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  1. Free, publicly-accessible full text available June 1, 2024
  2. Nanoparticle (NP)-based therapeutics have ushered in a new era in translational medicine. However, despite the clinical success of NP technology, it is not well-understood how NPs fundamentally change in biological environments. When introduced into physiological fluids, NPs are coated by proteins, forming a protein corona (PC). The PC has the potential to endow NPs with a new identity and alter their bioactivity, stability, and destination. Additionally, the conformation of proteins is sensitive to their physical and chemical surroundings. Therefore, biological factors and protein–NP-interactions can induce changes in the conformation and orientation of proteins in vivo . Since the function of a protein is closely connected to its folded structure, slight differences in the surrounding environment as well as the surface characteristics of the NP materials may cause proteins to lose or gain a function. As a result, this can alter the downstream functionality of the NPs. This review introduces the main biological factors affecting the conformation of proteins associated with the PC. Then, four types of NPs with extensive utility in biomedical applications are described in greater detail, focusing on the conformation and orientation of adsorbed proteins. This is followed by a discussion on the instances in which the conformation of adsorbed proteins can be leveraged for therapeutic purposes, such as controlling protein conformation in assembled matrices in tissue, as well as controlling the PC conformation for modulating immune responses. The review concludes with a perspective on the remaining challenges and unexplored areas at the interface of PC and NP research. 
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  3. Summary A microencapsulated, cell-based IL2 cytokine factory was recently developed, and the safety and efficacy of this platform in a mouse model of mesothelioma were demonstrated. This platform has the potential to overcome current challenges in the delivery of therapeutic cytokines for cancer immunotherapy. See related article by Nash et al., p. 5121 
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  4. Bruno, Vincent (Ed.)
    ABSTRACT Diaporthe ilicicola is a newly described fungal species that is associated with latent fruit rot in deciduous holly. This announcement provides a whole-genome assembly and annotation for this plant pathogen, which will inform research on its parasitism and identification of gene clusters involved in the production of bioactive metabolites. 
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  5. Abstract

    Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies using images is nontrivial, resulting in a subset of SNe with mismatched hosts and thus incorrect redshifts. We evaluate the host galaxy mismatch rate and resulting biases on cosmological parameters from simulations modeled after the Dark Energy Survey 5 Yr (DES-SN5YR) photometric sample. For both DES-SN5YR data and simulations, we employ the directional light radius method for host galaxy matching. In our SN Ia simulations, we find that 1.7% of SNe are matched to the wrong host galaxy, with redshift differences between the true and matched hosts of up to 0.6. Using our analysis pipeline, we determine the shift in the dark energy equation of state parameter (Δw) due to including SNe with incorrect host galaxy matches. For SN Ia–only simulations, we find Δw= 0.0013 ± 0.0026 with constraints from the cosmic microwave background. Including core-collapse SNe and peculiar SNe Ia in the simulation, we find that Δwranges from 0.0009 to 0.0032, depending on the photometric classifier used. This bias is an order of magnitude smaller than the expected total uncertainty onwfrom the DES-SN5YR sample of ∼0.03. We conclude that the bias onwfrom host galaxy mismatch is much smaller than the uncertainties expected from the DES-SN5YR sample, but we encourage further studies to reduce this bias through better host-matching algorithms or selection cuts.

     
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  6. null (Ed.)
    Abstract Additive manufacturing (AM) techniques, such as fused deposition modeling (FDM), are able to fabricate physical components from three-dimensional (3D) digital models through the sequential deposition of material onto a print bed in a layer-by-layer fashion. In FDM and many other AM techniques, it is critical that the part adheres to the bed during printing. After printing, however, excessive bed adhesion can lead to part damage or prevent automated part removal. In this work, we validate a novel testing method that quickly and cheaply evaluates bed adhesion without constraints on part geometry. Using this method, we study the effect of bed temperature on the peak removal force for polylactic acid (PLA) parts printed on bare borosilicate glass and polyimide (PI)-coated beds. In addition to validating conventional wisdom that bed adhesion is maximized between 60 and 70 °C (140 and 158 °F), we observe that cooling the bed below 40 °C (104 °F), as is commonly done to facilitate part removal, has minimal additional benefit. Counterintuitively, we find that heating the bed after printing is often a more efficient process for facile part removal. In addition to introducing a general method for measuring and optimizing bed adhesion via bed temperature modulation, these results can be used to accelerate the production and testing of AM components in printer farms and autonomous research systems. 
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