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Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti 3 C 2 T x MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels’ structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels’ physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. Machine learning-assisted robots produce MXene aerogels containing cellulose, gelatin, and glutaraldehyde, fabricating 162 compositions. Inverse design from resulting properties affords tailored compression-stable materials for Joule heating.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model’s prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database. An integrated workflow that uses robotics and machine learning to discover all-natural plastic substitutes with programmable properties is presented.

Introduction: Previous reports demonstrate racial/ethnic differences in survival for children hospitalized with cardiomyopathy and myocarditis. The impact of illness severity, a potential mechanism for disparities, has not been explored. Methods: Using the Virtual Pediatric Systems (VPS, LLC), we identified patients ≤ 18 years old admitted to the intensive care unit (ICU) for cardiomyopathy/myocarditis. Multivariate regression models were used to evaluate the association between race/ethnicity and Pediatric Risk of Mortality (PRISM 3). Multivariate logistic and competing risk regression was used to examine the relationship between race/ethnicity and mortality, CPR, and ECMO. Results: Black patients had higher PRISM 3 scores on first admission (𝛽 = 2.02, 95% CI: 0.15, 3.90). There was no difference in survival across race/ethnicity over multiple hospitalizations. Black patients were less likely to receive a heart transplant (SHR = 0.65, 95% CI: 0.45–0.92). Black and unreported race/ethnicity had higher odds of CPR on first admission (OR = 1.64, 95% CI: 1.01–2.45; OR = 2.12, 95% CI: 1.11–4.08, respectively). Conclusion: Black patients have higher severity of illness on first admission to the ICU, which may reflect differences in access to care. Black patients are less likely to receive a heart transplant. Additionally, Black patients and those with unreported race/ethnicity had higher odds of CPR, which was not mediated by severity of illness, suggesting variations in care may persist after admission.

Abstract Objectives Activated partial thromboplastin time (aPTT) is the primary test used to monitor intravenous (IV) direct thrombin inhibitors (DTIs) but has many limitations. The plasma diluted thrombin time (dTT) has shown better correlation with DTI levels than aPTT. This study compared dose-response curves for dTT and aPTT in pediatric patients receiving argatroban and bivalirudin. Methods A retrospective review of pediatric patients treated with argatroban (n = 45) or bivalirudin (n = 14) monitored with dTT and aPTT. Results The dTT assay was calibrated to report DTI concentrations in µg/mL for argatroban and bivalirudin with good analytic sensitivity and specificity. The dTT was fivefold more likely to show a stable dose-response slope than the aPTT (P < .0002; odds ratio, 4.9). For patients in whom both dTT and aPTT showed a significant correlation between dose and assay results, dTT had a higher average correlation factor compared with aPTT (P = .007). Argatroban dose-response slopes showed more inter- and intrapatient variation than bivalirudin (dose-response slope coefficient of variation, 132% vs 52%). Conclusions The dTT assay was more likely to show a stable dose response and have a stronger correlation with DTI dose than aPTT. Argatroban shows more variation in dose response than bivalirudin.

Pediatric heart failure (HF) is associated with significant morbidity and mortality. Medical treatment for pediatric HF is largely derived from adult studies. Previously, there has been no described use of dapagliflozin in pediatric HF patients. We describe our single-center experience using dapagliflozin in addition to standard HF medical therapy in 38 pediatric HF patients since January 2020. Median age was 12.2 years (interquartile range 6.2–17.5). Majority of patients had dilated cardiomyopathy (68.4%) and reduced left ventricular ejection fraction (LVEF) of 40% or less (65.8%). HF regimens commonly included sacubitril/valsartan, beta-blocker, mineralocorticoid receptor antagonist, and loop diuretic. Median follow-up from dapagliflozin initiation for the whole cohort was 130 days (IQR 76–332). Median B-type natriuretic peptide decreased significantly from 222 to 166 pg/mL at latest clinical follow-up ( P  = .04). Estimated glomerular filtration rate trended lower at latest follow-up but was not significant from baseline. There were no clinically significant changes in blood chemistries or vital signs after initiation of dapagliflozin. No patients experienced symptomatic hypoglycemia or hypovolemia. Six patients (15.8%) experienced a symptomatic urinary tract infection necessitating antibiotic treatment. In a separate analysis of 16 patients with dilated cardiomyopathy who received dapagliflozin for a median of 313 days (IQR 191–414), median LVEF increased significantly from 32 to 37.2% ( P  = .006). Dapagliflozin, when added to a background of guideline-directed medical therapy, appears well tolerated in children with HF. Larger studies are needed to evaluate safety and efficacy of dapagliflozin in this population.

Innovations in wearable electronics and soft robotics hinge significantly on the development of stretchable electrodes. However, a persistent challenge lies in balancing high stretchability, functional performance, and strain insensitivity. Conventional approaches, such as design of experiments and trial-and-error methods, often rely on time-consuming and labor-intensive experiments to navigate a vast and complex parameter space. To overcome this, we establish an integrated workflow merging robot-automated experimentation, machine learning predictions, and finite element simulations to enable the predictive design of stretchable electrodes with strain-insensitive performance. Initially, we construct an ensemble of artificial neural networks through a two-stage workflow, including feasible parameter space definition and active learning loops. Leveraging the prediction model and two-scale simulations, a microtextured stretchable nanocomposite is discovered as a strain-stable platform. Conformal deposition of a thin gold layer showcases metal-like conductivity, high resistance-insensitive stretchability, and robust durability. Furthermore, electrodeposition of Zn and MnO on gold conductors enables a stretchable Zn||MnO battery, exhibiting large elongation and strain-insensitive electrochemical performance. This machine intelligence-driven approach expedites the multi-parameter optimization of stretchable electrodes, achieving strain-invariant functionalities.

