Plos One
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There are no studies comparing synchronized and non-synchronized ventilation with bag-valve mask ventilation (BVMV) during cardiopulmonary resuscitation (CPR) in pediatric patients. The main aim is to compare between synchronized and non-synchronized BVMV with chest compressions (CC), and between guided and non-guided CC with a real-time feedback-device in a pediatric animal model of asphyxial cardiac arrest (CA). The secondary aim is to analyze the quality of CC during resuscitation. ⋯ The group receiving non-synchronized ventilation and guided-CC obtained significantly higher ROSC rates than the other modalities of resuscitation. Guided-CC achieved higher ROSC rates than non-guided CC. Non-synchronized ventilation was associated with better ventilation parameters, with no differences in hemodynamics or cerebral flow.
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During emergence from general anesthesia, coughing caused by the endotracheal tube frequently occurs and is associated with various adverse complications. In patients undergoing endovascular neurointervention, achieving smooth emergence from general anesthesia without coughing is emphasized since coughing is associated with intracranial hypertension. Therefore, the up-and-down method was introduced to determine the effective effect-site concentration (Ce) of remifentanil to prevent coughing in 50% and 95% (EC50 and EC95) of patients during emergence from sevoflurane anesthesia for endovascular neurointervention. ⋯ There was comparable emergence and recovery data between the cough suppression group (n = 22) and the cough group (n = 16). However, the Ce of remifentanil and total dose of remifentanil were significantly higher in the cough suppression group (P = 0.002 and P = 0.004, respectively). Target-controlled infusion of remifentanil at 1.70 ng/mL could effectively prevent extubation-related coughing in 95% of neurointervention patients, which could ensure smooth emergence.
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Comparative Study
Comparison of drug safety data obtained from the monitoring system, literature, and social media: An empirical proof from a Chinese patent medicine.
To investigate the consistency of adverse events (AEs) and adverse drug reactions (ADRs) reported in the literature, monitoring and social media data. ⋯ In our study, the most prevalent AEs and ADRs, mainly gastro-intestinal system disorders including nausea, diarrhea and vomiting, in monitoring system were largely similar with those in literature and social media. But data from different sources varied if looked at details. Multiple data sources (the monitoring system, literature and social media) should be integrated to collect safety information of interventions. The distributions of AEs and ADRs from RCTs were least similar with the data from other sources. Our empirical proof is consistent with other similar studies.
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In two studies we provide a novel investigation into the effects of monetary switching costs on choice-inertia (i.e., selection of the same option on consecutive choices). Study 1 employed a static decisions-from-feedback task and found that the introduction of, as well as larger, monetary switching costs led to increases in choice-inertia. ⋯ The effect of switching costs increasing choice-inertia for both the EV maximizing and the inferior option was replicated with little impact of the change in options values being detected. In sum, decision makers appear to be sensitive to switching costs, and this sensitivity can bias them towards inferior or superior options, revealing the good and the bad of choice-inertia.
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Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. ⋯ However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.