Gaurav Arya

The rational design of mesoscale superlattices with prescribed architectures remains a fundamental challenge in materials science. While self-assembly of patchy colloidal particles offers a powerful route to such structures, the combinatorial complexity linking patch number, placement, and interaction properties to emergent assembly architecture has hindered systematic inverse design. Here, we present a discrete neural-adjoint framework that enables direct optimization of building-block design for target superlattice formation. Leveraging a Gumbel–Softmax relaxation, our approach renders discrete patch configurations differentiable, allowing efficient gradient-based exploration of an otherwise intractable design space. Applied to a model system of self-assembling two-dimensional tiles, our method discovers patch designs that robustly assemble into complex superlattices, including multiple Archimedean tilings as well as previously unrealized Pythagorean and gyrated truncated hexagonal tilings. These results establish differentiable inverse design as a powerful approach for programming self-assembly, opening new avenues for the discovery of architected materials with tailored structure and function.

The incorporation of synthetic polymers into biomolecular materials provides a powerful strategy to enhance their properties. We recently showed that the interstitial spaces of highly solvated mesoporous ferritin crystals could be infiltrated with acrylate (Ac) and acrylamide (Am) monomers, which are subsequently polymerized in crystallo to yield a new class of hybrid materials termed Polymer-Integrated Protein Crystals (PIX). Our earlier studies had shown that ferritin-PIX displayed remarkable properties such as reversible expansion and contraction without losing crystalline order, efficient self-healing, and the ability to encapsulate and release large biomolecular cargo. However, the structure of the polyacrylate- co -acrylamide (p(Ac–Am)) polymer matrix, its distribution within the protein lattice, and the molecular nature of the protein–polymer interactions that ultimately engender the emergent properties of ferritin-PIX have remained unknown. Here, we combine small-angle neutron and X-ray scattering and analytical measurements with extensive all-atom and coarse-grained molecular dynamics simulations to examine the structure and dynamics of the polymer network within the crystalline framework of ferritin-PIX. Our results reveal an extensive and multivariate set of noncovalent interactions between the ferritin surfaces and p(Ac–Am) chains that sustain the structural coherence of the crystalline lattice while accommodating large-scale motions. Guided by these insights, we have demonstrated that changes in the chemical compositions of ferritin and the polymer matrix can be used to predictably control the structural dynamics of ferritin-PIX. Our increased molecular-level understanding and engineering of the polymer–protein interface in ferritin-PIX provide an important step toward the generalization of the PIX concept to other protein crystals and polymer compositions.

The rational design of mesoscale superlattices with prescribed architectures remains a fundamental challenge in materials science. While self-assembly of patchy colloidal particles offers a powerful route to such structures, the combinatorial complexity linking patch number, placement, and interaction properties to emergent assembly architecture has hindered systematic inverse design. Here, we present a discrete neural-adjoint framework that enables direct optimization of building-block design for target superlattice formation. Leveraging a Gumbel–Softmax relaxation, our approach renders discrete patch configurations differentiable, allowing efficient gradient-based exploration of an otherwise intractable design space. Applied to a model system of self-assembling two-dimensional tiles, our method discovers patch designs that robustly assemble into complex superlattices, including multiple Archimedean tilings as well as previously unrealized Pythagorean and gyrated truncated hexagonal tilings. These results establish differentiable inverse design as a powerful approach for programming self-assembly, opening new avenues for the discovery of architected materials with tailored structure and function.

Understanding how energetic interactions between faceted nanoparticles (NPs) drive their self-assembly into higher-order architectures is critical for controlling various properties of NP assemblies. Here, we integrate analytical potentials that capture orientation-dependent van der Waals interactions into a Monte Carlo simulation framework for fast and accurate modeling of NP self-assembly. By implementing virtual cluster moves in the framework, we overcome sampling limitations and account for size-dependent diffusion of clusters. Simulations using the analytical potentials are orders of magnitude faster than atomistic and coarse-grained models while producing correct assembly morphologies. Phase behavior calculations of faceted NPs with weak and strong interparticle attractions show that attraction enhances ordering and shifts isotropic-to-semiordered transitions to lower volume fractions, while semiordered-to-crystalline transitions remain largely entropy driven. Overall, this work highlights the importance of enthalpic interactions and the advantages of using analytical potentials for efficient simulations of faceted NPs.

