Low Throughput Research Articles

Throughput rates and time to completion of health science students of rural origin in South Africa

Background. There is a shortage of qualified healthcare workers in South Africa (SA), as well as a maldistribution between urban and rural areas. Research has shown that health professionals of rural origin are more likely to live and work in rural areas than their urban colleagues. It has been recommended that student selection policies of higher education institutions should prioritise applicants from rural and remote areas to address the urban-rural maldistribution of graduates and to redress historical equity issues. However, university students in SA have high attrition and low graduation rates. The Umthombo Youth Development Foundation (UYDF) recruits and supports rural-origin health science students, who completed Grade 12 at a rural school, to address staff shortages in rural areas. Objectives. To report on the throughput rates (percentage of cohort who graduate) and time to completion (number of years taken to graduate) of eight cohorts of UYDF-supported rural-origin health science students. Methods. A total of 388 student records from the 2008 - 2015 cohorts, covering 17 different health science disciplines, were analysed, and throughput rates and time to completion for the different cohorts and health science disciplines were calculated. Throughput rates and time to completion were also calculated for 3-year, 4-year and 6-year qualifications. Results. Throughput for the 2008 - 2015 cohorts ranged between 82% (2010 cohort) and 100% (2009 cohort). For 3-year qualifications, five cohorts had 100% throughput and one cohort 50% throughput. For 4-year qualifications, throughput ranged from 75% (2010 cohort) to 100% (2009 cohort). For medical students (6-year qualification), throughput ranged from a low of 81% (2014 cohort) to 100% in two cohorts (2008 and 2009). More female students (n=29) were excluded than male students (n=14). With regard to time to completion, over the eight cohorts 68% of students completed in the minimum time, 23% needed 1 additional year, 6% an additional 2 years, 2% an additional 3 years, and 1% an additional 4 years. For 4-year qualifications, 58% of students completed in the minimum time, 28% needed 1 additional year, 9% needed 2 additional years, 4% needed 3 additional years, and 1% needed an additional 4 years. Of medical students, 76% completed in the minimum time, 19% needed 1 additional year, and 98% had completed after 2 additional years. Discussion. The UYDF students studying for 3-year and 4-year qualifications exceeded the national throughput rates in seven of eight cohorts. UYDF medical students exceeded the national statistics in four cohorts, had similar throughputs in two cohorts, and had lower throughputs than the national statistics in two cohorts. Overall, 91% of UYDF students across the eight cohorts completed in the minimum time plus 1 additional year, exceeding national statistics. Conclusion. The relatively high throughput rates compared with the national statistics highlight the academic ability of rural-origin health science students and provide hope that national shortages of healthcare workers may be addressed if there is an investment in such students..

Evaluation of lignin and dip coating for elemental analysis by thin film microextraction followed by laser-induced breakdown spectroscopy

Separation and preconcentration procedures are often necessary to eliminate interferents, enhance sensitivity, and improve detection limits. Conventional solid-phase extraction (batch) finds extensive applications in the determination of various analytes, particularly in low concentrations and complex matrices such as environmental, food, and biological samples. However, drawbacks include high reagent consumption, time-consuming sample processing and low analytical throughput. To address these issues and promote Green Analytical Chemistry (GAC), novel methods have emerged, with solid-phase and liquid-phase microextraction being notable examples. Thin Film Microextraction (TFME) represents an innovative approach involving a solid support coated with a thin layer of adsorbent material, attached to a rod and immersed in the sample solution. Extracted analytes can be quantified either after desorption (elution) or directly on the thin film. This article aims to explore TFME's potential when combined with Laser-Induced Breakdown Spectroscopy (LIBS) for Cd, Cr and Ni determination using lignin as adsorbent material deposited on a solid substrate by dip coating. Under optimized conditions, limits of detection obtained were 1.76 μg kg−1 (Cd), 5.72 μg kg−1 (Cr), and 3.27 μg kg−1 (Ni). The accuracy of the proposed method was evaluated through the analysis of a certified reference material (ERM® CA713), drinking and tap water.

NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus

Permissioned blockchains are widely used in scenarios such as digital assets, supply chains, government services, and Web 3.0, but their development is hindered by low throughput and scalability. Blockchain sharding addresses these issues by dividing the ledger into disjoint shards that can be processed concurrently. However, since cross-shard transactions require the collaboration of multiple shards, blockchain sharding needs a commit protocol to ensure the atomicity of executing these transactions, significantly impacting system performance. To this end, by exploiting the characteristics of deterministic ordering, we propose a cross-shard transaction processing protocol called cross-reserve, which eliminates this costly cross-shard coordination while providing the same consistency and atomicity guarantee. Based on the ordering-free execute–validate (EV) architecture, we implemented a blockchain prototype called NeuChain+, which further reduces the cross-shard transaction processing overhead using the pipelined read sets transmission. Experimental results show that NeuChain+ is scalable and outperforms state-of-the-art blockchain systems with 1.7–75.3× throughput under the SmallBank workload.

Parallel on-chip micropipettes enabling quantitative multiplexed characterization of vesicle mechanics and cell aggregates rheology.

Micropipette aspiration (MPA) is one of the gold standards for quantifying biological samples' mechanical properties, which are crucial from the cell membrane scale to the multicellular tissue. However, relying on the manipulation of individual home-made glass pipettes, MPA suffers from low throughput and no automation. Here, we introduce the sliding insert micropipette aspiration method, which permits parallelization and automation, thanks to the insertion of tubular pipettes, obtained by photolithography, within microfluidic channels. We show its application both at the lipid bilayer level, by probing vesicles to measure membrane bending and stretching moduli, and at the tissue level by quantifying the viscoelasticity of 3D cell aggregates. This approach opens the way to high-throughput, quantitative mechanical testing of many types of biological samples, from vesicles and individual cells to cell aggregates and explants, under dynamic physico-chemical stimuli.

Reproducible and high sample throughput isomiR next-generation sequencing for cancer diagnosis.

e15013 Background: Next-generation sequencing (NGS) can produce up to 6 Tb of data per run with single-nucleotide accuracy, making it ideal for quantifying isomiRs, which encompass both canonical miRNAs and their variants, for clinical applications. However, NGS has poor reproducibility and low sample throughput in quantifying circulating isomiRs due to significant technical variations and the limitations of the multiplex strategy, as evidenced by the fact that no isomiR NGS technique has been successfully used to diagnose cancer. Methods: To address these challenges, a library construction method including a dual unique-dual-index (DUDI) technology was developed. DUDI uses a pair of Inner UDI (IUDI) and outer UDI (OUDI) to label a sample. Twelve independent batches of isomiR NGS were carried out, including three repeated batches. Each batch included 100 gastric cancer and 100 control plasma samples. Batch effect, correlation coefficient (R), and principal component (PCA) analyses were used to evaluate technical reproducibility. Machine learning binary classification was used to assess biological reproducibility, with each pair of batch data serving interchangeably as both training and testing data. Results: In this multicenter study, over 700G of isomiR data were generated from 402 gastric cancer and 498 control samples, with a maximum error rate of 1 in 7 million isomiRs being assigned to wrong samples. The PCA plot indicates high technical reproducibility across the three repeated batches, shown by the extensive intermingling of data points from each batch and the lack of distinct batch-wise clustering. This observation is reinforced by that the R value for each of 239 isomiRs between the repeated batches are close to 1. While the mutual machine learning validations between the repeated batches yielded ~95% accuracy, indicating high biological reproducibility. The accuracies of the validations between the different batches of different samples range from 70% to 82%. The lower accuracy is as expected, given the high genetic heterogeneity of cancer and the small sample size. Furthermore, the IsomiR differentiated expression profiles from the current NGS study closely match those from prior qPCR studies. Conclusions: The DUDI library construction method can produce reproducible high sample throughput NGS data, yet it is cost-effective and straightforward. The maximum number of samples that can be multiplexed in an NGS project is almost one million, i.e., 976 * 976, as IUDI and OUDI can be any of the 976 designed DUDIs. This number far exceeds the high sample throughput requirements of any NGS application. While the capability to distinguish true biological variations of IsomiRs from technical noise, demonstrated by the high technical and biological reproducibility and concordance with the qPCR data, enables the development of robust machine learning algorithms for cancer diagnostics.

Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

Low-Latency Adaptive Distributed Stream Join System Based on a Flexible Join Model

Stream join is a fundamental operation in stream processing and has attracted extensive research due to its large resource consumption and serious impact on system performance. As the theoretical basis of stream join systems, the stream join model greatly affects system performance. State-of-the-art stream join models either consume too much computing resources or too much storage resources, thus resulting in lower throughput or higher latency. In this paper, we propose a new stream join model for processing arbitrary join predicates, called CoModel, which offers a flexible trade-off between memory and computing resource consumption. More importantly, CoModel can achieve the minimum sum of the number of store operations and join operations among all existing join models, and thus can achieve the lowest latency and highest throughput when the overheads associated with the local stream join for each input tuple are approximately constant. We give a trade-off strategy for CoModel and theoretically prove its performance advantages based on queuing theory. Furthermore, we design and implement an adaptive distributed stream join system, CoStream, based on CoModel. CoStream can adaptively adjust its structure according to resource constraints and statistics of input data. We conduct extensive experiments for CoStream to evaluate its performance and adaptivity, and the results show that CoStream has the lowest latency and highest throughput in various scenarios.

Enhanced real-time road-vehicles’ detection and tracking for driving assistance

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%.

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

Integration Of Solution‐Processed BaTiO3 Thin Films with High Pockels Coefficient on Photonic Platforms

AbstractThe heterogeneous integration of ferroelectric BaTiO3 thin films on silicon (Si) and silicon nitride (SiN)‐based platforms for photonic integrated circuits (PICs) plays a crucial role in the development of future nanophotonic thin film modulators. Since the electro‐optic (EO) properties of ferroelectric thin films strongly depend on their crystal phase and texture, the integration of BaTiO3 thin films on these platforms is far from trivial. So far, a conventional integration route using a SrTiO3 template film in combination with high vacuum deposition methods has been developed, but it has a low throughput, is expensive and requires monocrystalline substrates. To close this gap, a cost‐efficient, high‐throughput and scalable method for integrating highly textured BaTiO3 films is needed. Therefore, an alternative method for the integration of highly textured BaTiO3 films using a La2O2CO3 template film in combination with a chemical solution deposition (CSD) process is presented. In this work, the structural and EO properties of the solution‐processed BaTiO3 film are characterized and its integration into an optical ring resonator is evaluated. The BaTiO3 film exhibits a fiber texture, has a large Pockels coefficient (reff) of 139 pm V−1, and integration into a ring resonator‐based modulator shows a VπL of 1.881 V cm and a bandwidth of > 40 GHz. This enables low‐cost, high‐throughput, and flexible integration of BaTiO3 films on PIC platforms and the potential large‐scale fabrication of nanophotonic BaTiO3 thin‐film modulators.

Highly sensitive extraction-free saliva-based molecular assay for rapid diagnosis of SARS-CoV-2.

Three years after the SARS-CoV-2 outbreak, there are no molecular tests that combine low-cost and straightforward sample preparation, effective sample handling, minimal reagent and disposable requirements, high sensitivity, and high throughput required for mass screening. Existing rapid molecular techniques typically sacrifice certain requirements to meet others. Yet, localized outbreaks of novel viral diseases happen daily in different parts of the world. In this context, respiratory diseases are of specific importance, as they are frequently airborne and highly contagious, with the potential for a rapid global spread. The widely accepted opinion is that another pandemic is just a question of time. To ensure that the containment efforts for the upcoming "disease X" are successful, introducing rapid, high-throughput, and highly sensitive diagnostic methods for detecting and identifying pathogens is critical. A few months into the pandemic, saliva was suggested as a diagnostic matrix for SARS-CoV-2 detection. The collection of saliva does not require swabs and is minimally invasive. In particular, extraction-free saliva-based assays require fewer reagents and disposables, and therefore are faster and cheaper, offering an appealing alternative for low-income countries. Unfortunately, current extraction-free saliva-based detection methods, such as direct RT-qPCR or isothermal amplification, have either low sensitivity or low throughput. Therefore, we believe that the presented highly sensitive ht-OMBi platform and the extraction-free saliva-based molecular assay can become an essential tool in the infectious disease monitoring toolbox.

