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Virus-like particles (VLPs) have been proposed as an attractive tool in SARS-CoV-2 vaccine development, both as (1) a vaccine candidate with high immunogenicity and low reactogenicity and (2) a substitute for live virus in functional and neutralization assays. Though multiple SARS-CoV-2 VLP designs have already been explored in Sf9 insect cells, a key parameter ensuring VLPs are a viable platform is the VLP spike yield (i.e., spike protein content in VLP), which has largely been unreported. In this study, we show that the common strategy of producing SARS-CoV-2 VLPs by expressing spike protein in combination with the native coronavirus membrane and/or envelope protein forms VLPs, but at a critically low spike yield (~0.04–0.08 mg/L). In contrast, fusing the spike ectodomain to the influenza HA transmembrane domain and cytoplasmic tail and co-expressing M1 increased VLP spike yield to ~0.4 mg/L. More importantly, this increased yield translated to a greater VLP spike antigen density (~96 spike monomers/VLP) that more closely resembles that of native SARS-CoV-2 virus (~72–144 Spike monomers/virion). Pseudotyping further allowed for production of functional alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2), and omicron (B.1.1.529) SARS-CoV-2 VLPs that bound to the target ACE2 receptor. Finally, we demonstrated the utility of pseudotyped VLPs to test neutralizing antibody activity using a simple, acellular ELISA-based assay performed at biosafety level 1 (BSL-1). Taken together, this study highlights the advantage of pseudotyping over native SARS-CoV-2 VLP designs in achieving higher VLP spike yield and demonstrates the usefulness of pseudotyped VLPs as a surrogate for live virus in vaccine and therapeutic development against SARS-CoV-2 variants. 

Backgroud The nasal route of targeted drug administration facilitates medical management of chronic and acute onsets of various respiratory conditions such as rhinitis and sinusitis and during the initial onset phase of severe acute respiratory syndrome coronavirus 2, when the infection is still contained within the upper airway. Nevertheless, patient comfort issues that are often associated with intranasal devise usage can lead to low compliance, thereby compromising treatment efficacy. Hence, there is an urgent need to detect reproducible and user-friendly intranasal drug delivery modalities that may promote adoption compliance and yet be effective at targeted transport of drugs to the infective airway regions. Methods In this pilot study, we have collected evaluation feedback from a cohort of 13 healthy volunteers, who used an open-angle swirling effect atomizer to assess two different nasal spray administration techniques (with 0.9% saline solution), namely the vertical placement protocol (or, VP), wherein the nozzle is held vertically upright at a shallow insertion depth of 0.5 cm inside the nasal vestibule; and the shallow angle protocol (or, SA), wherein the spray axis is angled at 45° to the vertical, with a vestibular insertion depth of 1.5 cm. The VP protocol is based on current usage instructions, while the SA protocol is derived from published findings on alternate spray orientations that have been shown to enhance targeted drug delivery at posterior infection sites, e.g., the ostiomeatal complex and the nasopharynx. Results All study participants reported that the SA protocol offered a more gentle and soothing delivery experience, with less impact pressure. Additionally, 60% of participants reported that the VP technique caused painful irritation. We also numerically tracked the drug transport processes for the two spray techniques in a computed tomography-based nasal cavity reconstruction; the SA protocol registered a distinct improvement in airway penetration when compared to the VP protocol. Conclusion The participant-reported unequivocally favorable experience with the new SA protocol justifies a full-scale clinical study aimed at testing the related medication compliance parameters and the corresponding therapeutic efficacies. 

Comprehending cutaneous lupus Cutaneous lupus erythematosus (CLE) is a disfiguring skin condition that affects most patients with systemic lupus erythematosus (SLE) and can be resistant to treatment even when systemic disease is responsive. Billi et al. analyzed CLE lesions and paired normal-appearing skin biopsies, as well as circulating immune cell subsets, to better understand changes in the skin that drive CLE pathogenesis. Using single-cell RNA sequencing and spatial RNA sequencing, they identified a type I IFN–rich signature in prelesional, normal-looking skin that influenced transcription and cell-cell communication for all major skin cell types. CD16 + dendritic cells, which are associated with SLE, were also shaped by the type I IFN environment, and cells in these sites shifted toward a proinflammatory phenotype. Together, these data provide insights into transcriptional changes in the skin that contribute to CLE pathogenesis. 

Cutaneous lupus erythematosus (CLE) is a chronic inflammatory skin disease characterized by a diverse cadre of clinical presentations. CLE commonly occurs in patients with systemic lupus erythematosus (SLE), and CLE can also develop in the absence of systemic disease. Although CLE is a complex and heterogeneous disease, several studies have identified common signaling pathways, including those of type I interferons (IFNs), that play a key role in driving cutaneous inflammation across all CLE subsets. However, discriminating factors that drive different phenotypes of skin lesions remain to be determined. Thus, we sought to understand the skin-associated cellular and transcriptional differences in CLE subsets and how the different types of cutaneous inflammation relate to the presence of systemic lupus disease. In this study, we utilized two distinct cohorts comprising a total of 150 CLE lesional biopsies to compare discoid lupus erythematosus (DLE), subacute cutaneous lupus erythematosus (SCLE), and acute cutaneous lupus erythematosus (ACLE) in patients with and without associated SLE. Using an unbiased approach, we demonstrated a CLE subtype-dependent gradient of B cell enrichment in the skin, with DLE lesions harboring a more dominant skin B cell transcriptional signature and enrichment of B cells on immunostaining compared to ACLE and SCLE. Additionally, we observed a significant increase in B cell signatures in the lesional skin from patients with isolated CLE compared with similar lesions from patients with systemic lupus. This trend was driven primarily by differences in the DLE subgroup. Our work thus shows that skin-associated B cell responses distinguish CLE subtypes in patients with and without associated SLE, suggesting that B cell function in skin may be an important link between cutaneous lupus and systemic disease activity. 

Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parametervarying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load. 

Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load.