Adsorption of SARS CoV-2 spike proteins on various functionalized surfaces correlates with the high transmissibility of Delta and Omicron variants

Efficacy of adjuvant-associated COVID-19 vaccines against SARS-CoV-2 variants of concern in randomized controlled trials: A systematic review and meta-analysis

BACKGROUND

Adjuvants may enhance the efficacy of vaccines. however, the efficacy of adjuvant-associated COVID-19 vaccines (ACVs) remains unclear since the emergence of the COVID-19 pandemic. This study aimed to address this gap by conducting a systematic review and meta-analysis of the efficacy of ACVs against Severe Acute Respiratory Syndrome Coronavirus 2 CoV (SARS-CoV-2) variants of concern (VOC).

METHODS

A systematic search was conducted of randomized controlled trials (RCTs) evaluating the vaccine efficacy (VE) of ACVs against VOC (alpha, beta, gamma, delta, or Omicron), up to May 27, 2023. The DerSimonian-Laird random-effects model was used to assess VE with 95% confidence intervals (CI) through meta-analysis. Cochrane Risk of Bias tools were used to assess the risk of bias in RCTs.

RESULTS

Eight RCTs with 113,202 participants were included in the analysis, which incorporated 4 ACVs [Matrix-M (NVX-CoV2373), Alum (BBV152), CpG-1018/Alum (SCB-2019), and AS03 (CoVLP]). The pooled efficacy of full vaccination with ACVs against VOC was 88.0% (95% CI: 83.0-91.5). Full vaccination was effective against Alpha, Beta, Delta, and Gamma variants, with VE values of 93.66% (95% CI: 86.5-100.74), 64.70% (95% CI: 41.87-87.54), 75.95% (95% CI: 67.9-83.99), and 91.26% (95% CI: 84.35-98.17), respectively. Currently, there is a lack of RCT evidence regarding the efficacy of ACVs against the Omicron variant.

CONCLUSION

In this meta-analysis, it should be that full vaccination with ACVs has high efficacy against Alpha or Gamma variants and moderate efficacy against Beta and Delta variants. Notably, with the exception of the aluminum-adjuvanted vaccine, the other ACVs had moderate to high efficacy against the SARS-CoV-2 variant. This raises concerns about the effectiveness of ACVs booster vaccinations against Omicron.

A high-frequency QCM biosensing platform for label-free detection of the SARS-CoV-2 spike receptor-binding domain: an aptasensor and an immunosensor

Herein, high-frequency quartz crystal microbalance biosensing platforms were constructed using an aptamer and antibody as bioreceptors for fast and label-free detection of the SARS-CoV-2 RBD.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

SARS-CoV-2 Spike Protein (RBD) Subunit Adsorption at Abiotic Surfaces and Corona Formation at Polymer Particles

The adsorption kinetics of the SARS-CoV-2 spike protein subunit with the receptor binding domain at abiotic surfaces was investigated. A combination of sensitive methods was used such as atomic force microscopy yielding a molecular resolution, a quartz microbalance, and optical waveguide lightmode spectroscopy. The two latter methods yielded in situ information about the protein adsorption kinetics under flow conditions. It was established that at pH 3.5-4 the protein adsorbed on mica and silica surfaces in the form of compact quasi-spherical aggregates with an average size of 14 nm. The maximum coverage of the layers was equal to 3 and 1 mg m^-2 at pH 4 and 7.4, respectively. The experimental data were successfully interpreted in terms of theoretical results derived from modeling. The experiments performed for flat substrates were complemented by investigations of the protein corona formation at polymer particles carried out using in situ laser Doppler velocimetry technique. In this way, the zeta potential of the protein layers was acquired as a function of the coverage. Applying the electrokinetic model, these primary data were converted to the dependence of the subunit zeta potential on pH. It was shown that a complete acid-base characteristic of the layer can be acquired only using nanomolar quantities of the protein.

A comprehensive analysis of the efficacy and effectiveness of COVID-19 vaccines

It is urgently needed to update the comprehensive analysis about the efficacy or effectiveness of COVID-19 vaccines especially during the COVID-19 pandemic caused by SARS-CoV-2 Delta and Omicron variants. In general, the current COVID-19 vaccines showed a cumulative efficacy of 66.4%, 79.7%, and 93.6% to prevent SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19, respectively, but could not prevent the asymptomatic infection of SARS-CoV-2. Furthermore, the current COVID-19 vaccines could effectively prevent COVID-19 caused by the Delta variant although the incidence of breakthrough infection of the SARS-CoV-2 Delta variant increased when the intervals post full vaccination extended, suggesting the waning effectiveness of COVID-19 vaccines. In addition, one-dose booster immunization showed an effectiveness of 74.5% to prevent COVID-19 caused by the Delta variant. However, current COVID-19 vaccines could not prevent the infection of Omicron sub-lineage BA.1.1.529 and had about 50% effectiveness to prevent COVID-19 caused by Omicron sub-lineage BA.1.1.529. Furthermore, the effectiveness was 87.6% and 90.1% to prevent severe COVID-19 and COVID-19-related death caused by Omicron sub-lineage BA.2, respectively, while one-dose booster immunization could enhance the effectiveness of COVID-19 vaccines to prevent the infection and COVID-19 caused by Omicron sub-lineage BA.1.1.529 and sub-lineage BA.2. Two-dose booster immunization showed an increased effectiveness of 81.8% against severe COVID-19 caused by the Omicron sub-lineage BA.1.1.529 variant compared with one-dose booster immunization. The effectiveness of the booster immunization with RNA-based vaccine BNT162b2 or mRNA-1273 was over 75% against severe COVID-19 more than 17 weeks after booster immunization whereas the heterogenous booster immunization showed better effectiveness than homologous booster immunization. In summary, the current COVID-19 vaccines could effectively protect COVID-19 caused by Delta and Omicron variants but was less effective against Omicron variant infection. One-dose booster immunization could enhance protection capability, and two-dose booster immunization could provide additional protection against severe COVID-19.