Exploring the conformational landscapes of protein kinases: perspectives from FRET and DEER.

Conformational changes of catalytically-important structural elements are a key feature of the regulation mechanisms of protein kinases and are important for dictating inhibitor binding modes and affinities. The lack of widely applicable methods for tracking kinase conformational changes in solution has hindered our understanding of kinase regulation and our ability to design conformationally selective inhibitors. Here we provide an overview of two recently developed methods that detect conformational changes of the regulatory activation loop and αC-helix of kinases and that yield complementary information about allosteric mechanisms. An intramolecular Förster resonance energy transfer-based approach provides a scalable platform for detecting and classifying structural changes in high-throughput, as well as quantifying ligand binding cooperativity, shedding light on the energetics governing allostery. The pulsed electron paramagnetic resonance technique double electron-electron resonance provides lower throughput but higher resolution information on structural changes that allows for unambiguous assignment of conformational states and quantification of population shifts. Together, these methods are shedding new light on kinase regulation and drug interactions and providing new routes for the identification of novel kinase inhibitors and allosteric modulators.

Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species

BackgroundThe sweet potato whitefly (Bemisia tabaci) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.ResultsWe tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals.ConclusionsSelective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.

Tango-seq: overlaying transcriptomics on connectomics to identify neurons downstream of Drosophila clock neurons.

Knowing how neural circuits change with neuronal plasticity and differ between individuals is important to fully understand behavior. Connectomes are typically assembled using electron microscopy, but this is low throughput and impractical for analyzing plasticity or mutations. Here, we modified the trans-Tango genetic circuit-tracing technique to identify neurons synaptically downstream of Drosophila s-LNv clock neurons, which show 24hr plasticity rhythms. s-LNv target neurons were labeled specifically in adult flies using a nuclear reporter gene, which facilitated their purification and then single cell sequencing. We call this Tango-seq, and it allows transcriptomic data - and thus cell identity - to be overlayed on top of anatomical data. We found that s-LNvs preferentially make synaptic connections with a subset of the CNMa+ DN1p clock neurons, and that these are likely plastic connections. We also identified synaptic connections between s-LNvs and mushroom body Kenyon cells. Tango-seq should be a useful addition to the connectomics toolkit.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

Visible Light-Illuminated Gold Nanohole Arrays With Tunable On-Chip Plasmonic Sensing Properties

Since the discovery of the extraordinary optical transmission phenomenon, nanohole arrays have attracted much attention and been widely applied in sensing. However, their typical fabrication process, utilizing photolithographic top-down manufacturing technologies, has intrinsic drawbacks including the high costs, time consumption, small footprint, and low throughput. This study presented a low-cost, high-throughput, and scalable method for fabricating centimeter-scale (1×2 cm2) nanohole arrays using the improved nanosphere lithography. The large-scale close-packed polystyrene monolayers obtained by the hemispherical-depression-assisted self-assembly method were employed as colloidal masks for the nanosphere lithography, and the nanohole diameter was tuned from 233 nm to 346 nm with a fixed period of 420 nm via plasma etching. The optical properties and sensing performance of the nanohole arrays were investigated, and two transmission dips were observed due to the resonant coupling of plasmonic modes. Both dips were found to be sensitive to the surrounding environment, and the maximum bulk refractive index sensitivity was up to 162.1 nm/RIU with a 233 nm hole diameter. This study offered a promising approach for fabricating large-scale highly ordered nanohole arrays with various periods and nanohole diameters that could be used for the development of low-cost and high-throughput on-chip plasmonic sensors.

