Manipulation of cell-cell interactions via cell surface engineering has potential biomedical applications in tissue engineering and cell therapy. However, manipulation of the comprehensive and multiple intercellular interactions remains a challenge and missing elements. Herein, utilizing a DNA triangular prism (TP) and a branched polymer (BP) as functional modules, we fabricate tunable DNA scaffold networks on the cell surface. The responsiveness of cell-cell recognition, aggregation and dissociation could be modulated by aptamer-functionalized DNA scaffold networks with high accuracy and specificity. By regulating the DNA scaffold networks coated on the cell surface, controlled intercellular molecular transportation is achieved. Our tunable network provides a simple and extendible strategy which addresses a current need in cell surface engineering to precisely manipulate cell-cell interactions and shows promise as a general tool for controllable cell behavior.
Membrane vesicles derived from live cells show great
potential
in biological applications due to their preserved cell membrane properties.
Here, we demonstrate that cell-derived giant membrane vesicles can
be used as vectors to deliver multiple therapeutic drugs and carry
out combinational phototherapy for targeted cancer treatment. We show
that therapeutic drugs can be efficiently encapsulated into giant
membrane vesicles and delivered to target cells by membrane fusion,
resulting in synergistic photodynamic/photothermal therapy under light
irradiation. This study highlights biomimetic giant membrane vesicles
for drug delivery with potential biomedical application in cancer
therapeutics.
Engineering
cell-derived nanovesicles with active-targeting ligands
is an important strategy to enhance the targeting efficiency. However,
the enhanced binding capability to targeting cells also leads to the
binding with nontarget cells that share the same biomarkers. DNA-based
logic gate is a kind of molecular system that responds to chemical
inputs by generating output signals, and the relationship between
the input and the output is based on a certain logic. Thus, the DNA-based
logic gate could provide a new approach to improve the delivery efficiency
of the nanovesicle. In this work, we developed a DNA logic-gated module
that coupled two tumor cell-targeting factors (e.g., low pH and a
tumor cell biomarker) in a Boolean manner. Immobilization of this
module on the surface of the nanovesicle enables the nanovesicle to
sense tumor cell-targeting factors and regard these cues as inputs
AND logic gate. With the guide of DNA-based logic gate, gold carbon
dots (GCDs) encapsulated within nanovesicles were delivered into target
cells, and then the intracellular redox status variation was reflected
by fluorescence change of GCDs. Overall, we developed DNA logic-gated
nanovesicles that contract different targeting factors into a unique
tag for target cells. This facile functionalization strategy can pave
the way for constructing smart nanovesicles and would broaden their
application in the field of precision medicine and personalized treatment.
Grey wolf optimizer (GWO) is a new meta-heuristic swarm intelligence algorithm, which has shown promising performance in solving optimization problems. In order to improve the convergence speed of GWO, an alpha-guided GWO (AgGWO), in which the evolving process of the population is guided by the update direction of alpha (best solution), is proposed. However, in the AgGWO, its evolutionary guidance mechanism makes the algorithm more likely to fall into the local optimal solution and the fixed value of theta may not be suitable for all problems optimization. To overcome these shortcomings and simplify its structure, an improved AgGWO (IAgGWO) is proposed in this paper. In the IAgGWO, the update direction of alpha is used to guide the evolving process of alpha, beta (second best solution), and delta (third best solution), and A and C are the coefficient scalars instead of coefficient vectors in the original algorithm. Therefore, a mutation operator is introduced to further enhance the exploration. The advantageous performance of the IAgGWO is validated by comparisons with other four algorithms on 35 benchmark functions and the engineering problem of two-stage operational amplifier design. INDEX TERMS Optimization, heuristic algorithm, grey wolf optimizer, mutation, two stage operational amplifier.
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