Self-assembly of nanoscale building blocks with programmable geometries and interactions offers a powerful route to engineer materials that mimic the complexity of biological structures. DNA origami provides an exceptional platform for this purpose, enabling precise control over subunit shape, binding angles, and interaction specificity. Here we present a modular DNA origami design approach to address the challenges of assembling geometrically complex nanoscale structures, including those with nonuniform curvatures. This approach features a core structure that completely conserves the scaffold routing across different designs and preserves more than 70% of the DNA staples between designs, dramatically reducing both cost and effort, while enabling precise and independent programming of subunit interactions and binding angles through adjustable overhang lengths and sequences. Using cryogenic electron microscopy, gel electrophoresis, and coarse-grained simulations, we validate a set of robust design rules and demonstrate the assembly of diverse self-limiting structures, including anisotropic shells, a T = 13 icosahedral shell, and a toroid with globally varying curvature. This modular strategy provides an efficient and cost-effective framework for the synthetic fabrication of complex nanostructures.

DNA origami placement (DOP) uses lithographically generated surface patterns to bind and align DNA origami. Since DNA origami are excellent breadboards for arranging functional nanomaterials with few-nanometer precision, DOP serves as a promising pathway toward integrating nanomaterials within advanced devices. However, progress in improving placement yield and alignment precision has been hampered by limited understanding of how the interactions between the heterogeneous surface and DNA origami drive DOP. To address this challenge, we studied DOP on nanografting-patterned self-assembled monolayers with tailored surface functionality and topography. Atomic force microscopy and modeling revealed that compared to existing hydrophobic passivating backgrounds, a charge-neutral hydrophilic background with tailored topography substantially improves yield, precision, and speed. These improvements arise from increased energetic penalty for misalignment, coupled with a dramatically enhanced surface-diffusion mediated pathway. These mechanistic insights provide a framework for rationally improving DOP to meet the stringent requirements for new nanoelectronic and nanophotonic architectures.

Understanding how energetic interactions between faceted nanoparticles (NPs) drive their self-assembly into higher-order architectures is a key area of investigation, given the valuable optical, catalytic, and plasmonic properties these assemblies exhibit. Re- cently, we devised an approach to derive analytical potentials that accurately capture the orientation-dependent van der Waals interactions between faceted NPs. In this work, we incorporate these analytical potentials into a Monte Carlo simulation frame- work to enable fast yet accurate simulation of NP self-assembly. Through the imple- mentation of virtual cluster moves in this framework, we mitigate unphysical energy traps and account for size-dependent diffusion of particles and their clusters. We find that the analytical potentials allow us to simulate NP assembly orders of magnitude faster than atomistic and coarse-grained models while yielding assembly morphologies closely resembling those from atomistic simulations. In contrast, coarse-grained mod- els of the NPs fail to capture the expected morphologies. Additionally, we explore the phase behavior of faceted NPs of varying shapes under weak and strong interactions, marking one of the first attempts at studying the phase diagram of attractive faceted particles. Our analysis reveals that, compared to hard-core potentials, attractive inter- actions enhance the ordering of particles in their assemblies. Specifically, they shift the transitions between isotropic and semiordered phases to lower volume fractions, but have little effect on the transition between semiordered and crystalline phases, which are primarily driven by entropy. Overall, our results offer new insights into the role of interparticle attraction in the phase behavior of faceted NPs, and emphasize the ad- vantages of our analytical potential over traditional hard-core potentials by accounting for enthalpic interactions between NPs.

