The delivery of entire functional proteins into living cells is a long-sought goal in science. Cyclic cell-penetrating peptides (cCPPs) have proven themselves to be potent delivery vehicles to carry proteins upon conjugation into the cytosol of living cells with immediate bioavailability via a non-endosomal uptake pathway. With this strategy, we pursue the cytosolic delivery of mCherry, a medium-sized fluorescent protein. Afterward, we achieve subcellular delivery of mCherry to different intracellular loci by genetic fusion of targeting peptides to the protein sequence. We show efficient transport into a membrane-bound compartment, the nucleus, as well as targeting of the actin cytoskeleton, marking one of the first ways to label actin fluorescently in genetically unmodified living cells. Furthermore, we demonstrate that only by conjugation of cCPPs via a disulfide bond, is flawless localization to the target area achieved. This finding underlines the importance of using a cCPP-based delivery vehicle that is cleaved inside cells, for the precise intracellular localization of a protein of interest.
Diethynyl phosphinates were developed as bisfunctional electrophiles for the site-selective modification of peptides, proteins and antibodies. One of their electrondeficient triple bonds reacts selectively with a thiol and positions an electrophilic moiety for a subsequent intra-or intermolecular reaction with another thiol. The obtained conjugates were found to be stable in human plasma and in the presence of small thiols. We further demonstrate that this method is suitable for the generation of functional protein conjugates for intracellular delivery. Finally, this reagent class was used to generate functional homogeneously rebridged antibodies that remain specific for their target. Their modular synthesis, thiol selectivity and conjugate stability make diethynyl phosphinates ideal candidates for protein conjugation for biological and pharmaceutical applications.
Objectives: The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping. Materials and Methods: Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8). Results: Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (−0.16) and SGB (−0.25) and a higher generalization error. Conclusions: When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using radiomics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.