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Summary: Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health. Methods: We conducted a retrospective cohort study of individuals

Introduction: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, the gold standard TSPO PET quantification method includes a 90 min scan and continuous arterial blood sampling, which is challenging to perform on a routine basis. In this work, we determine what information is required for a simplified quantification approach using a machine learning algorithm. Materials and Methods: We analyzed data from 18 patients with ischemic stroke who received 0–90 min [18F]GE-180 PET as well as T1-weigted (T1w), FLAIR, and arterial spin labeling (ASL) MRI scans. During PET scans, five manual venous blood samples at 5, 15, 30, 60, and 85 min post injection (p.i.) were drawn, and plasma activity concentration was measured. Total distribution volume (VT) was calculated using Logan plot with the full dynamic PET and an image-derived input function (IDIF) from the carotid arteries. IDIF was scaled by a calibration factor derived from all the measured plasma activity concentrations. The calculated VT values were used for training a random forest regressor. As input features for the model, we used three late PET frames (60–70, 70–80, and 80–90 min p.i.), the ASL image reflecting perfusion, the voxel coordinates, the lesion mask, and the five plasma activity concentrations. The algorithm was validated with the leave-one-out approach. To estimate the impact of the individual features on the algorithm’s performance, we used Shapley Additive Explanations (SHAP). Having determined that the three late PET frames and the plasma activity concentrations were the most important features, we tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots. Results: When using all the input features, the algorithm predicted VT values with high accuracy (87.8 ± 8.3%) for both lesion and non-lesion voxels. The SHAP values demonstrated high impact of the late PET frames (60–70, 70–80, and 80–90 min p.i.) and plasma activity concentrations on the VT prediction, while the influence of the ASL-derived perfusion, voxel coordinates, and the lesion mask was low. Among all the combinations of the late PET frames and plasma activity concentrations, the 70–80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest VT estimate in the ischemic lesion. Conclusion: Reliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.

The soundscape encompasses all biological, geophysical, and anthropogenic sounds in an environment and serves as an important communication medium for biota. While macro-level observations have suggested a correlation between the acoustic and physical environments, detailed and synthesized empirical evidence establishing nuanced relationships remains limited. Establishing empirical relationships between the soundscape and the environment is essential to ensure monitoring programs are grounded in robust ecological principles. However, empirical evidence about the relationship between the soundscape, environmental attributes, and biotic communities is scarce and scattered. Here, we synthesize evidence about correlations between the soundscape and at least one environmental variable or indicator. We analyse where (location) and when (year) the relationships between the soundscape and the environment have been investigated, what study designs and methods have been used to quantify these relationships, and what relationships were found between the soundscape and environmental or biological attributes. Our key findings include (1) the relationship between the soundscape and the environment is underexplored in aquatic ecosystems – particularly freshwater; (2) riparian and underwater soundscapes are seldom monitored simultaneously; and (3) the relationships found are not tested across various climate regions, elevations, temporal and spatial scales, or study designs. Since most studies use acoustic indices to analyse their soundscape, future research needs to assess whether the relationships between the soundscape and the environment change under different audio processing methods. We also show there is potential to extend the likes of acoustic monitoring far beyond monitoring soniferous biota populations, to include monitoring ecological health, ecosystem processes, and non-soniferous biota. Finally, we present a list of key questions to increase our knowledge of the connection between the soundscape and the environment. These questions will promote a solid conceptual base for future acoustic monitoring programs that enhance the interpretation of acoustic data, develop new technologies, and consider a broader cross-system (land to water) context.

Background: Sleep dysfunction is disabling in people with Parkinson’s disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. Objective: Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. Methods: We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. Results: We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. Conclusion: Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.