Structural DNA nanotechnology, a research field in which scientists use DNA as the primary material to make designer nanostructures, has experienced rapid growth in the past few decades. The continuous development of the field has produced a rich repository of impressive, complex nanostructures for applications in materials science, biological research, and therapeutics. The unprecedented programmability of DNA nanostructures, particularly DNA origami, combined with the biocompatibility and rich functionality of DNA molecules make them attractive candidates for building nanocarriers for cellular delivery. While the initial research toward this direction focused on the delivery of small molecule drugs and short nucleic acids, emerging efforts in the last two years have expanded to gene delivery by leveraging the capacity of DNA origami to fold gene sequences into compact structures amenable for cell delivery. Here, we review this exciting research direction and provide our perspective on the challenges and opportunities in this field.

We describe a modular design approach for creating versatile DNA origami subunits that can target diverse self-assembled structures. The subunit consists of a constant "core module" with variable "bond modules" and "angle modules" added to its exterior, controlling interaction specificity, strength, and structural geometry. The design features flexible joints between subunits, implemented by using single-stranded angle modules, whose mechanical properties and possible conformations are characterized by cryogenic electron microscopy and coarse-grained molecular modeling. We demonstrate the design's versatility through the assembly of structures with different Gaussian curvature, including sheets, spherical shells, and tubes. Our findings suggest that incorporating a judicious amount of flexibility in the bonds provides error tolerance in design and fabrication while maintaining target fidelity. Furthermore, off-target assemblies potentially introduced by flexibility can be counterbalanced by increasing the number of distinct bonds. This approach enables precise targeting of specific structural binding angles across a broad range of configurations by eliminating unfavorable interactions.

Certain viruses such as tailed bacteriophages and herpes simplex virus package double-stranded DNA into empty procapsids via powerful, ring-shaped molecular motors. High-resolution structures and force measurements on the DNA packaging motor of bacteriophage Φ29 revealed that its five ATPase subunits coordinate ATP hydrolysis with each other to maintain the proper cyclic sequence of DNA translocation steps about the ring. Here, we explore how the Φ29 motor regulates translocation by timing key events, namely ATP binding/hydrolysis and DNA gripping, through trans-subunit interactions. We used subunit dimers bound to DNA as our model system, a minimal system that still captures the conformation and trans-subunit interactions of the full pentameric motor complex. Molecular dynamics simulations of all-ATP and mixed ATP-ADP dimers revealed that the nucleotide occupancy of one subunit strongly affects the ability to hydrolyze ATP in the adjacent subunit by altering the free energy landscape of its catalytic glutamate approaching the gamma phosphate of ATP. Specifically, one ATP-bound subunit donates residues in trans that sterically block the neighboring subunit's catalytic glutamate. This steric hindrance is resolved when the first subunit hydrolyzes ATP and is ADP bound. This obstructive mechanism is supported by functional mutagenesis and appears to be conserved across several Φ29 relatives. Mutual information analysis of our simulations revealed intersubunit signaling pathways, via the trans-acting obstructive residues, that allow for sensing and communication between the binding pockets of adjacent subunits. This work reveals how the sequential order of DNA translocation events among subunits is preserved through trans-subunit interactions and pathways.

Introduction: Nephrotic syndrome is defined as the presence of nephrotic range proteinuria, hypercholesterolemia, and generalized edema. Proteinuria that is more than 85% albumin is selective proteinuria. Albumin has (-ve) charge, and it is loss of glomerular membrane (-ve) charges could be significant in causing albuminuria. Objective: Comparison of Furosemide-Spironolactone Combination and Furosemide-Metolazone Combination in Treating Refractory Edema in Nephrotic Syndrome Patients. Materials and methods: This was a cross-sectional study design of six month from December 2023 to May2024. Results: The mean Age of the patients enrolled for the study with the minimum age being 1 year and maximum age being 14 years and the mean age was 5.6 years. It shows that weight loss at day 4 and 5 was significantly higher in group B as compared to group A. Conclusions: Group B patients having lower mean abdominal girth as compared to group A. Mean Serum electrolyte values like S.Na+, S.K+, S.Ca+ of both the groups were compared and found to be similar thus highlighting that both the combinations had similar effects in the patients with respect to electrolyte abnormalities.