Nature provides a wealth of bio-inspiration for advanced material research. Assembling various nanomaterials into biomimetic microtextures with bioinspired functionalities has spurred increasing research interests and facilitated technological advances in various applications. In recent years, two-dimensional materials (2DMs) have emerged as important building block units in the biomimicry field due to their distinct chemical, physical, electrical, electrochemical, and catalytic properties. In this review article, various mechanically driven assembly approaches are summarized to fabricate various genealogies of biomimetic 2DM microtextures with bio-inspired multifunctionality. First, sequential deformation strategies are discussed to programmably construct higher dimensional 2DM microtextures, ranging from wrinkles/crumples (one-time deformation) to multiscale hierarchies (multiple deformations). Next, the current progress using higher dimensional 2DM microtextures to imitate different biological structures and/or induce bio-inspired multifunctionality is systematically summarized. Four showcases of bio-inspiration and biomimicry using different 2DM nanosheets are highlighted: (1) wrinkle patterns of an earthworm that spur the design of strain sensors with programmable working ranges and sensitivities, (2) wrinkle appearance of a Shar-Pei dog that motivates the fabrication of stretchable energy storage devices, (3) hierarchical scale textures of a desert lizard that inspire cation-induced gelation platforms for 2DM aerogels, and (4) wrinkle skin of an elephant that influences the development of 2DM protective skin for soft robots. Finally, challenges and future opportunities of adopting 2DM nanosheets to assemble biomimetic microstructures with synergistic functionalities are discussed.

Pain is a multidimensional perception and is modified at distinct regions of the neuroaxis. During enhanced pain, neuroplastic changes occur in the spinal and supraspinal nociceptive modulating centers and may result in a hypersensitive state termed central sensitization, which is thought to contribute to chronic pain states. Central sensitization culminates in hyperexcitability of dorsal horn nociceptive neurons resulting in increased nociceptive transmission and pain perception. This state is associated with enhanced nociceptive signaling, spinal glutamate-mediated N -methyl- d -aspartate receptor activation, neuroimmune activation, nitroxidative stress, and supraspinal descending facilitation. The nitroxidative species considered for their role in nociception and central sensitization include nitric oxide (NO), superoxide ( ), and peroxynitrite (ONOO − ). Nitroxidative species are implicated during persistent but not normal nociceptive processing. This review examines the role of nitroxidative species in pain through a discussion of their contributions to central sensitization and the underlying mechanisms. Future directions for nitroxidative pain research are also addressed. As more selective pharmacologic agents are developed to target nitroxidative species, the exact role of nitroxidative species in pain states will be better characterized and should offer promising alternatives to available pain management options.

Fontan palliation has improved survival for single ventricle patients, but long-term complications persist including cardiovascular dysfunction, neurohormonal abnormalities, and protein-losing enteropathy (PLE). Although chronic inflammation contributes to morbidity, an association between inflammation and vascular dysfunction has not been studied. We assessed inflammation and vascular function in 31 Fontan-palliated patients (52% male, median age 14.3 years), including 10 PLE+. Fontan circulation was associated with altered inflammatory cytokines (TNF-α: mean 2.5 ± 1.4 vs. 0.7 ± 0.2 pg/ml, p < 0.0001; sTNFR2: 371 ± 108 vs. 2694 ± 884 pg/ml, p < 0.0001) and vascular dysfunction [log-transformed reactive hyperemia index (lnRHI) 0.28 ± 0.19 vs. 0.47 ± 0.26, p < 0.01; augmentation index (AI) -2.9 ± 13.8 vs. -16.3 ± 12.0, p = 0.001; circulating endothelial progenitor cells (cEPCs) 5.0 ± 8.1 vs. 22.8 ± 15.9, p = 0.0002)]. Furthermore, PLE+ patients showed greater inflammation (IFN-γ 6.3 ± 2.2 vs. 11.5 ± 7.9 pg/ml, p = 0.01; sTNFR1: 1181 ± 420 vs. 771 ± 350 pg/ml, p = 0.01) and decreased arterial compliance (AI: 5.4 ± 17.1 vs. -6.8 ± 10.2, p = 0.02) than PLE- patients. Circulating EPCs, but not inflammatory cytokines, were inversely associated with arterial stiffness in Fontan patients. In conclusion, chronic inflammation and vascular dysfunction are observed after Fontan operation, with greater inflammation and arterial stiffness in Fontan patients with active PLE. However, there is no clear association between inflammatory cytokines and vascular dysfunction, suggesting these pathophysiologic processes are not mechanistically linked.

Therapies to support small infants in decompensated heart failure that are failing medical management are limited. We have used the hybrid approach, classically reserved for high-risk infants with single ventricle physiology, in patients with biventricular physiology with left ventricular failure. This approach secures systemic circulation, relieves left atrial hypertension, protects the pulmonary vasculature, and allows the right ventricle to support cardiac output. This approach can be used as a bridge to transplantation in select individuals. Infants without single ventricle congenital heart disease who were treated with the hybrid approach between 2008 and 2021 were included in analysis. Eight patients were identified. At the time of hybrid procedure, the median weight was 3.2 kg (range 2.4–3.6 kg) and the median age was 18 days (range 1–153 days). Seventy five percent were mechanically ventilated and 88% were on inotropic support. The median duration from hybrid procedure to transplant was 63 days (range 4–116 days). All patients experienced a good outcome (delisted for improvement or transplanted). The hybrid procedure is an appropriate therapeutic bridge to transplantation in a carefully selected subset of critically ill infants without single ventricle congenital heart disease in whom alternate therapies may confer increased risk for morbidity and mortality.