Objectives: The exact mechanisms of deep brain stimulation (DBS) are still an active area of investigation, in spite of its clinical successes. This is due in part to the lack of understanding of the effects of stimulation on neuronal rhythms. Entrainment of brain oscillations has been hypothesised as a potential mechanism of neuromodulation. A better understanding of entrainment might further inform existing methods of continuous DBS, and help refine algorithms for adaptive methods. The purpose of this study is to develop and test a theoretical framework to predict entrainment of cortical rhythms to DBS across a wide range of stimulation parameters. Materials and Methods: We fit a model of interacting neural populations to selected features characterising PD patients' off-stimulation finely-tuned gamma rhythm recorded through electrocorticography. Using the fitted models, we predict basal ganglia DBS parameters that would result in 1:2 entrainment, a special case of sub-harmonic entrainment observed in patients and predicted by theory. Results: We show that the neural circuit models fitted to patient data exhibit 1:2 entrainment when stimulation is provided across a range of stimulation parameters. Furthermore, we verify key features of the region of 1:2 entrainment in the stimulation frequency/amplitude space with follow-up recordings from the same patients, such as the loss of 1:2 entrainment above certain stimulation amplitudes. Conclusion: Our results reveal that continuous, constant frequency DBS in patients may lead to nonlinear patterns of neuronal entrainment across stimulation parameters, and that these responses can be predicted by modelling. Should entrainment prove to be an important mechanism of therapeutic stimulation, our modelling framework may reduce the parameter space that clinicians must consider when programming devices for optimal benefit.

Among functional imaging methods, metabolic connectivity (MC) is increasingly used for investigation of regional network changes to examine the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD) or movement disorders. Hitherto, MC was mostly used in clinical studies, but only a few studies demonstrated the usefulness of MC in the rodent brain. The goal of the current work was to analyze and validate metabolic regional network alterations in three different mouse models of neurodegenerative diseases (β-amyloid and tau) by use of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) imaging. We compared the results of FDG-µPET MC with conventional VOI-based analysis and behavioral assessment in the Morris water maze (MWM). The impact of awake versus anesthesia conditions on MC read-outs was studied and the robustness of MC data deriving from different scanners was tested. MC proved to be an accurate and robust indicator of functional connectivity loss when sample sizes ≥12 were considered. MC readouts were robust across scanners and in awake/ anesthesia conditions. MC loss was observed throughout all brain regions in tauopathy mice, whereas β-amyloid indicated MC loss mainly in spatial learning areas and subcortical networks. This study established a methodological basis for the utilization of MC in different β-amyloid and tau mouse models. MC has the potential to serve as a read-out of pathological changes within neuronal networks in these models.

ABSTRACT: Background: Coronary computed tomography angiography (CCTA) is recommended as the first-line diagnostic imaging modality in low-to-intermediate-risk individuals suspected of stable coronary artery disease (CAD). However, CCTA exposes patients to ionizing radiation and potentially nephrotoxic contrast agents. Invasive coronary angiography is the gold-standard investigation to guide coronary revascularisation strategy; however, invasive procedures incur an inherent risk to the patient. Coronary magnetic resonance angiography (CMRA) avoids these issues. Nevertheless, clinical implementation is currently limited due to extended scanning durations, inconsistent image quality, and consequent lack of diagnostic accuracy. Several technical CMRA innovations, including advanced respiratory motion correction with 100% scan efficiency (no data rejection), fast image acquisition with motion-corrected undersampled image reconstruction, and deep-learning–based automated planning, have been implemented and now await clinical validation in multi-center trials. Methods: The objective of the image navigator–based, automated CMRA prospective multi-center study is to evaluate the diagnostic accuracy of a newly developed, state-of-the-art, standardized, and automated CMRA framework compared to CCTA in 201 patients undergoing clinical investigation for CAD. The study protocol mandates the administration of oral beta-blockers to decrease heart rate to below 60 bpm and the use of sublingual nitroglycerine spray to induce vasodilation. Additionally, the study incorporates the utilization of standardized postprocessing with sliding-thin-slab multiplanar reformatting, in combination with evaluation of the source images, to optimize the visualization of coronary artery stenosis. Discussion: If proven effective, CMRA could provide a non-invasive, needle-free, yet also clinically viable, alternative to CCTA. Trial Registration: This study is registered at ClinicalTrials.gov (NCT05473117).

