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Table · dataset · 2026

pone.0358239.t009 - <p> </p>

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<div><p>Surface charge is widely regarded as a primary determinant of nanoparticle–immune cell interactions, yet nanocarriers with similar physicochemical profiles often exhibit markedly different biological outcomes.

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Here, we show that ligand conformational diversity, quantified through an information-theoretic descriptor (), is strongly associated with a regime transition in the relationship between surface charge and cellular uptake.

Analysis of 105 gold nanoparticles from a benchmark protein corona dataset identifies a critical threshold at bits (<i>F</i> = 47.3, <i>p</i> < 10<sup>−14</sup>): below this value, zeta potential is positively associated with uptake (<i>r</i> = +0.70), whereas above it the relationship reverses (), consistent with steric shielding by flexible surface ligands. Entropy-derived descriptors improve predictive performance across multiple model classes ( up to +0.118), with gains concentrated in high-entropy regimes where physicochemical descriptors alone are insufficient.

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An analytical competition model captures this transition in a compact form (<i>R</i><sup>2</sup> = 0.550), with effective microstates marking a numerical balance point between electrostatic and steric contributions. Cross-dataset validation on 652 multi-material nanoparticles supports transferability of the descriptor framework (). We emphasize that is a constructed information-theoretic descriptor rather than a thermodynamic entropy, and that all findings are derived from retrospective analysis of in vitro datasets and not from controlled experimental manipulation.

The identified threshold therefore represents a reproducible statistical pattern and a testable hypothesis for how ligand flexibility modulates interaction regimes. This framework provides a computable basis for organizing nanocarrier design hypotheses and motivates prospective validation in more complex carrier systems.</p></div>

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figshareoai:figshare.com:article/339381119 d agoJSON v1
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concepts[field].anzsrc:group:3204mapping · figshare comvocabulary-mapper@1.0.0keywords['Immunology']
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