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Cyber-physical system (CPS) can increase efficiency and lower costs in manufacturing operations. CPS and related technologies can help medical device manufacturers to meet the rising demands for quality and affordable healthcare products and services. In this paper, a novel architecture for CPS-based manufacturing workcell systems was proposed. Its contribution is to provide quality inspection and operator guidance in manual assembly systems, specifically for the production of hybrid medical devices. The proposed system involves the integration of enabling technologies including Machine Vision (MV), Internet-of-things (IoT) and Augmented Reality. The group illustrated and evaluated the implementation of two CPS prototypes based on the proposed architecture on the fabrication of a specific hybrid medical device, verifying their effectiveness as guidance tools for operators. The two prototypes, CPS-1 and CPS-2 differ in their MV subsystems by having non-deep learning and deep learning-based image processing methods respectively. Experimental results illustrate that, when exposed to various changing conditions, the Regional Convolutional Neural Network (R-CNN) of CPS-2, on average, has outperformed the non-deep learning image processing method of CPS-1 by 42.3% and 8.5%, in terms of accuracy and precision respectively. Hence, this presents evidence on the usefulness of R-CNN-based MV system to increase the flexibility and adaptability of the CPS in terms of object monitoring and reporting.

Massively parallel DNA sequencing is established, yet high-throughput protein profiling remains challenging. Here, we report a barcoding approach that leverages the combinatorial sequence content and the configurational programmability of DNA nanostructures for high-throughput multiplexed profiling of the subcellular expression and distribution of proteins in whole cells. The barcodes are formed by in situ hybridization of tetrahedral DNA nanostructures and short DNA sequences conjugated with protein-targeting antibodies, and by nanostructure-assisted ligation (either enzymatic or chemical) of the nanostructures and exogenous DNA sequences bound to nanoparticles of different sizes (which cause these localization sequences to differentially distribute across subcellular compartments). Compared with linear DNA barcoding, the nanostructured barcodes enhance the signal by more than 100-fold. By implementing the barcoding approach on a microfluidic device for the analysis of rare patient samples, we show that molecular subtypes of breast cancer can be accurately classified and that subcellular spatial markers of disease aggressiveness can be identified.

Accessible and adaptable nucleic acid diagnostics remains a critical challenge in managing the evolving COVID‐19 pandemic. Here, an integrated molecular nanotechnology that enables direct and programmable detection of SARS‐CoV‐2 RNA targets in native patient specimens is reported. Termed synergistic coupling of responsive equilibrium in enzymatic network (SCREEN), the technology leverages tunable, catalytic molecular nanostructures to establish an interconnected, collaborative architecture. SCREEN mimics the extraordinary organization and functionality of cellular signaling cascades. Through programmable enzyme–DNA nanostructures, SCREEN activates upon interaction with different RNA targets to initiate multi‐enzyme catalysis; through system‐wide favorable equilibrium shifting, SCREEN directly transduces a single target binding into an amplified electrical signal. To establish collaborative equilibrium coupling in the architecture, a computational model that simulates all reactions to predict overall performance and optimize assay configuration is developed. The developed platform achieves direct and sensitive RNA detection (approaching single‐copy detection), fast response (assay reaction is completed within 30 min at room temperature), and robust programmability (across different genetic loci of SARS‐CoV‐2). When clinically evaluated, the technology demonstrates robust and direct detection in clinical swab lysates to accurately diagnose COVID‐19 patients., A molecular nanotechnology is developed utilizing interconnected enzyme–DNA nanostructures to achieve cooperative multi‐enzyme catalysis. Through programmable nanostructures and favorable equilibrium coupling in the system architecture, the technology achieves sensitive and versatile detection for the direct measurement of SARS‐CoV‐2 RNA targets in native patient specimens (30 min at room temperature).

