Impact of Sampling Time Variability on Tacrolimus Dosage Regimen in Pediatric Primary Nephrotic Syndrome: Single-Center, Prospective, Observational Study

Background: Tacrolimus (TAC) is an important immunosuppressant for children with primary nephrotic syndrome (PNS). The relationship between sampling time variability in TAC therapeutic drug monitoring and dosage regimen in such children is unknown.Methods: In this single-center, prospective, observational study, we evaluated the sampling time variability, concentration error (CE), relative CE (RCE), and the impact of the sampling time on TAC dosage regimens in 112 PNS children with 188 blood samples. Nominal concentration (Cnom) at 12-h after last TAC dose was simulated based on observed concentration (Cobs) via previously published pharmacokinetic models, then CE and RCE were calculated. Inappropriate dosing adjustments resulting from deviated sampling time were evaluated based on a target Cnom of 5–10 ng/ml.Results: We found that 32 and 68% of samples were respectively collected early (2–180 min) and delayed (4–315 min). Furthermore, 24, 22, 22, and 32% of blood samples were drawn within deviations of ≤0.5, 0.5–1, 1–2, and >2 h, respectively, and 0.3 ng/ml of CE and 6% RCE per hour of deviation occurred. Within a deviation of >2 h, 25% of Cobs might result in inappropriate dosing adjustments. Early and delayed sampling might result in inappropriate dose holding or unnecessary dose increments, respectively, in patients with Cobs ∼ 5 ng/ml.Conclusions: Variable sampling time might lead to inappropriate dosing adjustment in a minority of children with PNS, particularly those with TAC Cobs ∼ 5 ng/ml collected with a deviation of >2 h.

Electromagnetic Exposure of Personnel Involved in Cardiac MRI Examinations in 1.5T, 3T and 7T Scanners

(1) Background: It has been hypothesised that a significant increase in the use of cardiac magnetic resonance (CMR), for example, when examining COVID-19 convalescents using magnetic resonance imaging (MRI), has an influence the exposure profiles of medical personnel to static magnetic fields (STmf). (2) Methods: Static exposure to STmf (SEmf) was recorded during activities that modelled performing CMR by radiographers. The motion-induced time variability of that exposure (TVEmf) was calculated from SEmf samples. The results were compared with: (i) labour law requirements; (ii) the distribution of vertigo perception probability near MRI magnets; and (iii) the exposure profile when actually performing a head MRI. (3) Results: The exposure profiles of personnel managing 42 CMR scans (modelled using medium (1.5T), high (3T) and ultrahigh (7T) field scanners) were significantly different than when managing a head MRI. The majority of SEmf and TVEmf samples (up to the 95th percentile) were at low vertigo perception probability (SEmf < 500 mT, TVEmf < 600 mT/s), but a small fraction were at medium/high levels; (4) Conclusion: Even under the “normal working conditions” defined for SEmf (STmf < 2T) by labour legislation (Directive 2013/35/EC), increased CMR usage increases vertigo-related hazards experienced by MRI personnel (a re-evaluation of electromagnetic safety hazards is suggested in the case of these or similar changes in work organisation).

Clarifying the relationship between trait mindfulness and objective performance

The present study focused on the relationship between trait mindfulness and the outcome component of performance, evaluated with objective indicators. In particular, four objective performance indicators were studied: accuracy, reaction time, variability in reaction times, and detection of unexpected stimuli. Because attention and awareness have been described as core components of mindfulness, and previous research suggests that mindfulness is associated with improved attention skills, this study predicted that trait mindfulness would be positively related to objective indicators of high performance (accuracy, detection of unexpected stimuli) and negatively related to objective indicators of low performance (reaction time, variability in reaction time), on an attention task. Moreover, the study predicted that the relationship between trait mindfulness and objective performance would be modulated by task complexity. University students (139) completed mindfulness, intelligence, and personality questionnaires and completed an adapted Stroop task (Stroop, 1935) in E-prime 2 software. To test our hypotheses, we performed hierarchical multiple regression analyses in SPSS. Our results revealed that trait mindfulness is not related to objective indicators of performance in an attention task, except for the detection of unexpected stimuli. Going further with our analyses, we also confirmed the important role of intelligence in performance outcomes. Finally, task complexity was not playing a moderator role in the relationship between mindfulness and objective performance. Our research contributes to the literature on mindfulness and objective performance, providing empirical evidence for the relationship between trait mindfulness and the detection of unexpected stimuli. Study limitations and avenues for future research are discussed.