Abstract A theoretical framework is presented for temperature imaging from long‐wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end‐to‐end design of a metasurface‐optics frontend and a computational‐reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end‐to‐end approach that optimizes manufacturable, single‐layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high‐quality, noise‐robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra‐compact thermal‐imaging device. It is also shown that Planck regression is much more generalizable to arbitrary images than a straightforward neural‐network reconstruction, which requires a large training set of domain‐specific images.

Polymer-grafted metal-organic frameworks (MOFs) can be used to form free-standing self-assembled MOF monolayers (SAMMs). Polymer chains can be introduced onto MOF surfaces through either the ligands or metal nodes using both grafting-to and grafting-from approaches. However, controlling the grafting density of polymer-grafted MOFs has not yet been achieved, because a means to control the density of grafting sites on the MOF surface has not been developed. In this study, the grafting density of polymer-grafted UiO-66 (UiO = University of Oslo) was controlled by functionalizing a portion of the Zr(IV) secondary building units (SBUs) on a UiO-66 surface with a so-called blocking agent. The remaining sites on the UiO-66 SBUs were functionalized with polymerization initiation groups, and polymers were grown from these sites to obtain particles with variable grafting densities and chain lengths that form SAMMs at an air-water interface. Even under conditions of low grafting density, these materials retain the ability to form SAMMs and their free-standing ability. Changes in particle arrangement within the monolayers were investigated using SEM imaging, and the toughness of the monolayers was evaluated using a film-on-water (FOW) method. Furthermore, coarse-grained molecular dynamics simulations were carried out to elucidate the morphology and mechanical properties of the monolayers. Findings from both experiments and simulations indicate that the toughness of SAMMs is more heavily influenced by the chain length of the grafted polymers than by the overall polymer content in the composite.

We present a modular DNA origami design approach to address the challenges of assembling geometrically complex nanoscale structures, including those with nonuniform Gaussian curvature. This approach features a core structure that completely conserves the scaffold routing across different designs and preserves more than 70% of the DNA staples between designs, dramatically reducing both cost and effort, while enabling precise and independent programming of subunit interactions and binding angles through adjustable overhang lengths and sequences. Using cryogenic electron microscopy, gel electrophoresis, and coarse-grained molecular dynamics simulations, we validate a set of robust design rules. We demonstrate the method's utility by assembling a variety of self-limiting structures, including anisotropic shells with controlled inter-subunit interactions and curvature, and a toroid with globally varying curvature. Our strategy is both cost-effective and versatile, providing a promising and efficient solution for the synthetic fabrication of complex nanostructures.

Abstract Certain viruses such as tailed bacteriophages and herpes simplex virus package double-stranded DNA into empty procapsids via powerful, ring-shaped molecular motors. High resolution structures and force measurements on the DNA packaging motor of bacteriophage Φ29 revealed that its five ATPase subunits coordinate ATP hydrolysis with each other to maintain the proper cyclic sequence of DNA translocation steps about the ring. Here, we explore how the Φ29 motor regulates translocation by timing key events, namely ATP binding/hydrolysis and DNA gripping, through trans-subunit interactions. We used subunit dimers bound to DNA as our model system, a minimal system that still captures the conformation and trans-subunit interactions of the full pentametric motor complex. Molecular dynamics simulations of all-ATP and mixed ATP-ADP dimers revealed that the nucleotide occupancy of one subunit strongly affects the ability to hydrolyze ATP in the adjacent subunit by altering the free energy landscape of its catalytic glutamate approaching the gamma phosphate of ATP. Specifically, one ATP-bound subunit donates residues in trans that sterically block the neighboring subunit’s catalytic glutamate. This steric hindrance is resolved when the first subunit hydrolyzes ATP and is ADP-bound. This obstructive mechanism is supported by functional mutagenesis and appears to be conserved across several Φ29 relatives. Mutual information analysis of our simulations revealed intersubunit signaling pathways, via the trans-acting obstructive residues, that allow for sensing and communication between the binding pockets of adjacent subunits. This work shows that the sequential order of DNA translocation events amongst subunits is preserved through novel trans-subunit interactions and pathways.

