The Impact of Parental Electronic Health Literacy on Disease Management and Outcomes in Pediatric Type 1 Diabetes Mellitus: Cross-Sectional Clinical Study.

Background: Despite the growing uptake of smart technologies in pediatric type 1 diabetes mellitus (T1DM) care, little is known about caregiving parents' skills to deal with electronic health information sources.

Objective: We aimed to assess the electronic health literacy of parents caring for children with T1DM and investigate its associations with disease management and children's outcomes.

Methods: A cross-sectional survey was performed involving 150 parent-child (8-14 years old with T1DM) dyads in a university pediatric diabetology center.

Qualitative and quantitative image quality of coronary CT angiography using photon-counting computed tomography: Standard and Ultra-high resolution protocols.

Purpose: We aimed to identify the optimal reconstruction settings based on qualitative and quantitative image quality parameters on standard and ultra-high resolution (UHR) images using photon-counting CT (PCCT).

Method: We analysed 45 patients, 29 with standard and 16 with UHR acquisition, applying both smoother and sharper kernel settings. Coronary CT angiography images were performed on a dual-source PCCT system using standard (0.

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation.

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and optimization of traditional invasive treatments and methods. Atrial Fibrillation (AF) is the most common cardiac arrhythmia with critical clinical relevance today. Delayed intervention can lead to heart failure, stroke, or sudden cardiac death.

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities.

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities.

Compositional Optimization of Sputtered WO/MoO Films for High Coloration Efficiency.

Thin films of mixed MoO and WO were obtained using reactive magnetron sputtering onto ITO-covered glass, and the optimal composition was determined for the best electrochromic (EC) properties. A combinatorial material synthesis approach was applied throughout the deposition experiments, and the samples represented the full composition range of the binary MoO/WO system. The electrochromic characteristics of the mixed oxide films were determined with simultaneous measurement of layer transmittance and applied electric current through the using organic propylene carbonate electrolyte cells in a conventional three-electrode configuration.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms.

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm.

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated.

A novel multi-hybrid differential evolution algorithm for optimization of frame structures.

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm.

Efficient implicit constraint handling approaches for constrained optimization problems.

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput.

Revitalizing oncology medications access in Saudi Arabia: Current challenges and recommendations by the Saudi Oncology Pharmacy Assembly.

Background: Cancer care is posing immense challenges to healthcare systems globally. Advances in screening, monitoring, and treating cancer improved patient outcomes and survival rates yet amplified the disease burden. Multiple barriers might impede early access to innovative therapies.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions.

Potholes and traffic signs detection by classifier with vision transformers.

Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees.

The metabolic effect of cannot be determined based on the available clinical evidence: a systematic review and meta-analysis of randomized clinical trials.

Several studies have shown that L. (Cucurbitaceae, bitter melon) has beneficial effects on metabolic syndrome (MetS) parameters and exerts antidiabetic, anti-hyperlipidemic, and anti-obesity activities. Since the findings of these studies are contradictory, the goal of this systematic review and meta-analysis was to assess the efficacy of bitter melon in the treatment of metabolic syndrome, with special emphasis on the anti-diabetic effect.

Computer vision-based plants phenotyping: A comprehensive survey.

The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually.

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [Lu]Lu-DOTATATE radiopharmaceutical therapy.

Purpose: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks.

Micro- and Nano-Roughness Separation Based on Fractal Analysis.

When describing the tribological behaviour of technical surfaces, the need for full-length scale microtopographic characterization often arises. The self-affine of surfaces and the characterisation of self-affine using a fractal dimension and its implantation into tribological models are commonly used. The goal of our present work was to determine the frequency range of fractal behaviour of surfaces by analysing the microtopographic measurements of an anodised aluminium brake plunger.

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study.

Background: This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients.

Methods: A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery.

Quercetin modulates expression of serum exosomal long noncoding RNA NEAT1 to regulate the miR-129-5p/BDNF axis and attenuate cognitive impairment in diabetic mice.

Aims: Cognitive impairment poses a considerable health challenge in the context of type 2 diabetes mellitus (T2DM), emphasizing the need for effective interventions. This study delves into the therapeutic efficacy of quercetin, a natural flavonoid, in mitigating cognitive impairment induced by T2DM in murine models.

Materials And Methods: Serum exosome samples were obtained from both T2DM-related and healthy mice for transcriptome sequencing, enabling the identification of differentially expressed mRNAs and long noncoding RNAs (lncRNAs).

Influence of changes in foot morphology and temperature on bruised toenail injury risk during running.

Despite runners frequently suffering from dermatologic issues during long distance running, there is no compelling evidence quantitatively investigating their underlying injury mechanism. This study aimed to determine the foot morphology and temperature changes during long distance running and reveal the effect of these alterations on the injury risk of bruised toenail by measuring the subjective-perceived hallux comfort and gap length between the hallux and toebox of the shoe. Ten recreational runners participated in the experimental tests before (baseline), immediately after 5 and 10 km of treadmill running (12 km/h), in which the foot morphology was measured by a 3D foot scanner, the foot temperature was detected by an infrared camera, the perceived comfort was recorded by a visual analogue scale, and the gap length in the sagittal plane was captured by a high-speed camera.