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Neuroimaging outcomes in suspected papilledema
Abstract Papilledema due to elevated intracranial pressure may indicate life-threatening conditions such as intracranial mass lesions or cerebral sino-venous thrombosis. This bicentric retrospective cohort study evaluated neuroimaging outcomes and clinical predictors in adults presenting with suspected papilledema to emergency departments between 2009 and 2022. Diagnostic outcomes were categorized as confirmed papilledema (due to idiopathic intracranial hypertension, space-occupying lesions, hydrocephalus or secondary intracranial causes) or papilledema ruled out. Clinical features, visual acuity, and frequency of focal neurological deficits were compared between groups. Diagnostic yield and number needed to scan (NNS) for CT and MRI were calculated, and univariable logistic regression was performed to identify predictors of secondary intracranial causes. Among 225 patients (mean age 43.0 ± 16.8 years; 57% female), papilledema was confirmed in 124 (55%), including 44 (35.5%) with secondary intracranial causes. Papilledema was excluded in 73 patients (32.4%). Primary CT and MRI identified secondary pathology in 13.3% (NNS 7.5) and 16.7% (NNS 6) of patients, respectively. Diplopia, nausea/vomiting, and focal neurological deficits were associated with secondary causes but were not consistently present. Secondary intracranial pathology is common in suspected papilledema. Given the limited discriminatory value of clinical features, timely neuroimaging should be performed in the diagnostic evaluation.
Correlating solvation shell dynamics and ion transport in highly ordered nanoporous polymers
An uncertainty-aware evaluation framework based on hierarchical vision transformers for robust cross-domain plant leaf disease classification
miR-140-5p Inhibition alleviates LPS-induced cholangitis in rats and is associated with modulation of the NF-κB signaling pathway
Erratum: Kim et al., “GABAergic/Glycinergic and Glutamatergic Neurons Mediate Distinct Neurodevelopmental Phenotypes of <i>STXBP1</i> Encephalopathy”
Silver-Zinc oxide nanocomposites: a green approach to mitigate herpes simplex virus type 1 challenges
Molecular Principles of Gating Proton Transport in the Antiporter Modules of Respiratory Complex I
Pituitary adenylate cyclase-activating polypeptide (PACAP) promotes regeneration of corneal epithelial cells possibly via the NR4A1 signaling pathway
Improving medical waste treatment in the Republic of Korea through diversification of operating conditions in sterilization and crushing facilities
Application analysis of soil nail and cable support in deep excavations of loess regions under complex environmental conditions
Adaptive constraint-following control for maintenance robotic manipulator with uncertainties
Dynamic response and pore evolution mechanism of composite improved loess using an eco-friendly curing agent and cement: a macroscopic and microscopic experimental study
A hybrid fungal growth and differential evolution algorithm for energy-efficient UAV trajectory planning in MEC
Abstract The deployment of Unmanned Aerial Vehicles (UAVs) in conjunction with Mobile Edge Computing (MEC) has come to be a viable approach to solve some challenges that face the internet of things systems, including energy consumption, latency, and data processing efficiency. However, trajectory planning optimization for UAVs in the MEC systems remains a challenging issue due to energy restrictions. This study introduces a trajectory planning algorithm, Fungal Growth–Differential Evolution (FGODE), seeking to minimize overall energy consumption without compromising task offloading efficiency and UAV mobility. The approach employs a hybrid optimization algorithm that combines the Fungal Growth Optimizer (FGO) and Differential Evolution (DE) algorithms to effectively maintain between searching new regions and refining promising solutions. The method also utilizes an optimized population size-based encoding mechanism to properly represent candidate solutions. Furthermore, a low-complexity greedy mechanism is employed to sequence the stop points along each UAV’s trajectory, while elite opposition-based learning and Gaussian mutation are utilized to accelerate convergence and mitigate premature stagnation. Several experiments have been conducted to compare with several algorithms. Experimental findings show that FGODE delivers more competitive results than state-of-the-art algorithms across several performance metrics, displaying higher optimization capability.
Soliton dynamics in the stochastic nonlinear Schrödinger equation with self-phase modulation and multiplicative white noise
Abstract In this study, we investigate the stochastic nonlinear Schrödinger equation incorporating self-phase modulation under the influence of multiplicative white noise in the dispersionless regime. By employing the improved modified extended tanh function method , we derive a rich spectrum of analytical solutions, including bright and dark solitons, singular and periodic structures, as well as solutions represented through Jacobi and Weierstrass elliptic functions. This analytical framework not only provides a systematic approach for capturing accurate solutions in noisy environments but also provides an effective analytical approach in addressing nonlinear stochastic partial differential equations. We present a thorough graphical analysis that shows solution behavior across various noise intensity regimes and methodically examine the effects of stochastic perturbations on soliton propagation dynamics. The proposed approach provides an analytical framework for constructing exact wave solutions to the considered model and demonstrates its applicability through several representative solution structures.
Intensifying precipitation extremes and shortened snow persistence reshape hydroclimatic risk in a Himalayan mountain basin
A lightweight hybrid attention network with multi-scale feature integration for intelligent recognition of underwater acoustic targets
KBDR: document retrieval based on graph matching with knowledge enhancement
Application of machine learning algorithms for prediction of abortion and its determinants among women of reproductive-age in East African countries
Abstract In low- and middle-income nations, abortion ranks among the top five causes of maternal mortality. It is associated to a pregnancy and childbirth-related complications. Despite this, there are few scientific researches focusing on predicting abortion and its determinants in East African countries. Therefore, this study aimed to predict abortion and its determinants among women of reproductive-age in East African countries. Community-based cross-sectional study design was used from eleven East African countries DHS dataset spanning 2015 to 2023. The study participants were all reproductive age women who were selected using a two-stage stratified sampling technique. The machine-learning algorithms were applied to predict abortion and its determinants using Python, particularly Jupiter notebook in Google colab. Data cleaning, one-hot encoding, data splitting, and ten-fold cross-validation were performed. Ten machine learning algorithms and SHAP were used to select and interpret the best model. From the total of 372,053 reproductive age women in East Africa, 12.2% participants perform abortion. Random forest was found the best model for training data with 91% of an AUC and 86% of accuracy. According to SHAPE analysis, women who have been never in union, women whose age 15–19 years, women whose age 20–24 years, women from Ethiopia, and women who not used any method were the top four features of abortion. This study identified that random forest classifier was emerged as the best-performing model to predict abortion among women reproductive age in East African countries. Marital status, marital status, age, country, Contraceptive use by method type, and living children plus current pregnancy were key determinant of abortion in East African countries. Governments and health systems should provide access to comprehensive family planning services, reproductive health education, and maternal health support.