DNA nanotechnology enables the precise construction of intricate nanoscale structures. Over the past two decades, significant progress has been made in incorporating dynamic functionalities into these nanostructures. Concurrently, innovative strategies have emerged for their self-assembly and surface patterning into larger, more complex architectures. This review explores the convergence of these two key capabilities-reconfigurability and hierarchical assembly-to engineer DNA origami superstructures with intrinsic dynamic behavior. We begin by outlining foundational strategies in dynamic design, hierarchical assembly, and surface placement, then review recent progress in leveraging these strategies to construct dynamic superstructures with emergent behaviors. The article concludes with a roadmap of major challenges and opportunities shaping the future of this rapidly evolving field.

Fluid-fluid interfaces are an attractive platform for self-assembling nanoparticles into low-dimensional materials. In this Perspective, we review recent developments in the use of interfaces to direct the assembly of spherical and anisotropic nanoparticles into diverse and sophisticated architectures. We illustrate how nanoparticle clusters, strings, networks, superlattices, chiral lattices, and quasicrystals can be self-assembled by harnessing the frustration between interfacial and interparticle forces. We highlight the role of polymeric ligands attached to the surface of nanoparticles in modulating assembly behavior by directly altering particle-fluid and particle-particle interactions or by deforming at interfaces and junctions between particles. We conclude by providing a roadmap of key questions and opportunities in this exciting field of interfacial assembly.

We present a theoretical framework for temperature imaging from long-wavelength infrared thermal radiation (e.g. 8-12 $μ$m) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. We introduce a new nonlinear reconstruction algorithm, ``Planck regression," that reconstructs the temperature map from a grayscale sensor image, even in the presence of severe chromatic aberration, by exploiting blackbody and optical physics particular to thermal imaging. We combine this algorithm with an end-to-end approach that optimizes a manufacturable, single-layer metasurface to yield the most accurate reconstruction. Our designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. We also show that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images.

DNA origami nanostructures (DOs) are promising tools for applications including drug delivery, biosensing, detecting biomolecules, and probing chromatin substructures. Targeting these nanodevices to mammalian cell nuclei could provide impactful approaches for probing, visualizing, and controlling biomolecular processes within live cells. We present an approach to deliver DOs into live-cell nuclei. We show that these DOs do not undergo detectable structural degradation in cell culture media or cell extracts for 24 hours. To deliver DOs into the nuclei of human U2OS cells, we conjugated 30-nanometer DO nanorods with an antibody raised against a nuclear factor, specifically the largest subunit of RNA polymerase II (Pol II). We find that DOs remain structurally intact in cells for 24 hours, including inside the nucleus. We demonstrate that electroporated anti-Pol II antibody-conjugated DOs are piggybacked into nuclei and exhibit subdiffusive motion inside the nucleus. Our results establish interfacing DOs with a nuclear factor as an effective method to deliver nanodevices into live-cell nuclei.

Metropolis-Hastings estimates intractable expectations - can differentiating the algorithm estimate their gradients? The challenge is that Metropolis-Hastings trajectories are not conventionally differentiable due to the discrete accept/reject steps. Using a technique based on recoupling chains, our method differentiates through the Metropolis-Hastings sampler itself, allowing us to estimate gradients with respect to a parameter of otherwise intractable expectations. Our main contribution is a proof of strong consistency and a central limit theorem for our estimator under assumptions that hold in common Bayesian inference problems. The proofs augment the sampler chain with latent information, and formulate the estimator as a stopping tail functional of this augmented chain. We demonstrate our method on examples of Bayesian sensitivity analysis and optimizing a random walk Metropolis proposal.

