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Why kids need to take more risks: science reveals the benefits of wild, free play
Systematic review and meta-analysis of pain management after tonsillectomy
AbstractTonsillectomy is one of the most common operations. Tonsillectomy is also one of the most painful surgical procedures. However, there is still no satisfactory standard for postoperative pain management. Four databases (Cochrane Library, Ovid Technologies, PubMed, Web of Science) were searched for the period from 1908 to 2019. The systematic literature review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were pooled using random-effects and fixed-effects models. Randomized controlled trials, reviews and meta-analyses were included. Primary outcomes were quantitative pain intensity in the first 24 h after tonsillectomy and on days 1, 3, and 7 postoperatively. The search yielded 1594 publications, of which 111 publications with 7566 patients, both children and adults, could be included. Intraoperative medication with intravenous dexamethasone significantly reduced pain (mean difference [MD] -0.42; 95% confidence interval [CI]: -0.61- -0.24). Among the local anesthetics, only the preoperative injection of levobupivacaine into the tonsillar compartment was able to provide sufficient pain reduction up to three days after tonsillectomy (MD: -1.92; 95% CI: -2.73 - -1.11). Preoperative or intraoperative administration of non-steroidal anti-inflammatory drugs (NSAIDs) significantly reduced pain (MD: -0.75; 95% CI: -0.87- -0.63). Steroids and NSAIDs are an important part of pain management after tonsillectomy.
A novel mitochondrial-related risk model for predicting prognosis and immune checkpoint blockade therapy response in uterine corpus endometrial carcinoma
Exploring the antioxidant and antimicrobial properties of five indigenous Kenyan plants used in traditional medicine
The impact of labeling automotive AI as trustworthy or reliable on user evaluation and technology acceptance
Abstract This study explores whether labeling AI as either “trustworthy” or “reliable” influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as “trustworthy” did not significantly influence people’s judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions. The study provides insights into how specific labels affect adopting certain perceptions of AI technology.
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A new-year round-up of the science stories you may have missed
Graduate-student stipends in Canada below the poverty line
Erythema nodosum, malignant melanoma and non-melanoma skin cancer in relation to inflammatory bowel disease: a Mendelian randomization study
Live birth prevalence of major congenital anomalies in the United Arab Emirates
Poplar transformation with variable explant sources to maximize transformation efficiency
Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data
Improved S-bend optical waveguide loss formula verified by experiments
Experimental investigation into the Impact of Cyclic High Low Pressure Water Immersion on the Pore structure and Mechanical properties of coal
Predecting power transformer health index and life expectation based on digital twins and multitask LSTM-GRU model
AbstractPower transformers play a crucial role in enabling the integration of renewable energy sources and improving the overall efficiency and reliability of smart grid systems. They facilitate the conversion, transmission, and distribution of power from various sources and help to balance the load between different parts of the grid. The Transformer Health Index (THI) is one of the most important indicators of ensuring their reliability and preventing unplanned outages. To this end, this study introduces a proposed new architecture called a Smart Electricity Monitoring System based on Fog Computing and Digital Twins (SEMS-FDT) for monitoring the health performance of transformers by measuring the THI rate in real time. The SEMS-FDT is specifically designed to enable the observation of the transformer’s health and performance purposes in real time. The study investigates the role of machine learning (ML) models, including traditional and ensemble methods, in predicting THI and LI (Heat Load Index) by exploring the use of the entire set of features and optimized feature subsets for prediction. To improve the forecasting prediction process and achieve optimal performance a novel multitasks LSTM_GRU model is also proposed. The experimental results demonstrate that there is a promising performance of 2.543, 0.13646, 0.0284, and 0.985 for MSE, MAE, MedAE, and R2 scores respectively. Moreover, the framework is extended by incorporating model explanations, which include global explanations, which provide insights based on the entire dataset, and local explanations, which offer instance-specific explanations. The integration of the proposed model and explainability features provides engineers with comprehensive outcomes regarding the model’s result.
Predictive modeling of air quality in the Tehran megacity via deep learning techniques
Ten-year outcomes after DMEK, DSAEK, and PK: insights on graft survival, endothelial cell density loss, rejection and visual acuity
AbstractFuchs Endothelial Corneal Dystrophy (FECD) is the most frequent indication for corneal transplantation, with Descemet membrane endothelial keratoplasty (DMEK), Descemet stripping automated endothelial keratoplasty (DSAEK), and penetrating keratoplasty (PK) being viable options. This retrospective study compared 10-year outcomes of these techniques in a large cohort of 2956 first-time keratoplasty eyes treated for FECD at a high-volume corneal transplant center in Germany. While DMEK and DSAEK provided faster visual recovery (median time to BSCVA ≥ 6/12 Snellen: DMEK 7.8 months, DSAEK 12.4 months, PK 37.9 months; cumulative probability of BSCVA ≥ 6/12 Snellen within 5 years: DMEK 93%, DSAEK 83%, PK 63%), PK surprisingly exhibited superior long-term graft survival (92% vs. 75% for DMEK and 73% for DSAEK at 10 years). Endothelial cell density (ECD) decreased significantly faster after DMEK and DSAEK, potentially contributing to their lower graft survival (10-year ECD > 1000 cells/mm2 probability: DMEK 3%, DSAEK 8%, PK 18%). DMEK demonstrated the lowest rejection rate (10% at 10 years vs. 13% for PK and 19% for DSAEK). These findings challenge the perceived superiority of endothelial keratoplasty and highlight the need for further investigation into the long-term implications of accelerated endothelial cell loss after DMEK and DSAEK.