Transition-state molecular switches enable hyper responsive detection of SARS-CoV-2 in native clinical samples., Despite the importance of nucleic acid testing in managing the COVID-19 pandemic, current detection approaches remain limited due to their high complexity and extensive processing. Here, we describe a molecular nanotechnology that enables direct and sensitive detection of viral RNA targets in native clinical samples. The technology, termed catalytic amplification by transition-state molecular switch (CATCH), leverages DNA-enzyme hybrid complexes to form a molecular switch. By ratiometric tuning of its constituents, the multicomponent molecular switch is prepared in a hyperresponsive state—the transition state—that can be readily activated upon the binding of sparse RNA targets to turn on substantial enzymatic activity. CATCH thus achieves superior performance (~8 RNA copies/μl), direct fluorescence detection that bypasses all steps of PCR (

Biologically templated plasmonics enables simultaneous biophysical and biomolecular analysis of exosome biomarkers., Exosomes are nanoscale vesicles distinguished by characteristic biophysical and biomolecular features; current analytical approaches, however, remain univariate. Here, we develop a dedicated platform for multiparametric exosome analysis—through simultaneous biophysical and biomolecular evaluation of the same vesicles—directly in clinical biofluids. Termed templated plasmonics for exosomes, the technology leverages in situ growth of gold nanoshells on vesicles to achieve multiselectivity. For biophysical selectivity, the nanoshell formation is templated by and tuned to distinguish exosome dimensions. For biomolecular selectivity, the nanoshell plasmonics locally quenches fluorescent probes only if they are target-bound on the same vesicle. The technology thus achieves multiplexed analysis of diverse exosomal biomarkers (e.g., proteins and microRNAs) but remains unresponsive to nonvesicle biomarkers. When implemented on a microfluidic, smartphone-based sensor, the platform is rapid, sensitive, and wash-free. It not only distinguished biomarker organizational states in native clinical samples but also showed that the exosomal subpopulation could more accurately differentiate patient prognosis.

Current technologies to measure drug–target interactions require complex processing and invasive tissue biopsies, limiting their clinical utility for cancer treatment monitoring. Here we develop an analytical platform that leverages circulating extracellular vesicles (EVs) for activity-based assessment of tumour-specific drug–target interactions in patient blood samples. The technology, termed extracellular vesicle monitoring of small-molecule chemical occupancy and protein expression (ExoSCOPE), utilizes bio-orthogonal probe amplification and spatial patterning of molecular reactions within matched plasmonic nanoring resonators to achieve in situ analysis of EV drug dynamics. It measures changes in drug occupancy and protein composition in molecular subpopulations of EVs. When used to monitor various targeted therapies, the ExoSCOPE revealed EV signatures that closely reflected cellular treatment efficacy. We further applied the technology for clinical cancer diagnostics and treatment monitoring. Using a small volume of blood, the ExoSCOPE accurately classified disease status and rapidly distinguished between targeted treatment outcomes, within 24 h after treatment initiation. In this work, the authors develop a platform that leverages extracellular vesicles to measure drug–target engagement and apply it to monitor the outcomes of targeted treatments in lung cancer patients.

Manufacturers often overlook the importance of optimizing workcell layout. Optimizing manufacturing workcell layouts can help increase production efficiency by reducing unnecessary actions and movements. However, the application of conventional optimization methods to modern production lines is not only becoming too complex, but also becoming inaccurate and unreliable. In this paper, we proposed an accurate and practical methodology that is readily implementable. The methodology uses multi-objective combinatorial optimization (MOCO) concepts to optimize manufacturing workcell layouts, specifically for the production of hybrid medical devices. The proposed model, also known as the Two-Step MOCO model, considers objectives and constraints that are selected by the manufacturer. The first step determines the optimal number of equipment and operators required given a fixed budget, while the second step determines the optimal positions for every equipment, given a fixed space and the required safety distance among each equipment, after considering the results of the first step. We illustrated the implementation of the proposed model on the fabrication of a hybrid medical device. Virtual manufacturing workcells were developed and utilized to evaluate the proposed methodology. The evaluation results proved the feasibility and effectiveness of the proposed model, and its ability to provide flexibility to manufacturers. This paper highlights the potential of our proposed optimization model in improving production efficiency in medical device and other manufacturing industries.

