In [1], the dose estimation accuracy using the alternative baseline method under modulated tube current was not correctly calculated due to an unintentional simulation error.
In [1], the dose estimation accuracy using the alternative baseline method under modulated tube current was not correctly calculated due to an unintentional simulation error.
Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for probing the momentum-resolved single-particle spectral function of materials. Historically, in situ magnetic fields have been carefully avoided ...
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Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal cond...
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Phonons, or vibrational quanta, are behind some of the most fundamental physical phenomena in solids, including superconductivity, Raman processes, and broken-symmetry phases. It is therefore of fundamental importance...
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Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing the cortex-basal ganglia-thalamus network. Currently, deep brain stimulation (DBS) is used as an effective therapy for reducing the excessive power and synchrony observed in brain circuits, thereby ameliorating the PD symptoms. In the present study, we used a biologically plausible computational model of cortex-basal ganglia-thalamus network, which represents both healthy and PD conditions, to systematically investigate the effects of DBS frequency on the model outputs. DBS was applied to the subthalamic nucleus (STN) at different stimulation frequencies (40Hz to 300Hz). Spike train variability and spectral power in the 7-35Hz band were measured from the several nuclei represented in the model. In addition, the magnitude squared coherence between the nuclei was assessed. An increased DBS frequency tended to produce interspike intervals (ISIs) with higher variability as compared to PD condition. Also, DBS significantly reduced the alpha-beta power for almost all brain nuclei. The median of the magnitude-squared coherence matrix (which is a metric of global network synchronization) decreased significantly with the increase of DBS frequency.
Background: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnos...
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Background: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. Objective: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. Methods: This study was conducted at KK Women’s and Children’s Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years;mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. Results: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection p
Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring...
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Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data wit...
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Purpose To develop and characterize individualized dose and quality measures at organ level compared to their generic counterparts across a clinical CT dataset. Materials and methods The study included 9801 chest-abdo...
Purpose To develop and characterize individualized dose and quality measures at organ level compared to their generic counterparts across a clinical CT dataset. Materials and methods The study included 9801 chest-abdomen-pelvis and abdomen-pelvis CT exams (7,763 patients, mean age, 56 ± 17 years; 4113 women) representing 20 unique protocols. For each exam, patient-specific organ dose of all radiosensitive organs was estimated using a validated method by generating personalized computational phantoms and Monte Carlo simulations. Effective dose ( E OD ) was calculated by weighted sum of the organ doses. Liver dose, O D liver , noise in the liver, N liver , and observer model detectability, d ′ , were assessed within the liver as examples of individualized, organ-based image assessment measurements. The organ-based measurements ( O D liver , E OD , and N liver ) were compared to their generic counterparts: ssize-specific ddose estimates (SSDE), effective dose based on dose length product ( E DLP ), and whole-body noise ( N global ), respectively. Results Generic dose values were substantially higher than individualized estimates for SSDE vs. O D liver (median of all exams: 51.2 %, p < 0.001) and E DLP vs. ED OD (median: 41.0 %, p < 0.001). N global was generally lower than N liver (median: −7.2 %, p < 0.001). The correlation relationships of E OD and d ′ were substantially varied ( R 2 range: 0–0.5) for different patient sizes and scan parameters. Conclusions Demonstrated across a population of exams, individualized organ-based measurements of dose and quality are feasible. Generic measures cannot fully represent individualized organ-based values. The correlation relationships between individualized dose and image quality values varies for different vendors and protocols, implying imaging optimization is best when done semi-independently for each factor using individualized measurements.
Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data wit...
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