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I. Sapaev

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Review Open access Sep 2026

Decellularized extracellular matrix bioinks through a polymer engineering lens: tissue-source-dependent macromolecular architecture, crosslinking chemistry, rheological behavior, and 3D bioprinting performance.

Decellularized extracellular matrix (dECM) bioinks are widely regarded as one of the more compositionally faithful hydrogel platforms for three-dimensional (3D) bioprinting in tissue engineering. However, the field still lacks a unified polymer engineering framework that links tissue-specific macromolecular architecture to rheological behavior and printing performance. This review synthesizes recent experimental studies on dECM bioinks derived from six tissue sources: cardiac, cartilage, liver, adipose, dermal, and neural tissues. These systems are analyzed through polymer network design, focusing on matrix composition, crosslinking chemistry, rheological properties, and printability. Tissue origin defines the polymeric composition of dECM and therefore influences the crosslinking strategies required to produce printable constructs with relevant mechanical properties. Across several composite systems, dECM incorporation creates a rheological paradox: storage modulus and viscosity may decrease compared with single-component matrices, likely because bioactive ECM macromolecules interfere with pre-formed polymer networks. Methacrylation partially resolves this limitation by separating mechanical tunability from native compositional constraints, enabling concentration-dependent stiffness modulation across approximately two orders of magnitude. Decellularization methodology also emerges as a critical, yet often underestimated, determinant of bioink performance. A polymer engineering perspective provides a more mechanistically useful basis for dECM bioink design than biological fidelity alone. Matching tissue-specific matrix composition, crosslinking architecture, rheological behavior, and printing parameters is essential for advancing dECM-based constructs toward clinical translation.

Y. Q. Almajidi, Ho Soonmin, Mirza R. Baig et al. · 0 citations
Review Open access Aug 2026

Machine learning-enhanced fluorescence signal processing of carbon quantum dots for high-accuracy chemical sensing

A comprehensive analysis of ML-driven methodologies for denoising, spectral decomposition, feature extraction, and high-accuracy classification in CQD fluorescence systems shows how ML enables ultra-low-level analyte detection, interpretable photophysical modeling, and real-time intelligent sensing across chemical and biological environments.

B. T. Sayed, Maharshi B. Shukla, Sumit Sharma et al. · 0 citations
#protein folding Open access Aug 2026

SIRT1 deacetylates GAPDH to drive microglial glycolysis and neuroinflammation.

SIRT1–GAPDH signaling represents a post-translational axis linking sirtuin activity directly to glycolytic enzyme function, distinct from SIRT1's traditional transcriptional roles and serving as a viable molecular checkpoint in microglial immunometabolism.

Chou-Yi Hsu, I. Sapaev, O. Nematov et al. · 0 citations

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