Rapid, visual detection of pathogen nucleic acids has broad applications in infection management. Here we present a modular detection platform, termed enzyme-assisted nanocomplexes for visual identification of nucleic acids (enVision). The system consists of an integrated circuit of enzyme–DNA nanostructures, which function as independent recognition and signaling elements, for direct and versatile detection of pathogen nucleic acids from infected cells. The built-in enzymatic cascades produce a rapid color readout for the naked eye; the assay is thus fast (, Rapid, visual detection of pathogens is important for point-of-care diagnostics. Here the authors present enVision, which uses enzyme-DNA complexes to detect pathogen nucleic acids and provide a rapid, smartphone compatible readout.

Programmable Detection of SARS‐CoV‐2 In article number 2101155, Huilin Shao and co‐workers report the collaborative coupling of enzyme–DNA nanostructures to establish multi‐enzyme catalysis. Through programmable nanostructures and favorable equilibrium architecture, the technology achieves direct and sensitive detection of SARS‐CoV‐2 RNA in native patient samples, within 30 minutes at room temperature. [Image: see text]

Optimizing assembly is an important process for fabrication of hybrid medical devices comprising multiple components and materials. The process is highly complex, time-consuming and requires the devices to be free from contamination. It requires highly skilled and well-trained workers and a sterile and conducive environment to perform the assembly operations. However, the assembly of medical devices is often neglected and/or overlooked. We propose a novel, intelligent workcell system design that aims to effectively guide operators in the assembly process while maintaining the necessary environmental settings. The potential of the proposed workcell for fabrication of an artificial trachea is investigated.

This paper proposed a blockchain manufacturing framework that involves the utilization of secondary validation and collaborative distribution concepts. Conventional and blockchain manufacturing simulators were developed to investigate the effects of blockchain technologies on manufacturing systems in terms of time and product quality. The simulation results illustrated that the products manufactured in the blockchain-based system are of better quality than the conventional system but does not necessarily reduce the manufacturing times due to the secondary validation and collaborative distribution processes, which are also responsible for ensuring manufacturing quality. The results also emphasized the importance of utilizing collaborative distribution models to improve workcell utilization within blockchain-based systems. By implementing the collaborative distribution logic within the blockchain, the manufacturing times will be lessened. This paper highlights the potential of utilizing blockchain technologies to enhance Cyber-Physical Systems in the manufacturing industry.

Monitoring energy consumption of equipment in a manufacturing workcell is part of an important process of energy consumption control. Sophisticated and advanced energy sensors have made it possible to monitor energy consumption of equipment in the workcell via a smart device. However, it is worthy to note that stakeholders like new workers, eco-consultants and/or authorities are unfamiliar with the equipment. It might be challenging to visualize the energy consumption of each equipment in the workcell from the smart device for them. In order to aid this group of stakeholders in the monitoring of energy consumption process, we propose a novel, augmented reality (AR) and Internet of Things (IoT)-based energy monitoring conceptual design. It aims to not only give users an option to have an overall view of the energy consumption in the workcell but also to allow users to visualize energy consumption of each equipment in the actual field. Moreover, AR technology has already started being used for assembly training and inspection purposes in some manufacturing companies; thus the proposed energy monitoring concept will add value with minimum cost because of its easy integration in the inbuilt system together with these features. With the help of AR technology, one can immediately pinpoint equipment with constant high consumption rates in real environments and give better advice to control energy consumption by exploring ways to reduce their energy usage. This paper highlights the potential of the proposed energy monitoring conceptual design in aiding users to better visualize the energy consumption patterns of individual equipment in the workcell.

Motor neuron diversification and regionalization are important hallmarks of spinal cord development and rely on fine spatiotemporal release of molecular cues. Here, we present a dedicated platform to engineer complex molecular profiles for directed neuronal differentiation. Methods: The technology, termed microhexagon interlace for generation of versatile and fine gradients (microHIVE), leverages on an interlocking honeycomb lattice of microstructures to dynamically pattern molecular profiles at a high spatial resolution. By packing the microhexagons as a divergent, mirrored array, the platform not only enables maximal mixing efficiency but also maintains a small device footprint. Results: Employing the microHIVE platform, we developed optimized profiles of growth factors to induce rostral-caudal patterning of spinal motor neurons, and directed stem cell differentiation in situ into a spatial continuum of different motor neuron subtypes. Conclusions: The differentiated cells showed progressive RNA and protein signatures, consistent with that of representative brachial, thoracic and lumbar regions of the human spinal cord. The microHIVE platform can thus be utilized to develop advanced biomimetic systems for the study of diseases in vitro.

