Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

A sophisticated high-order framework nucleic acid (FNA) was engineered for the targeted delivery and responsive release of environment tolerant antisense peptide nucleic acids (asPNAs). The dendritic FNA-asPNAs system was constructed simple one-pot modular assembly and demonstrated a good synergistic effect with chemotherapy on drug resistant cancer cells.

Download full-text PDF

Source
http://dx.doi.org/10.1039/d3cc04298gDOI Listing

Publication Analysis

Top Keywords

high-order framework
8
framework nucleic
8
nucleic acid
8
antisense peptide
8
peptide nucleic
8
nucleic acids
8
nucleic
4
acid targeted-delivery
4
targeted-delivery antisense
4
acids overcome
4

Similar Publications

Implicit Runge-Kutta based sparse identification of governing equations in biologically motivated systems.

Sci Rep

September 2025

Department of Applied Mathematics, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, 51666-16471, Iran.

Identifying governing equations in physical and biological systems from datasets remains a long-standing challenge across various scientific disciplines. Common methods like sparse identification of nonlinear dynamics (SINDy) often rely on precise derivative approximations, making them sensitive to data scarcity and noise. This study presents a novel data-driven framework by integrating high order implicit Runge-Kutta methods (IRKs) with the sparse identification, termed IRK-SINDy.

View Article and Find Full Text PDF

We introduce the supervised Gromov-Wasserstein (sGW) optimal transport, an extension of Gromov-Wasserstein that incorporates potential infinity entries in the cost tensor. These infinity entries enable sGW to enforce application-induced constraints on preserving pairwise distance to a certain extent. A numerical solver is proposed for the sGW problem and the effectiveness is demonstrated in various numerical experiments.

View Article and Find Full Text PDF

The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE.

View Article and Find Full Text PDF

Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities.

Commun Eng

September 2025

Department of Disaster Mitigation for Structures, College of Civil Engineering, Tongji University, Shanghai, China.

Standard physics informed deep learning and their enhanced variants encounter challenges in addressing inverse problems characterized by extreme discontinuities and high-order parameterized differential equations due to the use of globally smooth activation functions, especially when the unknown parameters exhibit spatially distributed characteristics. Phenomena such as discontinuous loads, boundary truncations, and abrupt changes in material properties introduce singularities in the derivatives, which in turn lead to ill-conditioned information in the gradient flow. To address these limitations, here we propose an information-distilled physics-informed deep-learning framework that combines reduced-order modeling, multi-level domain decomposition, and an ill-conditioning-suppression mechanism.

View Article and Find Full Text PDF

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character.

View Article and Find Full Text PDF