Introduction: Large datasets are required to ensure reliable non-invasive glioma assessment with radiomics-based machine learning methods. This can often only be achieved by pooling images from different centers. Moreover, trained models should perform with high accuracy when applied to data from different centers. In this study, the impact of reconstruction settings and segmentation methods on radiomic features derived from amino acid and TSPO PET images of glioma patients was examined. Additionally, the ability to model and thus reduce feature differences was investigated. Methods: [18F]FET and [18F]GE-180 PET data were acquired from 19 glioma patients. For each acquisition, 10 reconstruction settings and 9 segmentation methods were included to emulate multicentric data. Statistical robustness measures were calculated before and after ComBat harmonization. Differences between features due to setting variations were assessed using Friedman test, coefficient of variation (CV) and inter-rater reliability measures, including intraclass and Spearman’s rank correlation coefficients and Fleiss’ Kappa. Results: According to Friedman analyses, most features (>60%) showed significant differences. Yet, CV and inter-rater reliability measures indicated higher robustness. ComBat resulted in almost complete harmonization (>87%) according to Friedman test and little to no improvement according to CV and inter-rater reliability measures. [18F]GE-180 features were more sensitive to reconstruction settings than [18F]FET features. Conclusions: According to Friedman test, feature distributions could be successfully aligned using ComBat. However, depending on settings, changes in patient ranks were observed for some features and could not be eliminated by harmonization. Thus, for clinical utilization it is recommended to exclude affected features.

Stroke is a severe and life-threatening disease, necessitating more research on new treatment strategies. Infiltrated T lymphocytes, an essential adaptive immune cell with extensive effector function, are crucially involved in post-stroke inflammation. Immediately after the initiation of the innate immune response triggered by microglia/macrophages, the adaptive immune response associated with T lymphocytes also participates in the complex pathophysiology of stroke and partially informs the outcome of stroke. Preclinical and clinical studies have revealed the conflicting roles of T cells in post-stroke inflammation and as potential therapeutic targets. Therefore, exploring the mechanisms that underlie the adaptive immune response associated with T lymphocytes in stroke is essential. The T-cell receptor (TCR) and its downstream signaling regulate T lymphocyte differentiation and activation. This review comprehensively summarizes the various molecules that regulate TCR signaling and the T-cell response. It covers both the co-stimulatory and co-inhibitory molecules and their roles in stroke. Because immunoregulatory therapies targeting TCR and its mediators have achieved great success in some proliferative diseases, this article also summarizes the advances in therapeutic strategies related to TCR signaling in lymphocytes after stroke, which can facilitate translation.