Massively parallel DNA sequencing is established, yet high-throughput protein profiling remains challenging. Here, we report a barcoding approach that leverages the combinatorial sequence content and the configurational programmability of DNA nanostructures for high-throughput multiplexed profiling of the subcellular expression and distribution of proteins in whole cells. The barcodes are formed by in situ hybridization of tetrahedral DNA nanostructures and short DNA sequences conjugated with protein-targeting antibodies, and by nanostructure-assisted ligation (either enzymatic or chemical) of the nanostructures and exogenous DNA sequences bound to nanoparticles of different sizes (which cause these localization sequences to differentially distribute across subcellular compartments). Compared with linear DNA barcoding, the nanostructured barcodes enhance the signal by more than 100-fold. By implementing the barcoding approach on a microfluidic device for the analysis of rare patient samples, we show that molecular subtypes of breast cancer can be accurately classified and that subcellular spatial markers of disease aggressiveness can be identified.

The cell expansion process is a crucial part of generating cells on a large-scale level in a bioreactor system. Hence, it is important to set operating conditions (e.g. initial cell seeding distribution, culture medium flow rate) to an optimal level. Often, the initial cell seeding distribution factor is neglected and/or overlooked in the design of a bioreactor using conventional seeding distribution methods. This paper proposes a novel seeding distribution method that aims to maximize cell growth and minimize production time/cost. The proposed method utilizes two computational models; the first model represents cell growth patterns whereas the second model determines optimal initial cell seeding positions for adherent cell expansions. Cell growth simulation from the first model demonstrates that the model can be a representation of various cell types with known probabilities. The second model involves a combination of combinatorial optimization, Monte Carlo and concepts of the first model, and is used to design a multi-layer 2D bio-scaffold system that increases cell production efficiency in bioreactor applications. Simulation results have shown that the recommended input configurations obtained from the proposed optimization method are the most optimal configurations. The results have also illustrated the effectiveness of the proposed optimization method. The potential of the proposed seeding distribution method as a useful tool to optimize the cell expansion process in modern bioreactor system applications is highlighted.

Spermatozoa are one of the few mammalian cell types that cannot be fully derived in vitro, severely limiting the application of modern genomic techniques to study germ cell biology. The current gold standard approach of characterizing single-gene knockout mice is slow as generation of each mutant line can take 6-9 months. Here, we describe an in vivo approach to rapid functional screening of germline genes based on a new nonsurgical, nonviral in vivo transfection method to deliver nucleic acids into testicular germ cells. By coupling multiplex transfection of short hairpin RNA (shRNA) constructs with pooled amplicon sequencing as a readout, we were able to screen many genes for spermatogenesis function in a quick and inexpensive experiment. We transfected nine mouse testes with a pilot pool of RNA interference (RNAi) against well-characterized genes to show that this system is highly reproducible and accurate. With a false negative rate of 18% and a false positive rate of 12%, this method has similar performance as other RNAi screens in the well-described Drosophila model system. In a separate experiment, we screened 26 uncharacterized genes computationally predicted to be essential for spermatogenesis and found numerous candidates for follow-up studies. Finally, as a control experiment, we performed a long-term selection screen in neuronal N2a cells, sampling shRNA frequencies at five sequential time points. By characterizing the effect of both libraries on N2a cells, we show that our screening results from testis are tissue-specific. Our calculations indicate that the current implementation of this approach could be used to screen thousands of protein-coding genes simultaneously in a single mouse testis. The experimental protocols and analysis scripts provided will enable other groups to use this procedure to study diverse aspects of germ cell biology ranging from epigenetics to cell physiology. This approach also has great promise as an applied tool for validating diagnoses made from medical genome sequencing, or designing synthetic biological sequences that can act as potent and highly specific male contraceptives.