Blockchain-Based Solution for Supply Chain Data Integrity

Data plays a crucial role in today's world, guiding business decisions across various computer- assisted activities. Maintaining the integrity of data is vital, as any tampering could have serious consequences for important business decisions. This concern is particularly significant in cloud computing settings, where data owners have limited control over key aspects like physical storage and access control. Blockchain technology has emerged as an intriguing solution to address data integrity concerns. With its inherent properties, blockchain offers promising avenues for ensuring data integrity. However, some challenges need to be overcome, such as low throughput, high latency, and stability issues, which currently limit the practical implementation of blockchain-based solutions. we focus on a case study from the European SUNFISH project, which aims to develop a secure cloud federation platform for the public sector. We examine the specific data integrity requirements in cloud computing environments and identify the research questions that need to be addressed to implement blockchain-based databases effectively. We start by outlining the open research questions and the inherent difficulties associated with addressing them. Then, we propose a preliminary design for a blockchain-based database tailored to cloud computing environments. This design aims to leverage the strengths of blockchain technology while addressing the challenges unique to cloud computing. By addressing these research questions and proposing practical solutions, we aim to pave the way for the adoption of blockchain-based databases in cloud computing environments. This has the potential to enhance data integrity and security, ultimately benefiting organizations operating in the cloud.

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

Single-cell ionic current phenotyping elucidates non-canonical features and predictive potential of cardiomyocytes during automated drug experiments.

All new drugs must go through preclinical screening tests to determine their proarrhythmic potential. While these assays effectively filter out dangerous drugs, they are too conservative, often misclassifying safe compounds as proarrhythmic. In this study, we attempt to address this shortcoming with a novel, medium-throughput drug-screening approach: we use an automated patch-clamp system to acquire optimized voltage clamp (VC) and action potential (AP) data from human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at several drug concentrations (baseline, 3×, 10× and 20× the effective free plasma concentrations). With our novel method, we show correlations between INa block and upstroke slowing after treatment with flecainide or quinine. Additionally, after quinine treatment, we identify significant reductions in current during voltage steps designed to isolate If and IKs. However, we do not detect any IKr block by either drug, and upon further investigation, do not see any IKr present in the iPSC-CMs when prepared for automated patch experiments (i.e. in suspension) - this is in contrast to similar experiments we have conducted with these cells using the manual patch setup. In this study, we: (1) present a proof-of-concept demonstration of a single-cell medium-throughput drug study, and (2) characterize the non-canonical electrophysiology of iPSC-CMs when prepared for experiments in a medium-throughput setting. KEY POINTS: Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer potential as an in vitro model to study the proarrhythmic potential of drugs, but insights from these cells are often limited by the low throughput of manual patch-clamp. In this study, we use a medium-throughput automated patch-clamp system to acquire action potential (AP) and complex voltage clamp (VC) data from single iPSC-CMs at multiple drug concentrations. A correlation between AP upstroke and INa transients was identified and drug-induced changes in ionic currents found. We also characterize the substantially altered physiology of iPSC-CMs when patched in an automated system, suggesting the need to investigate differences between manual and automated patch experiments.

Nozzle Innovations That Improve Capacity and Capabilities of Multimaterial Additive Manufacturing.

Multimaterial additive manufacturing incorporates multiple species within a single 3D-printed object to enhance its material properties and functionality. This technology could play a key role in distributed manufacturing. However, conventional layer-by-layer construction methods must operate at low volumetric throughputs to maintain fine feature resolution. One approach to overcome this challenge and increase production capacity is to structure multimaterial components in the printhead prior to deposition. Here we survey four classes of multimaterial nozzle innovations, nozzle arrays, coextruders, static mixers, and advective assemblers, designed for this purpose. Additionally, each design offers unique capabilities that provide benefits associated with accessible architectures, interfacial adhesion, material properties, and even living-cell viability. Accessing these benefits requires trade-offs, which may be mitigated with future investigation. Leveraging decades of research and development of multiphase extrusion equipment can help us engineer the next generation of 3D-printing nozzles and expand the capabilities and practical reach of multimaterial additive manufacturing.