Efficacy and safety of ketamine alone and ketamine-dexmedetomidine combination for sedation for brain computed tomography in paediatric patients with head injuries: A retrospective study.

OBJECTIVE: To compare the efficacy and safety of ketamine alone with those of ketamine-dexmedetomidine combination for sedation during brain CT in paediatric patients with head injuries. METHODS: We retrospectively analysed the data of paediatric patients who underwent sedation for brain CT at the ED. We included patients aged 6 months to 6 years with American Society of Anesthesiologists physical status I or II. The sedative protocol involved the administration of intramuscular (IM) ketamine 3 mg/kg (K), ketamine 2 mg/kg with dexmedetomidine 1.5 µg/kg (KD) or ketamine 1.5 mg/kg with dexmedetomidine 1.5 µg/kg (low-KD). The primary and secondary outcomes were sedation failure and adverse events, respectively. RESULTS: We included 77 patients; among them, 28, 23 and 26 were in the K, KD and low-KD groups, respectively. In multivariable analysis, the combination groups (KD and low-KD groups) were significantly associated with a lower possibility of sedation failure compared to the K group (adjusted odds ratio, 0.12; 95% confidence interval, 0.02-0.56). Moreover, there were no significant differences in adverse events between the groups, and the sedation-related time variables also did not significantly differ among the three groups. CONCLUSIONS: Our findings indicated that a combination of IM ketamine-dexmedetomidine provides effective sedation for paediatric patients undergoing brain CT without significant adverse events. Further research is needed to investigate the potential benefits of using lower doses of ketamine in combination.

Effects of Emotional Regulation, Resilience, and Distress Disclosure on Post-Traumatic Growth in Nursing Students.

Nursing students, who need to reflect on self, secure their identity, and be prepared as would-be nurses, can make a good use of post-traumatic growth (PTG) that can function as a catalyst for positive change even amidst this COVID-19 crisis. Emotional regulation strategies in traumatic events are key factors for successful growth, resilience is positively associated with PTG, and distress disclosure is an important factor for stress reduction. In this context, this study is a descriptive research study to identify factors influencing the PTG of nursing students, using emotional regulation, resilience, and distress disclosure as the main variables. Data were collected from 231 junior and senior students of the nursing departments of two universities, and the collected data were analyzed using the t-test, the Mann-Whitney U test, ANOVA, the Scheffé test, Pearson's correlation coefficients, and stepwise multiple regression in SPSS/WIN 26.0. Analysis of the PTG scores of the nursing students by general characteristics revealed significant differences in PTG according to the transfer status, perceived health status, and levels of satisfaction with major, hybrid-learning class, interpersonal relationship satisfaction, and clinical practice. Factors influencing PTG were identified to be resilience, reappraisal among emotional regulation strategies, satisfaction with clinical practice, and transfer, with the overall explanatory power calculated at 44%. Based on the results of this study, it is necessary to consider resilience and reappraisal, which is a sub-variable of emotional regulation strategies, in order to develop programs designed to promote PTG of nursing students in the future.

Effects of Test Location and Sample Number on the Liking Ratings of Almond Beverage and Vegan Ramen Products.

The present study investigated the effects of the evaluation environment and sample number on liking ratings within the same testing session. It comprised two experiments that determined consumer taste ratings of the following food products: (1) almond beverage and (2) vegan ramen, as rated by 322 and 287 Korean consumers, respectively. Consumers tasted each food product under either laboratory or home-used test conditions. Additionally, three levels of sample numbers were established for evaluation (almond beverage test: 1, 2, and 4; vegan ramen test: 1, 3, and 5) in each test condition. A target sample was selected for each of the two food products to directly ascertain the effects of the evaluation environment and sample number on the liking ratings. The results revealed that during the same evaluation session, the sample number affected the liking ratings of the target sample more than the testing location. Moreover, the sample number effect was product item dependent, that is, no significant change was noted in the liking ratings of the target almond beverage sample according to sample number, whereas significant differences were observed in the liking ratings of the target vegan ramen sample. Furthermore, the sample number effect was more prominent under laboratory test conditions than under home-used test conditions probably due to the serving order effect driven by hedonic contrast, carry over effect, and sensory specific satiety. The findings demonstrate that home-used tests should be recommended over laboratory tests when measuring the liking of a small number of multiple sample food items with high flavor complexity.

Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).

High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.

Exploring linearity of deep neural network trained QSM: QSMnet.

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test "linearity" of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet+, is assessed in computer-simulated lesions with an extended susceptibility range (-1.4 â€‹ppm to +1.4 â€‹ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 â€‹ppm vs. QSMnet: 0.36 â€‹ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet+ in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet+: slope â€‹= â€‹1.05, intercept â€‹= â€‹-0.03, R2 â€‹= â€‹0.93; QSMnet: slope â€‹= â€‹0.68, intercept â€‹= â€‹0.06, R2 â€‹= â€‹0.86), consolidating improved linearity in QSMnet+. This study demonstrates the importance of the trained data range in deep neural network-powered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.

Quantitative susceptibility mapping using deep neural network: QSMnet.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.

Determination of organic milk authenticity using carbon and nitrogen natural isotopes.

Natural stable isotopes of carbon and nitrogen ((12)C, (13)C, (14)N, (15)N) have abundances unique to each living creature. Therefore, measurement of the stable isotope ratio of carbon and nitrogen (δ(13)C=(13)C/(12)C, δ(15)N=(15)N/(14)N) in milk provides a reliable method to determine organic milk (OM) authenticity. In the present study, the mean δ(13)C value of OM was higher than that of conventional milk (CM), whereas the mean δ(15)N value of OM was lower than that of CM; nonetheless both δ(13)C and δ(15)N values were statistically different for the OM and CM (P<0.05). Furthermore, the values of δ(13)C and δ(15)N were found to differ statistically with the collection date and the milk brand (P<0.05). The combination of δ(13)C and δ(15)N values was more effective than either value alone in distinguishing between OM and CM. The results of the present study, which is based on preliminary data from a limited sample size and sampling period, could be highly valuable and helpful for consumers, the food industry, and/or government regulatory agencies as it can prevent fraudulent labelling of organic food. Further studies include additional analyses of other milk brands and analyses over longer time periods in order to accurately determine OM authenticity using stable isotopes of carbon and nitrogen.