Checkerboard lattices-where the resulting structure is open, porous, and highly symmetric-are difficult to create by self-assembly. Synthetic systems that adopt such structures typically rely on shape complementarity and site-specific chemical interactions that are only available to biomolecular systems (e.g., protein, DNA). Here we show the assembly of checkerboard lattices from colloidal nanocrystals that harness the effects of multiple, coupled physical forces at disparate length scales (interfacial, interparticle, and intermolecular) and that do not rely on chemical binding. Colloidal Ag nanocubes were bi-functionalized with mixtures of hydrophilic and hydrophobic surface ligands and subsequently assembled at an air-water interface. Using feedback between molecular dynamics simulations and interfacial assembly experiments, we achieve a periodic checkerboard mesostructure that represents a tiny fraction of the phase space associated with the polymer-grafted nanocrystals used in these experiments. In a broader context, this work expands our knowledge of non-specific nanocrystal interactions and presents a computation-guided strategy for designing self-assembling materials.

Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

Nucleic acids in biofluids are emerging biomarkers for the molecular diagnostics of diseases, but their clinical use has been hindered by the lack of sensitive detection assays. Herein, we report the development of a sensitive nucleic acid detection assay named SPOT (sensitive loop-initiated DNAzyme biosensor for nucleic acid detection) by rationally designing a catalytic DNAzyme of endonuclease capability into a unified one-stranded allosteric biosensor. SPOT is activated once a nucleic acid target of a specific sequence binds to its allosteric module to enable continuous cleavage of molecular reporters. SPOT provides a highly robust platform for sensitive, convenient and cost-effective detection of low-abundance nucleic acids. For clinical validation, we demonstrated that SPOT could detect serum miRNAs for the diagnostics of breast cancer, gastric cancer and prostate cancer. Furthermore, SPOT exhibits potent detection performance over SARS-CoV-2 RNA from clinical swabs with high sensitivity and specificity. Finally, SPOT is compatible with point-of-care testing modalities such as lateral flow assays. Hence, we envision that SPOT may serve as a robust assay for the sensitive detection of a variety of nucleic acid targets enabling molecular diagnostics in clinics.

Abstract Nucleic acids in biofluids are emerging biomarkers for the molecular diagnostics of diseases, but their clinical use has been hindered by the lack of sensitive detection assays. Herein, we report the development of a sensitive nucleic acid detection assay named SPOT ( s ensitive loo p ‐initiated DNAzyme biosens o r for nucleic acid de t ection) by rationally designing a catalytic DNAzyme of endonuclease capability into a unified one‐stranded allosteric biosensor. SPOT is activated once a nucleic acid target of a specific sequence binds to its allosteric module to enable continuous cleavage of molecular reporters. SPOT provides a highly robust platform for sensitive, convenient and cost‐effective detection of low‐abundance nucleic acids. For clinical validation, we demonstrated that SPOT could detect serum miRNAs for the diagnostics of breast cancer, gastric cancer and prostate cancer. Furthermore, SPOT exhibits potent detection performance over SARS‐CoV‐2 RNA from clinical swabs with high sensitivity and specificity. Finally, SPOT is compatible with point‐of‐care testing modalities such as lateral flow assays. Hence, we envision that SPOT may serve as a robust assay for the sensitive detection of a variety of nucleic acid targets enabling molecular diagnostics in clinics.

