Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy.

Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

ACE2-Decorated Virus-Like Particles Effectively Block SARS-CoV-2 Infection.

Purpose: Over the past three years, extensive research has been dedicated to understanding and combating COVID-19. Targeting the interaction between the SARS-CoV-2 Spike protein and the ACE2 receptor has emerged as a promising therapeutic strategy against SARS-CoV-2. This study aimed to develop ACE2-coated virus-like particles (ACE2-VLPs), which can be utilized to prevent viral entry into host cells and efficiently neutralize the virus. Methods: Virus-like particles were generated through the utilization of a packaging plasmid in conjunction with a plasmid containing the ACE2 envelope sequence. Subsequently, ACE2-VLPs and ACE2-EVs were purified via ultracentrifugation. The quantification of VLPs was validated through multiple methods, including Nanosight 3000, TEM imaging, and Western blot analysis. Various packaging systems were explored to optimize the ACE2-VLP configuration for enhanced neutralization capabilities. The evaluation of neutralization effectiveness was conducted using pseudoviruses bearing different spike protein variants. Furthermore, the study assessed the neutralization potential against the Omicron BA.1 variant in Vero E6 cells. Results: ACE2-VLPs showed a high neutralization capacity even at low doses and demonstrated superior efficacy in in vitro pseudoviral assays compared to extracellular vesicles carrying ACE2. ACE2-VLPs remained stable under various environmental temperatures and effectively blocked all tested variants of concern in vitro. Notably, they exhibited significant neutralization against Omicron BA.1 variant in Vero E6 cells. Given their superior efficacy compared to extracellular vesicles and proven success against live virus, ACE2-VLPs stand out as crucial candidates for treating SARS-CoV-2 infections. Conclusion: This novel therapeutic approach of coating VLPs with receptor particles provides a proof-of-concept for designing effective neutralization strategies for other viral diseases in the future.

Protein Scaffold-Based Multimerization of Soluble ACE2 Efficiently Blocks SARS-CoV-2 Infection In Vitro and In Vivo.

Soluble ACE2 (sACE2) decoys are promising agents to inhibit SARS-CoV-2, as their efficiency is unlikely to be affected by escape mutations. However, their success is limited by their relatively poor potency. To address this challenge, multimeric sACE2 consisting of SunTag or MoonTag systems is developed. These systems are extremely effective in neutralizing SARS-CoV-2 in pseudoviral systems and in clinical isolates, perform better than the dimeric or trimeric sACE2, and exhibit greater than 100-fold neutralization efficiency, compared to monomeric sACE2. SunTag or MoonTag fused to a more potent sACE2 (v1) achieves a sub-nanomolar IC50 , comparable with clinical monoclonal antibodies. Pseudoviruses bearing mutations for variants of concern, including delta and omicron, are also neutralized efficiently with multimeric sACE2. Finally, therapeutic treatment of sACE2(v1)-MoonTag provides protection against SARS-CoV-2 infection in an in vivo mouse model. Therefore, highly potent multimeric sACE2 may offer a promising treatment approach against SARS-CoV-2 infections.

Dental age estimation by different methods in patients with amelogenesis imperfecta.

Amelogenesis imperfecta (AI) is one of the developmental enamel defects. In patients with AI, as well as enamel defects, some dental anomalies are encountered and may affect tooth development. For children whose enamel structure is not normal, whether an accurate result can be obtained by dental age determination methods is not clear. The aims of this study are to determine the dental age for the assessment of tooth development in patients with AI by 3 different methods, to decide a method that will present the most accurate result to the chronological age, and to build a basis in terms of postmortem identification. The records of patients who were referred to the Department of Pediatric Dentistry between the years of 1999 and 2018, and diagnosed with AI were reviewed. At the age of 6-15 years, 58 patients (33 males-25 females) (Group I) with AI were determined. A total of 116 healthy individuals (66 males-50 females) (Group II) who were age and gender matched with the AI group were selected as the control group. Chronological age was calculated, and Nolla's, Demirjian's and Haavikko's methods were used to estimate dental age. Chronological and dental ages were compared according to the group and gender. The accuracy of three methods was also evaluated. There was no statistical significant differences for the tooth development in terms of group and gender. Dental age was over-estimated by Demirjian's method and under-estimated by Nolla's and Haavikko's methods. Haavikko's and Nolla's methods were more accurate in the dental age estimation. AI did not present an impact on the dental development. By using particularly Haavikko's method, the possible margin of error in evaluations of dental development can be reduced. Similar studies comprising other defects affecting the tooth structure and morphology are also necessary.