Objective: In preclinical research, the use of [18F]Fluorodesoxyglucose (FDG) as a biomarker for neurodegeneration may induce bias due to enhanced glucose uptake by immune cells. In this study, we sought to investigate synaptic vesicle glycoprotein 2A (SV2A) PET with [18F]UCB-H as an alternative preclinical biomarker for neurodegenerative processes in two mouse models representing the pathological hallmarks of Alzheimer’s disease (AD). Methods: A total of 29 PS2APP, 20 P301S and 12 wild-type mice aged 4.4 to 19.8 months received a dynamic [18F]UCB-H SV2A-PET scan (14.7 ± 1.5 MBq) 0–60 min post injection. Quantification of tracer uptake in cortical, cerebellar and brainstem target regions was implemented by calculating relative volumes of distribution (VT) from an image-derived-input-function (IDIF). [18F]UCB-H binding was compared across all target regions between transgenic and wild-type mice. Additional static scans were performed in a subset of mice to compare [18F]FDG and [18F]GE180 (18 kDa translocator protein tracer as a surrogate for microglial activation) standardized uptake values (SUV) with [18F]UCB-H binding at different ages. Following the final scan, a subset of mouse brains was immunohistochemically stained with synaptic markers for gold standard validation of the PET results. Results: [18F]UCB-H binding in all target regions was significantly reduced in 8-months old P301S transgenic mice when compared to wild-type controls (temporal lobe: p = 0.014; cerebellum: p = 0.0018; brainstem: p = 0.0014). Significantly lower SV2A tracer uptake was also observed in 13-months (temporal lobe: p = 0.0080; cerebellum: p = 0.006) and 19-months old (temporal lobe: p = 0.0042; cerebellum: p = 0.011) PS2APP transgenic versus wild-type mice, whereas the brainstem revealed no significantly altered [18F]UCB-H binding. Immunohistochemical analyses of post-mortem mouse brain tissue confirmed the SV2A PET findings. Correlational analyses of [18F]UCB-H and [18F]FDG using Pearson’s correlation coefficient revealed a significant negative association in the PS2APP mouse model (R = -0.26, p = 0.018). Exploratory analyses further stressed microglial activation as a potential reason for this inverse relationship, since [18F]FDG and [18F]GE180 quantification were positively correlated in this cohort (R = 0.36, p = 0.0076). Conclusion: [18F]UCB-H reliably depicts progressive synaptic loss in PS2APP and P301S transgenic mice, potentially qualifying as a more reliable alternative to [18F]FDG as a biomarker for assessment of neurodegeneration in preclinical research.

Summary: Directional migration initiated at the wound edge leads epithelia to migrate in wound healing. How such coherent migration is achieved is not well understood. Here, we used electric fields to induce robust migration of sheets of human keratinocytes and developed an in silico model to characterize initiation and propagation of epithelial collective migration. Electric fields initiate an increase in migration directionality and speed at the leading edge. The increases propagate across the epithelial sheets, resulting in directional migration of cell sheets as coherent units. Both the experimental and in silico models demonstrated vector-like integration of the electric and default directional cues at free edge in space and time. The resultant collective migration is consistent in experiments and modeling, both qualitatively and quantitatively. The keratinocyte model thus faithfully reflects key features of epithelial migration as a coherent tissue in vivo, e.g. that leading cells lead, and that epithelium maintains cell-cell junction.

Background: Beta-based adaptive Deep Brain Stimulation (aDBS) is effective in Parkinson’s disease (PD), when assessed in the immediate post-implantation phase. However, the potential benefits of aDBS in patients with electrodes chronically implanted, in whom changes due to the microlesion effect have disappeared, are yet to be assessed. Methods: To determine the acute effectiveness and side-effect profile of aDBS in PD compared to conventional continuous DBS (cDBS) and no stimulation (NoStim), years after DBS implantation, 13 PD patients undergoing battery replacement were pseudo-randomised in a crossover fashion, into three conditions (NoStim, aDBS or cDBS), with a 2-min interval between them. Patient videos were blindly evaluated using a short version of the Unified Parkinson’s Disease Rating Scale (subUPDRS), and the Speech Intelligibility Test (SIT). Results: Mean disease duration was 16 years, and the mean time since DBS-implantation was 6.9 years. subUPDRS scores (11 patients tested) were significantly lower both in aDBS (p=

Summary: Biological rhythms pervade physiology and pathophysiology across multiple timescales. Because of the limited sensing and algorithm capabilities of neuromodulation device technology to-date, insight into the influence of these rhythms on the efficacy of bioelectronic medicine has been infeasible. As the development of new devices begins to mitigate previous technology limitations, we propose that future devices should integrate chronobiological considerations in their control structures to maximize the benefits of neuromodulation therapy. We motivate this proposition with preliminary longitudinal data recorded from patients with Parkinson's disease and epilepsy during deep brain stimulation therapy, where periodic symptom biomarkers are synchronized to sub-daily, daily, and longer timescale rhythms. We suggest a physiological control structure for future bioelectronic devices that incorporates time-based adaptation of stimulation control, locked to patient-specific biological rhythms, as an adjunct to classical control methods and illustrate the concept with initial results from three of our recent case studies using chronotherapy-enabled prototypes.