This research was to formulate and evaluate bilayer tablets for the effective treatment of diabetes. Bilayer tablets, combining immediate and sustained-release layers, aim to provide a swift onset of action followed by prolonged therapeutic effects. The immediate-release layer was formulated using Metformin HCl, while the sustained-release layer contained Glipizide. Both layers were prepared using wet granulation and direct compression techniques. Reformulation studies, including drug-excipient compatibility and powder flow properties, were conducted to ensure stability and manufacturability. The bilayer tab is evaluated for its properties, like hardness, friability, weight variation, and thickness. Additionally, In vitro dissolution studies were performed to compare tab release profiles for the immediate and sustained-release layers. The immediate-release layer showed a rapid drug release within the first hour, ensuring quick therapeutic action. In contrast, the sustained-release layer exhibited a controlled release over 12 hours, maintaining steady plasma levels. Stability studies indicated that the bilayer tablets remained stable under accelerated conditions. The combination of Metformin HCl and Glipizide in a bilayer tablet offers an efficient therapeutic approach for managing diabetes by enhancing patient compliance through reduced dosing frequency and optimized drug release profiles.

applications including drug delivery; biosensing, detecting biomolecules; and probing chromatin sub-structures. Targeting these nanodevices to mammalian cell nuclei could provide impactful approaches for probing visualizing and controlling important biological processes in live cells. Here we present an approach to deliver DO strucures into live cell nuclei. We show that labelled DOs do not undergo detectable structural degradation in cell culture media or human cell extracts for 24 hr. To deliver DO platforms into the nuclei of human U2OS cells, we conjugated 30 nm long DO nanorods with an antibody raised against the largest subunit of RNA Polymerase II (Pol II), a key enzyme involved in gene transcription. We find that DOs remain structurally intact in cells for 24hr, including within the nucleus. Using fluorescence microscopy we demonstrate that the electroporated anti-Pol II antibody conjugated DOs are efficiently piggybacked into nuclei and exihibit sub-diffusive motion inside the nucleus. Our results reveal that functionalizing DOs with an antibody raised against a nuclear factor is a highly effective method for the delivery of nanodevices into live cell nuclei.

DNA origami has been used as biotemplates for growing a range of inorganic materials to create novel organic-inorganic hybrid nanomaterials. Recently, the solution-based silicification of DNA has been used to grow thin silica shells on DNA origami. However, the silicification reaction is sensitive to the reaction conditions and often results in uncontrolled DNA origami aggregation, especially when growth of thicker silica layers is desired. Here, we investigated how site-specifically placed polynucleotide brushes influence the silicification of DNA origami. Our experiments showed that long DNA brushes, in the form of single- or double-stranded DNA, significantly suppress the aggregation of DNA origami during the silicification process. Furthermore, we found that double-stranded DNA brushes selectively promote silica growth on DNA origami surfaces. These observations were supported and explained by coarse-grained molecular dynamics simulations. This work provides new insights into our understanding of the silicification process on DNA and provides a powerful toolset for the development of novel DNA-based organic-inorganic nanomaterials.

Abstract Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

DNA nanotechnology provides an approach to create precise, tunable, and biocompatible nanostructures for biomedical applications. However, the stability of these structures is severely compromised in biological milieu due to their fast degradation by nucleases. Recently, we showed how enzymatic polymerization could be harnessed to grow polynucleotide brushes of tunable length and location on the surface of DNA origami nanostructures, which greatly enhances their nuclease stability. Here, we report on strategies that allow for both spatial and temporal control over polymerization through activatable initiation, cleavage, and regeneration of polynucleotide brushes using restriction enzymes. The ability to site-specifically decorate DNA origami nanostructures with polynucleotide brushes in a spatiotemporally controlled way provides access to "smart" functionalized DNA architectures with potential applications in drug delivery and supramolecular assembly.

Abstract DNA nanotechnology provides an approach to create precise, tunable, and biocompatible nanostructures for biomedical applications. However, the stability of these structures is severely compromised in biological milieu due to their fast degradation by nucleases. Recently, we showed how enzymatic polymerization could be harnessed to grow polynucleotide brushes of tunable length and location on the surface of DNA origami nanostructures, which greatly enhances their nuclease stability. Here, we report on strategies that allow for both spatial and temporal control over polymerization through activatable initiation, cleavage, and regeneration of polynucleotide brushes using restriction enzymes. The ability to site‐specifically decorate DNA origami nanostructures with polynucleotide brushes in a spatiotemporally controlled way provides access to “smart” functionalized DNA architectures with potential applications in drug delivery and supramolecular assembly.

