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An advanced skin lesion segmentation and classification framework using deep learning strategies
Design and evolution of artificial enzyme with in-situ biosynthesized non-canonical amino acid
Sinapic acid accelerates diabetic wound healing by promoting angiogenesis and reducing oxidative stress
Characterization of carbapenem-resistant biofilm forming Acinetobacter baumannii isolates from clinical and surveillance samples
Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo
Abstract Lipid nanoparticles (LNPs) are the most clinically advanced nonviral RNA-delivery vehicles, though challenges remain in fully understanding how LNPs interact with biological systems. In vivo, proteins form an associated corona on LNPs that redefines their physicochemical properties and influences delivery outcomes. Despite its importance, the LNP protein corona is challenging to study owing to the technical difficulty of selectively recovering soft nanoparticles from biological samples. Herein, we develop a quantitative, label-free mass spectrometry-based proteomics approach to characterize the protein corona on LNPs. Critically, this protein corona isolation workflow avoids artifacts introduced by the presence of endogenous nanoparticles in human biofluids. We apply continuous density gradient ultracentrifugation for protein-LNP complex isolation, with mass spectrometry for protein identification normalized to protein composition in the biofluid alone. With this approach, we quantify proteins consistently enriched in the LNP corona including vitronectin, C-reactive protein, and alpha-2-macroglobulin. We explore the impact of these corona proteins on cell uptake and mRNA expression in HepG2 human liver cells, and find that, surprisingly, increased levels of cell uptake do not correlate with increased mRNA expression in part due to protein corona-induced lysosomal trafficking of LNPs. Our results underscore the need to consider the protein corona in the design of LNP-based therapeutics.
The reliability of satellite precipitation estimates during tropical cyclone Shaheen
Discrepancies in media reporting of fatal road crashes and official data in India
60 cm2 perovskite-silicon tandem solar cells with an efficiency of 28.9% by homogeneous passivation
Abstract Inverted perovskite solar cells face performance limitations due to non-radiative recombination at the perovskite surfaces in devices, including functional layers. Advanced characterization and density functional theory reveal that phosphonic acids passivate perovskite surface defects, while piperazinium chloride mitigates interface recombination by improving energy level alignment, introducing a field effect, and homogenizing the surface. Together, the quasi-Fermi level splitting of the perovskite is homogeneously increased by ca. 100 mV. This enables two-terminal perovskite-on-silicon tandems to achieve a certified open-circuit voltage of 2 V for a 1 cm² device and high performance in excess of 31%. The scalability of the passivation is furthermore demonstrated with homogeneously passivated devices reaching certified efficiencies of 28.9% for an active area of 60 cm².
Network design for bypass roads using interval valued fuzzy outerplanar graphs
Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram
Abstract Left ventricular (LV) scar is a major risk factor for sudden death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar evolves over time and needs longitudinal assessment. Currently, LV scar detection relies on late gadolinium enhancement MRI, which is limited by high cost and artifacts from implanted cardiac devices. To address this, we developed XplainScar, an explainable machine learning model that identifies LV scar using 12-lead electrocardiogram (ECG) data. XplainScar was trained and validated on retrospective data from 748 HCM patients across two centers (500 from Johns Hopkins hospital for model development, and 248 from UCSF for validation). XplainScar employs a combination of unsupervised and self-supervised representation learning to effectively predict scar presence, and discover ECG features associated with LV scar. XplainScar rapidly analyzes ECG data (< 1 min for 10 patients) and demonstrates strong predictive performance on the held-out test set, achieving an F1-score of 89%, sensitivity of 90%, specificity of 78%, and precision of 88%. By providing an effective, cost-effective, and transparent alternative to MRI, XplainScar has the potential to assist with patient care, and reduce healthcare costs related to LV scar monitoring in HCM. XplainScar is available at https://github.com/KasraNezamabadi/XplainScar.