Summary: This study addresses the roles of nuclear receptor corepressor 2 (NCOR2) in prostate cancer (PC) progression in response to androgen deprivation therapy (ADT). Reduced NCOR2 expression significantly associates with shorter disease-free survival in patients with PC receiving adjuvant ADT. Utilizing the CWR22 xenograft model, we demonstrate that stably reduced NCOR2 expression accelerates disease recurrence following ADT, associates with gene expression patterns that include neuroendocrine features, and induces DNA hypermethylation. Stably reduced NCOR2 expression in isogenic LNCaP (androgen-sensitive) and LNCaP-C4-2 (androgen-independent) cells revealed that NCOR2 reduction phenocopies the impact of androgen treatment and induces global DNA hypermethylation patterns. NCOR2 genomic binding is greatest in LNCaP-C4-2 cells and most clearly associates with forkhead box (FOX) transcription factor FOXA1 binding. NCOR2 binding significantly associates with transcriptional regulation most when in active enhancer regions. These studies reveal robust roles for NCOR2 in regulating the PC transcriptome and epigenome and underscore recent mutational studies linking NCOR2 loss of function to PC disease progression.

Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.

Freshwater wetlands play an important role in preserving global biodiversity and substantial ecosystem services, but they are exposed to higher pressures and threats and have received less attention in conservation planning in comparison to terrestrial ecosystems. Although a number of freshwater protected areas have been established in China, considerable freshwater conservation gaps still exist where ecologically valuable freshwater wetlands are vulnerable to anthropogenic threats, our study therefore explored systematic conservation planning specifically suited to freshwater ecosystems for the large river basins in China. A scheme of Climatic-geomorphological Classification of Freshwater Ecosystems (CGCFEs) was developed and employed as broad-scale surrogates based on their unique ecohydrological processes and biodiversity assemblages. A freshwater conservation assessment was conducted for each river basin using a complementarity-based planning framework (i.e., systematic conservation planning) and the prioritization software Marxan in consideration of the conservation targets of CGCFEs. Our research also demonstrated the need to introduce the concept of incidental gaps (IGs) in freshwater conservation planning, where the focal species and ecosystems have unintentionally been incorporated into a nature reserve with different conservation targets and thus created “conservation gaps” due to incidental and unfocused conservation efforts. The identified complete gaps (CGs) and IGs accounted for 10.56% and 8.66% of the total freshwater area respectively, implicating the need to enhance freshwater conservation efficacy by realigning existing conservation patterns to address these gaps. Our research provides a spatially explicit freshwater conservation strategy at the river basin and national scales and thus enables the central government and wetland resource managers to set ecologically meaningful spatial conservation priorities for freshwater ecosystems within those major large river basins. In addition, the methodology of using CGCFEs can be replicated for large-scale freshwater conservation planning elsewhere and promote freshwater conservation efficiency by capturing irreplaceable freshwater habitats.

