Bedside Ultrasonography for Arteriovenous Fistula Cannulation.

Difficulty in accessing a new arteriovenous fistula (AVF) is a common technical issue in hemodialysis patients, which often leads to interventional radiology and/or vascular surgery referral. As a consequence, the patient who needs dialysis may require a temporary dialysis catheter with its known potential complications. We present a case where bedside ultrasonography facilitated successful cannulation of a difficult AVF. Ultrasonography (US) training in this procedure may allow early cannulation of new AVFs when the venous diameter is large enough (>0.6 cm) but the fistula is too deep (>0.6 cm). Real-time, US-guided AVF cannulation may also decrease the number of failed venous punctures per hemodialysis (HD) session minimizing vessel wall damage and subsequent potential hematoma and aneurysm formation.

Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning.

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning.

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.

Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids.

Polymer-protein hybrids are intriguing materials that can bolster protein stability in non-native environments, thereby enhancing their utility in diverse medicinal, commercial, and industrial applications. One stabilization strategy involves designing synthetic random copolymers with compositions attuned to the protein surface, but rational design is complicated by the vast chemical and composition space. Here, a strategy is reported to design protein-stabilizing copolymers based on active machine learning, facilitated by automated material synthesis and characterization platforms. The versatility and robustness of the approach is demonstrated by the successful identification of copolymers that preserve, or even enhance, the activity of three chemically distinct enzymes following exposure to thermal denaturing conditions. Although systematic screening results in mixed success, active learning appropriately identifies unique and effective copolymer chemistries for the stabilization of each enzyme. Overall, this work broadens the capabilities to design fit-for-purpose synthetic copolymers that promote or otherwise manipulate protein activity, with extensions toward the design of robust polymer-protein hybrid materials.

Machine-Assisted Discovery of Chondroitinase ABC Complexes toward Sustained Neural Regeneration.

Among the many molecules that contribute to glial scarring, chondroitin sulfate proteoglycans (CSPGs) are known to be potent inhibitors of neuronal regeneration. Chondroitinase ABC (ChABC), a bacterial lyase, degrades the glycosaminoglycan (GAG) side chains of CSPGs and promotes tissue regeneration. However, ChABC is thermally unstable and loses all activity within a few hours at 37 °C under dilute conditions. To overcome this limitation, the discovery of a diverse set of tailor-made random copolymers that complex and stabilize ChABC at physiological temperature is reported. The copolymer designs, which are based on chain length and composition of the copolymers, are identified using an active machine learning paradigm, which involves iterative copolymer synthesis, testing for ChABC thermostability upon copolymer complexation, Gaussian process regression modeling, and Bayesian optimization. Copolymers are synthesized by automated PET-RAFT and thermostability of ChABC is assessed by retained enzyme activity (REA) after 24 h at 37 °C. Significant improvements in REA in three iterations of active learning are demonstrated while identifying exceptionally high-performing copolymers. Most remarkably, one designed copolymer promotes residual ChABC activity near 30%, even after one week and notably outperforms other common stabilization methods for ChABC. Together, these results highlight a promising pathway toward sustained tissue regeneration.

Point-of-care ultrasound for assessing arteriovenous fistula maturity in outpatient hemodialysis.

BACKGROUND: Point-of-care ultrasound in end-stage renal disease is on the rise. Presently the decision to cannulate an arteriovenous fistula is based on its duration since surgery and physical exam. This study examines the effects of point-of-care ultrasound on decreasing the time to arteriovenous fistula cannulation, time spent with a central venous catheter, and the complications and infections that arise. METHODS: Prospective point-of-care ultrasound patients were recruited between January 2015 and January 2018, while retrospective data (non-point-of-care ultrasound) were collected via chart review from patients who had fistula creation between November 2011 and May 2014. Patients had point-of-care ultrasound within 3 weeks after arteriovenous fistula creation and were followed for 1 year. Arteriovenous fistula cannulation was initiated when the following parameters were met: diameter > 6 mm (with no depreciable narrowing of more than 20% throughout), depth < 6 mm, and length > 6 cm. Demographic data, as well as time to cannulation and central venous catheter removal, number of infections, complications, and interventions were compared between point-of-care ultrasound and non-point-of-care ultrasound groups using unpaired t-test, chi-square, and Fisher exact test statistical analysis. RESULTS: A total of 37 patients with new arteriovenous fistulas were followed by point-of-care ultrasound compared to 29 non-point-of-care ultrasound patients. Point-of-care ultrasound patients had earlier cannulations (35.5 vs 63.3 days, p < 0.05), shorter central venous catheter duration (68.2 vs 98.3 days, p < 0.05), and less infections (12 vs 19) without differences in complication compared to the non-point-of-care ultrasound. CONCLUSION: Point-of-care ultrasound facilitates early and safe arteriovenous fistula cannulation leading to a reduction in central venous catheter time and risk of infection. Point-of-care ultrasound may also aid in earlier identification of complications and difficult cannulations.