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Abstract Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal datasets. Here, we present a generalizable data framework that was implemented for large animal research at the Children’s Hospital of Philadelphia to address this technological gap. The presented framework culminates in a custom, interactive dashboard for exploratory analysis and filtered dataset download. Compared with existing clinical and preclinical data management solutions, the presented framework better enables management of various data types (single measure, repeated measures, time series, and imaging), integration of datasets for comparison across experimental models, cohorts, and groups, and facilitation of predictive modeling from integrated datasets. Further, a predictive model development use case demonstrated utilization and value of the data framework. The general outline of a preclinical data framework presented here can serve as a template for other translational research labs that generate heterogeneous datasets and require a dynamic platform that can easily evolve alongside their research.

Traumatic brain injury (TBI) is associated with high mortality and morbidity in children and adults. Unfortunately, there is no effective management for TBI in the acute setting. Rodent studies have shown that xenon, a well-known anesthetic gas, can be neuroprotective when administered post-TBI. Gas inhalation therapy, however, the approach typically used for administering xenon, is expensive, inconvenient, and fraught with systemic side effects. Therapeutic delivery to the brain is minimal, with much of the inhaled gas cleared by the lungs. To bridge major gaps in clinical care and enhance cerebral delivery of xenon, this study introduces a novel xenon delivery technique, utilizing microbubbles, in which a high impulse ultrasound signal is used for targeted cerebral release of xenon. Briefly, an ultrasound pulse is applied along the carotid artery at the level of the neck on intravenous injection of xenon microbubbles (XeMBs) resulting in release of xenon from microbubbles into the brain. This delivery technique employs a hand-held, portable ultrasound system that could be adopted in resource-limited environments. Using a high-fidelity porcine model, this study demonstrates the neuroprotective efficacy of xenon microbubbles in TBI for the first time.

Purpose: Microvascular imaging ultrasound (MVI) can detect slow blood flow in small-caliber cerebral vessels. This technology may help assess flow in other intracranial structures, such as the ventricular system. In this study, we describe the use of MVI for characterizing intraventricular cerebrospinal fluid (CSF) flow dynamics in infants. Materials and methods: We included infants with brain ultrasound that had MVI B-Flow cine clips in the sagittal plane. Two blinded reviewers examined the images, dictated a diagnostic impression, and identified the third ventricle, cerebral aqueduct, fourth ventricle, and CSF flow direction. A third reviewer evaluated the discrepancies. We evaluated the association of visualization of CSF flow as detectable with MVI, with the diagnostic impressions. We also assessed the inter-rater reliability (IRR) for detecting CSF flow. Results: We evaluated 101 infants, mean age 40 ± 53 days. Based on brain MVI B-Flow, a total of 49 patients had normal brain US scans, 40 had hydrocephalus, 26 had intraventricular hemorrhage (IVH), and 14 had hydrocephalus+IVH. Using spatially moving MVI signal in the third ventricle, cerebral aqueduct, and fourth ventricle as the criteria for CSF flow, CSF flow was identified in 10.9% (n = 11), 15.8% (n = 16), and 16.8% (n = 17) of cases, respectively. Flow direction was detected in 19.8% (n = 20) of cases; 70% (n = 14) was caudocranial, 15% (n = 3) was craniocaudal, and 15% (n = 3) bidirectional, with IRR = 0.662, p < 0.001. Visualization of CSF flow was significantly associated with the presence of IVH alone (OR 9.7 [3.3–29.0], p < 0.001) and IVH+hydrocephalus (OR 12.4 [3.5–440], p < 0.001), but not with hydrocephalus alone (p = 0.116). Conclusion: This study demonstrates that MVI can detect CSF flow dynamics in infants with a history of post-hemorrhagic hydrocephalus with a high IRR.