Imbalanced neuronal excitability homeostasis is commonly observed in patients with fragile X syndrome (FXS) and the animal model of FXS, the Fmr1 KO. While alterations of neuronal intrinsic excitability and synaptic activity at the steady state in FXS have been suggested to contribute to such a deficit and ultimately the increased susceptibility to seizures in FXS, it remains largely unclear whether and how the homeostatic response of neuronal excitability following extrinsic challenges is disrupted in FXS. Our previous work has shown that the acute response following induction of endoplasmic reticulum (ER) stress can reduce neural activity and seizure susceptibility. Because many signaling pathways associated with ER stress response are mediated by Fmr1, we asked whether acute ER stress–induced reduction of neural activity and seizure susceptibility are altered in FXS. Our results first revealed that acute ER stress can trigger a protein synthesis–dependent prevention of neural network synchronization in vitro and a reduction of susceptibility to kainic acid–induced seizures in vivo in wild-type but not in Fmr1 KO mice. Mechanistically, we found that acute ER stress–induced activation of murine double minute-2 (Mdm2), ubiquitination of p53, and the subsequent transient protein synthesis are all impaired in Fmr1 KO neurons. Employing a p53 inhibitor, Pifithrin-α, to mimic p53 inactivation, we were able to blunt the increase in neural network synchronization and reduce the seizure susceptibility in Fmr1 KO mice following ER stress induction. In summary, our data revealed a novel cellular defect in Fmr1 KO mice and suggest that an impaired response to common extrinsic challenges may contribute to imbalanced neuronal excitability homeostasis in FXS.

Background: In Alzheimer`s disease (AD), regional heterogeneity of β-amyloid burden and microglial activation of individual patients is a well-known phenomenon. Recently, we described a high incidence of inter-individual regional heterogeneity in terms of asymmetry of plaque burden and microglial activation in β-amyloid mouse models of AD as assessed by positron-emission-tomography (PET). We now investigate the regional associations between amyloid plaque burden, microglial activation, and impaired spatial learning performance in transgenic mice in vivo. Methods: In 30 AppNL-G-F mice (15 female, 15 male) we acquired cross-sectional 18 kDa translocator protein (TSPO-PET, 18F-GE-180) and β-amyloid-PET (18F-florbetaben) scans at ten months of age. Control data were obtained from age- and sex-matched C57BI/6 wild-type mice. We assessed spatial learning (i.e. Morris water maze) within two weeks of PET scanning and correlated the principal component of spatial learning performance scores with voxel-wise β-amyloid and TSPO tracer uptake maps in AppNL-G-F mice, controlled for age and sex. In order to assess the effects of hemispheric asymmetry, we also analyzed correlations of spatial learning performance with tracer uptake in bilateral regions of interest for frontal cortex, entorhinal/piriform cortex, amygdala, and hippocampus, using a regression model. We tested the correlation between regional asymmetry of PET biomarkers with individual spatial learning performance. Results: Voxel-wise analyses in AppNL-G-F mice revealed that higher TSPO-PET signal in the amygdala, entorhinal and piriform cortices, the hippocampus and the hypothalamus correlated with spatial learning performance. Region-based analysis showed significant correlations between TSPO expression in the right entorhinal/piriform cortex and the right amygdala and spatial learning performance, whereas there were no such correlations in the left hemisphere. Right lateralized TSPO expression in the amygdala predicted better performance in the Morris water maze (β = -0.470, p = 0.013), irrespective of the global microglial activation and amyloid level. Region-based results for amyloid-PET showed no significant associations with spatial learning. Conclusion: Elevated microglial activation in the right amygdala-entorhinal-hippocampal complex of AppNL-G-F mice is associated with better spatial learning. Our findings support a protective role of microglia on cognitive function when they highly express TSPO in specific brain regions involved in spatial memory.

Introduction: Adaptive deep brain stimulation (aDBS) has been applied in Parkinson's disease (PD), based on the presence of brief high-amplitude beta (13–35 Hz) oscillation bursts in the subthalamic nucleus (STN), which correlate with symptom severity. Analogously, average low-frequency (LF) oscillatory power (4–12 Hz) in the internal globus pallidus (GPi) correlates with dystonic symptoms and might be a suitable physiomarker for aDBS in dystonia. Characterization of pallidal bursts could facilitate the implementation of aDBS in the GPi of PD and dystonia patients. Objective and methods: We aimed to describe the bursting behaviour of LF and beta oscillations in a cohort of five GPi-DBS PD patients and compare their amplitude and length with those of a cohort of seven GPi-DBS dystonia, and six STN-DBS PD patients (n electrodes = 34). Furthermore, we used the information obtained to set up aDBS and test it in the GPi of both a dystonia and a PD patient (n = 2), using either LF (dystonia) or beta oscillations (PD) as feedback signals. Results: LF and beta oscillations in the dystonic and parkinsonian GPi occur as phasic, short-lived bursts, similarly to the parkinsonian STN. The amplitude profile of such bursts, however, differed significantly. Dystonia showed higher LF burst amplitudes, while PD presented higher beta burst amplitudes. Burst characteristics in the parkinsonian GPi and STN were similar. Furthermore, aDBS applied in the GPi was feasible and well tolerated in both diseases. Conclusion: Pallidal LF and beta burst amplitudes have different characteristics in PD and dystonia. The presence of increased burst amplitudes could be employed as feedback for GPi-aDBS.

