women to develop globally over the time and what are the other needs that need to be tackled for smooth developments in near future. The study focuses on the facts that gender discrimination has been driven down due to women empowerment except for the poor and unprivileged households, where patriarchy still exists. Also it is shown in the study that how the empowerment of women provides them considerable status and decision making power in the family and influences their opportunities as equal to men counterparts. The opportunities in disguise and futuristic potentials have been briefly discussed that are going to affect women's choices and abilities post Covid-19 pandemic.