Controlled rate slow freezing with lyoprotective agent to retain the integrity of lipid nanovesicles during lyophilization

Lyophilization process optimization and molecular dynamics simulation of mRNA-LNPs for SARS-CoV-2 vaccine

Some studies have shown that lyophilization significantly improves the stability of mRNA-LNPs and enables long-term storage at 2-8 °C. However, there is little research on the lyophilization process of mRNA-lipid nanoparticles (LNPs). Most previous studies have used empirical lyophilization with only a single lyoprotectant, resulting in low lyophilization efficiency, often requiring 40-100 h. In the present study, an efficient lyophilization method suitable for mRNA-LNPs was designed and optimized, shortening the total length of the lyophilization process to 8-18 h, which significantly reduced energy consumption and production costs. When the mixed lyoprotectant composed of sucrose, trehalose, and mannitol was added to mRNA-LNPs, the eutectic point and collapse temperature of the system were increased. The lyophilized product had a ginger root-shaped rigid structure with large porosity, which tolerated rapid temperature increases and efficiently removed water. In addition, the lyophilized mRNA-LNPs rapidly rehydrated and had good particle size distribution, encapsulation rate, and mRNA integrity. The lyophilized mRNA-LNPs were stable at 2-8 °C, and they did not reduce immunogenicity in vivo or in vitro. Molecular dynamics simulation was used to compare the phospholipid molecular layer with the lyoprotectant in aqueous and anhydrous environments to elucidate the mechanism of lyophilization to improve the stability of mRNA-LNPs. This efficient lyophilization platform significantly improves the accessibility of mRNA-LNPs.

Linoleic Acid-Based Transferosomes for Topical Ocular Delivery of Cyclosporine A

Delivering high-molecular-weight hydrophobic peptides, such as cyclosporine A, across the corneal epithelium remains a challenge that is complicated by other physio-anatomical ocular structures that limit the ocular bioavailability of such peptides. Transferosomes have previously been used to improve transdermal permeability, and have the potential for improving the ocular corneal permeability of applicable drugs. In this study, transferosomes for the potential ocular delivery of cyclosporine A were investigated. Linoleic acid was evaluated for its effect on the stability of the transferosomes and was substituted for a portion of the cholesterol in the vesicles. Additionally, Span® 80 and Tween® 80 were evaluated for their effect on transferosome flexibility and toxicity to ocular cells as edge activators. Attenuated Total Reflectance–Fourier Transform Infrared spectroscopy (ATF-FTIR), differential scanning calorimetry (DSC), and dynamic light scattering (DLS) were used to evaluate the physicochemical parameters of the blank and the cyclosporine A-loaded transferosomes. Cyclosporine A release and corneal permeability were studied in vitro and in a New Zealand albino rabbit corneal model, respectively. The linoleic acid contributed to improved stability and the nano-size of the transferosomes. The Tween®-based formulation was preferred on the basis of a more favorable toxicity profile, as the difference in their corneal permeability was not significant. There was an initial burst release of cyclosporine A in the first 24 h that plateaued over one week. The Tween®-based formulation had a flux of 0.78 µg/cm2/h. The prepared transferosomes demonstrated biocompatibility in the ocular cell line, adequately encapsulated cyclosporine A, ensured the corneal permeability of the enclosed drug, and were stable over the period of investigation of 4 months at −20 °C.

Machine learning and clinical neurophysiology

Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.

Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories

Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition field. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identification scenario. In this setup, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.