Purpose: Designing effective driving safety interventions is imperative as traffic crashes are the leading cause of injury and death for adolescents. Using concepts from the Integrated Behavioral Model, we investigated adolescents' attitudes and intentions towards engaging in safe driving practices and using smartphone-based driving safety technology. Methods: Two-hundred and seven adolescents aged 14–18 (M = 16.1, SD = 0.8) completed a safe driving survey. A path model testing the associations between individual scores of attitudes, perceived norms, and perceived behavioral control with intentions controlling for demographic covariates was conducted. Results: Greater intentions to drive safely was associated with greater perceived norms from family and peers (β = 0.75, p

The Grüneisen parameter is an essential factor in biomedical photoacoustic (PA) diagnostics. In most PA imaging applications, the variation of the Grüneisen parameter with tissue type is insignificant. This is not the case for PA imaging and characterization of lipids, as they have a very distinct Grüneisen parameter compared with other tissue types. One example of PA applications involving lipids is the imaging and characterization of atherosclerotic plaques. Intravascular photoacoustic (IVPA) imaging is a promising diagnostic tool that can evaluate both plaque severity and composition. The literature for IVPA has mainly focused on using the difference in absorption coefficients between plaque components and healthy arterial tissues. However, the Grüneisen parameters for lipids and their behavior with temperature have not been well established in the literature. In this study we employ frequency-domain photoacoustic measurements to estimate the Grüneisen parameter by virtue of the ability of this modality to independently measure both the absorption coefficient and the Grüneisen parameter through the use of the phase channel. The values of the Grüneisen parameters of some lipids are calculated as functions of temperature in the range 25–45 °C. Keywords: Grüneisen parameter, Intravascular imaging, Ultrasound, Atherosclerosis, Frequency-domain biomedical photoacoustics

Data in this article show radioligand uptake (to gamma counter and positron-emission-tomography) as well as polymerase chain reaction analyses of 18 kDa translocator protein (TSPO) quantification. We confirmed specificity of [18F]GE180 binding of rodent brain and myocardium by blocking experiments with prior application of non-radioactive GE180, using dynamic in vivo positron-emission-tomography and ex vivo gamma counter measurements. Expression of TSPO was compared between rodent brain and myocardium by quantitative polymerase chain reaction.

Excessive activation of inflammatory signaling pathways seems to be a hallmark of epileptogenesis. Positron emission tomography (PET) allows in vivo detection of brain inflammation with spatial information and opportunities for longitudinal follow-up scanning protocols.Here, we assessed whether molecular imaging of the 18kDa translocator protein (TSPO) can serve as a biomarker for the development of epilepsy. Therefore, brain uptake of [18F]GE-180, a highly selective radioligand of TSPO, was investigated in a longitudinal PET study in a chronic rat model of temporal lobe epilepsy. Analyses revealed that the influence of the epileptogenic insult on [18F]GE-180 brain uptake was most pronounced in the earlier phase of epileptogenesis. Differences were evident in various brain regions during earlier phases of epileptogenesis with [18F]GE-180 standardized uptake value enhanced by 2.1 to 2.7fold. In contrast, brain regions exhibiting differences seemed to be more restricted with less pronounced increases of tracer uptake by 1.8–2.5fold four weeks following status epilepticus and by 1.5–1.8fold in the chronic phase. Based on correlation analysis, we were able to identify regions with a predictive value showing a correlation with seizure development. These regions include the amygdala as well as a cluster of brain areas. This cluster comprises parts of different brain regions, e.g. the hippocampus, parietal cortex, thalamus, and somatosensory cortex.In conclusion, the data provide evidence that [18F]GE-180 PET brain imaging can serve as a biomarker of epileptogenesis. The identification of brain regions with predictive value might facilitate the development of preventive concepts as well as the early assessment of the interventional success. Future studies are necessary to further confirm the predictivity of the approach. Keywords: Positron emission tomography, Temporal lobe epilepsy, [18F]GE-180, TSPO, Neuroinflammation, Predictive cluster VOI