Background: Severe acute malnutrition (SAM) is one of the major causes of mortality and morbidity among children in developing countries and is an important contributing factor to deaths occurring from preventable causes in children <5 years of age. Decreasing child mortality and improving maternal health depends heavily on reducing malnutrition. Aims and Objectives: To study the prevalence, risk factors, medical complications, and outcome (morbidity and mortality) of children admitted with SAM along with biochemical changes. Materials and Methods: This was an observational cross-sectional study conducted in the Department of Pediatrics, Uttar Pradesh University of Medical Sciences, Saifai, Uttar Pradesh, India. Indoor Patient admitted in the department of pediatrics with SAM between ages 6 and 59 months. A total of 100 children were included in the present study. This study was done from January 2019 to June 2020. Results: Approximately 59% of admitted SAM patients were under 1 year of age and 24% were between 1 and 2 years. Most of the patients have rural residences nearly 93%, as most of the vicinity of our institute belonged to rural areas. According to Modified Kuppuswamy’s socio-economic status (SES) 46% that is maximally belonged to the Lower middle class and 45% belonged to the Lower class of SES. Out of 100 children, 20% were having magnesium deficiency and 56% children were having phosphate deficiency. Conclusion: Survival of children was significantly associated with random blood sugar, mg, and PO4 level. Therefore, timely identification and intervention of biochemical derangement in SAM patients are necessary to improve the survival of SAM patients.

Recent advances in structural DNA nanotechnology have been facilitated by design tools that continue to push the limits of structural complexity while simplifying an often-tedious design process. We recently introduced the software MagicDNA, which enables design of complex 3D DNA assemblies with many components; however, the design of structures with free-form features like vertices or curvature still required iterative design guided by simulation feedback and user intuition. Here, we present an updated design tool, MagicDNA 2.0, that automates the design of free-form 3D geometries, leveraging design models informed by coarse-grained molecular dynamics simulations. Our GUI-based, stepwise design approach integrates a high level of automation with versatile control over assembly and subcomponent design parameters. We experimentally validated this approach by fabricating a range of DNA origami assemblies with complex free-form geometries, including a 3D Nozzle, G-clef, and Hilbert and Trifolium curves, confirming excellent agreement between design input, simulation, and structure formation.

Abstract DNA nanotechnology leverages the canonical base-pairing rules and geometry of DNA to create highly precise nanoscale structures with many potential applications. While the design and fabrication of DNA nanostructures is well-established, the self-assembly process that produces these structures is still poorly understood, especially for DNA origami that involve the assembly of hundreds of strands. Many experimental and computational efforts have sought to better understand DNA origami folding, but the small length and time scales of individual binding events and the long timescale over which folding occurs have posed significant challenges. Here, we present a new mesoscopic model that uses a switchable force field to capture the mechanical behavior of single- and double-stranded DNA motifs and transition between them at a coarseness level of up to 8 nucleotides per particle, allowing access to the long assembly timescales of DNA origami up to several kilobases in size. Brownian dynamics simulations of 4-helix bundle (4HB) structures using this model reveal a hierarchical folding process involving the zipping of structural domains into a partially folded precursor structure followed by gradual crystallization into the final structure. We elucidate the role of hybridization strength, scaffold routing, and staple design in the folding order and kinetics. Simulation of larger 32HB structures reveals heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to smaller, more accessible structures like the 4HB, which exhibit first-order kinetics and virtually defect-free folding. The development of this model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it. Our approach fuses recent advances in stochastic automatic differentiation with traditional Markov chain coupling schemes, providing an unbiased and low-variance gradient estimator. This allows us to apply gradient-based optimization to objectives expressed as expectations over intractable target densities. We demonstrate our approach by finding an ambiguous observation in a Gaussian mixture model and by maximizing the specific heat in an Ising model.