In-Season Nutrition Strategies and Recovery Modalities to Enhance Recovery for Basketball Players: A Narrative Review

Basketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players’ recovery for training and competition throughout the season.

GCN based virtual resource allocation scheme for power internet of things

With the construction of smart grid, increasing number of smart devices will be connected to the power communication network. Therefore, how to allocate the resources of access devices has become an urgent problem to be solved in smart grid. However, due to the diversity and time-variability of access devices at the edge of the power grid, such dynamic changes may lead to untimely and unbalanced resource allocation of the power grid and additional system overhead, resulting in reducing the efficiency of power grid operation, unbalanced workload and other problems. In this paper, a grid resource allocation scheme based on Gauss optimization is proposed. The grid virtualization application resources are managed through three main steps: decomposition, combination and exchange, so as to realize the reasonable allocation of grid resources. Considering the time-variability of the grid topology and the diversity of the access device, the computational complexity of the traditional data analysis model is too high to be suitable for time-sensitive power network structure. This paper proposes an MPNN framework combined with the Graph Convolutional Network (GCN) to enhance the calculation efficiency and realize the rapid allocation of network resources. Since the smart gateway connected by the grid terminal has certain computation ability, the cloud computing used in distribution model in deep learning to find the optimal solution can be distributed in the cloud and edge computing gateway. In this way, The entire electricity network can efficiently manage and orchestrate virtual services to maximize the utility of grid virtual resources. Furthermore, this paper also adopt the GG-NN (Gated Graph Neural Network) which is based on the MPNN framework in the training. Finally, we carry out simulation for the Gauss optimization scheme and the MPNN-based scheme to verify that the convolutional diagram neural network is suitable for virtual resource allocating in multi-access power Internet-of –Things (IoTs).

Contrast implementation affects phonetic variability: A case study of Hindi and English stops

There is a large body of work in phonetics and phonology demonstrating sources and structure of acoustic variability, showing that variability in speech production is not random. This paper examines the question of how variability itself varies across languages and speakers, arguing that differences in extent of variability are also systematic. A classic hypothesis from Dispersion Theory (Lindblom, 1986) posits a relationship between extent of variability and phoneme inventory size, but this has been shown to be inadequate for predicting differences in phonetic variability. I propose an alternative hypothesis, Contrast-Dependent Variation, which considers cue weight of individual phonetic dimensions rather than size of phonemic inventories. This is applied to a case study of Hindi and American English stops and correctly predicts more variability in English stop closure voicing relative to Hindi, but similar amounts of lag time variability in both languages. In addition to these group-level between- language differences, the results demonstrate how patterns of individual speaker differences are language-specific and conditioned by differences in phonological contrast implementation.

Prior Prognostic Expectations as a Potential Predictor in Neurofeedback Training

The present study evaluated whether subjects’ expectations and neurofeedback training performance predict neurofeedback efficacy in cognitive training by controlling both factors as statistical variables. Twenty-two psychology students underwent neurofeedback training, employing beta/theta protocol to enhance beta1 power (13–21 Hz) and suppress theta (4–7 Hz) power. Neurofeedback efficacy was evaluated by behavioral components measured on pre-tests and post-tests employing a visual continuous performance task. The results revealed a significant interaction term between change in reaction time from pre-test to post-test and expectancy effect, indicating that participants with high prognostic expectations showed better improvement in reaction time scores. The data did not reveal that actual neurofeedback performance influenced the post-test measurements of the visual continuous performance task. No significant differences were found for reaction time variability, omission, or commission errors. Possible factors contributing to the results are discussed, and directions for future research are suggested.