The development of macrophages requires signaling through the lineage-restricted receptor Csf1r. Macrophage-restricted expression of transgenic reporters based upon Csf1r requires the highly conserved Fms-intronic regulatory element (FIRE). We have created a lentiviral construct containing mouse FIRE and promoter. The lentivirus is capable of directing macrophage-restricted reporter gene expression in mouse, rat, human, pig, cow, sheep, and even chicken. Rat bone marrow cells transduced with the lentivirus were capable of differentiating into macrophages expressing the reporter gene in vitro. Macrophage-restricted expression may be desirable for immunization or immune response modulation, and for gene therapy for lysosomal storage diseases and some immunodeficiencies. The small size of the Csf1r transcription control elements will allow the insertion of large “cargo” for applications in gene therapy and vaccine delivery.

Background: Celiac disease (CD) is a common autoimmune disorder. Efficient identification of patients may improve chronic management of the disease. Prior studies have shown searching International Classification of Diseases-9 (ICD-9) codes alone is inaccurate for identifying patients with CD. In this study, we developed automated classification algorithms leveraging pathology reports and other clinical data in Electronic Health Records (EHRs) to refine the subset population preselected using ICD-9 code (579.0). Materials and Methods: EHRs were searched for established ICD-9 code (579.0) suggesting CD, based on which an initial identification of cases was obtained. In addition, laboratory results for tissue transglutaminse were extracted. Using natural language processing we analyzed pathology reports from upper endoscopy. Twelve machine learning classifiers using different combinations of variables related to ICD-9 CD status, laboratory result status, and pathology reports were experimented to find the best possible CD classifier. Ten-fold cross-validation was used to assess the results. Results: A total of 1498 patient records were used including 363 confirmed cases and 1135 false positive cases that served as controls. Logistic model based on both clinical and pathology report features produced the best results: Kappa of 0.78, F1 of 0.92, and area under the curve (AUC) of 0.94, whereas in contrast using ICD-9 only generated poor results: Kappa of 0.28, F1 of 0.75, and AUC of 0.63. Conclusion: Our automated classification system presented an efficient and reliable way to improve the performance of CD patient identification.

Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

Background: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. Methods: In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. Results: Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. Conclusions: These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

This article describes a dataset used to calibrate a finite element model of a thick circular hollow section (CHS) with varying d/t (diameter to thickness) ratio under cyclic loading which may be used as a computational model validation benchmark by researchers working on similar problems in structural and mechanical engineering. The test data consists of seven cold-formed S335J2H steel CHS tube specimens tested to buckling failure in low-cycle fatigue under a three-point bending arrangement, instrumented with discrete strain gauges, displacement transducers and string potentiometers together with continuous surface deformation fields obtained by two pairs of digital image correlation (DIC) cameras. ‘Half-cycle’ material data from the uniaxial tensile testing of dog-bone coupons is also provided. Comparisons between measured and simulated entities such as midspan forces, moments, displacements and mean curvatures can be obtained with MATLAB processing scripts. Complete ABAQUS model input files are also provided to aid in benchmarking.