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In the ablation model, long interpulse delays lowered the effective frequency of burst waveforms, modulating field redistribution and reducing heat production. Finally, we demonstrate mathematically that variable delays allow for increased voltages and larger ablations with similar extents of excitation as symmetric waveforms.
Interphase and interpulse delays play a significant role in outcomes resulting from H-FIRE treatment.
Waveforms with short interphase delays (d1) and extended interpulse delays (d2) may improve therapeutic efficacy of H-FIRE as it emerges as a clinical tissue ablation modality. Index Termsbipolar pulses, muscle contraction, tumor ablation, cardiac ablation, pulsed electric field.
Waveforms with short interphase delays (d1) and extended interpulse delays (d2) may improve therapeutic efficacy of H-FIRE as it emerges as a clinical tissue ablation modality. Index Termsbipolar pulses, muscle contraction, tumor ablation, cardiac ablation, pulsed electric field.Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.Continuous glucose monitoring (CGM) enables improvements in diabetes treatment by providing frequent temporal information on glycemia, and prediction of future glucose concentration (GC) trends. The accurate prediction of the future GC trajectory is important for making meal, activity and insulin dosing decisions. Glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions by integrating input features derived from supplemental physiological variables measured from a wearable device. We use a wristband that is conducive of use by free-living ambulatory people. The readily obtained biosignals are used to generate new quantifiable input features for PA and APS. Machine learning techniques are used to estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated for the first time as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments are used to illustrate the improvement in GC prediction accuracy. The average mean absolute error (MAE) of one-hour-ahead GC predictions decreases from 35.1 to 31.9 mg/dL (p-value=0.01) for testing data with the inclusion of PA information. The average MAE of one-hour-ahead GC predictions decreases from 16.9 to 14.2 mg/dL (p-value=0.006) for testing data with the inclusion of PA and APS information.
To evaluate whether non-invasive knee sound measurements can provide information related to the underlying structural changes in the knee following meniscal tear. These changes are explained using an equivalent vibrational model of the knee-tibia structure.
First, we formed an analytical model by modeling the tibia as a cantilever beam with the fixed end being the knee. The knee end was supported by three lumped components with features corresponding with tibial stiffnesses, and meniscal damping effect. Second, we recorded knee sounds from 46 healthy legs and 9 legs with acute meniscal tears (n = 34 subjects). We developed an acoustic event (click) detection algorithm to find patterns in the recordings, and used the instrumental variable continuous-time transfer function estimation algorithm to model them.
The knee sound measurements yielded consistently lower fundamental mode decay rate in legs with meniscal tears (1613 s^(-1)) compared to healthy legs (182128 s^(-1)), p<0.05. When we performed an intra-subject analysis of the injured versus contralateral legs for the 9 subjects with meniscus tears, we observed significantly lower natural frequency and damping ratio (first mode results for healthy f_n1=11544 Hz,_1=0.0980.022, injured f_n1=6710 Hz, _1=0.0360.029) for the first three vibration modes (p<0.05). These results agreed with the theoretical expectations gleaned from the vibrational model.
This combined analytical and experimental method improves our understanding of how vibrations can describe the underlying structural changes in the knee following meniscal tear, and supports their use as a tool for future efforts in non-invasively diagnosing meniscal tear injuries.
This combined analytical and experimental method improves our understanding of how vibrations can describe the underlying structural changes in the knee following meniscal tear, and supports their use as a tool for future efforts in non-invasively diagnosing meniscal tear injuries.Since the first wave of coronavirus disease in March 2020, citizens and permanent residents returning to New Zealand have been required to undergo managed isolation and quarantine (MIQ) for 14 days and mandatory testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). PP242 cost As of October 20, 2020, of 62,698 arrivals, testing of persons in MIQ had identified 215 cases of SARS-CoV-2 infection. Among 86 passengers on a flight from Dubai, United Arab Emirates, that arrived in New Zealand on September 29, test results were positive for 7 persons in MIQ. These passengers originated from 5 different countries before a layover in Dubai; 5 had negative predeparture SARS-CoV-2 test results. To assess possible points of infection, we analyzed information about their journeys, disease progression, and virus genomic data. All 7 SARS-CoV-2 genomes were genetically identical, except for a single mutation in 1 sample. Despite predeparture testing, multiple instances of in-flight SARS-CoV-2 transmission are likely.