Browse Articles

Harnessing light-activated gallium porphyrins to combat intracellular Staphylococcus aureus using an in vitro keratinocyte infection model

Abstract Staphylococcus aureus ( S. aureus ) can survive inside nonprofessional phagocytes such as keratinocytes, enabling it to evade antibiotics and cause recurrent infections once treatment stops. New antibacterial strategies to eliminate intracellular, multidrug-resistant bacteria are needed. This study used a keratinocyte model infected with methicillin-resistant S. aureus (MRSA) to test light-activated compounds, specifically heme-mimetic gallium (III) porphyrin (Ga 3+ CHP) and visible light, known as antimicrobial photodynamic inactivation (aPDI), for eliminating intracellular MRSA. Ga 3+ CHP was found to accumulate more in infected cells, particularly within lysosomal structures where MRSA resides. Flow cytometry and fluorescence microscopy revealed significant colocalization of MRSA and Ga 3+ CHP. Under aPDI, MRSA showed reduced adhesion to host cells and a 70% reduction in the GFP signal from intracellular bacteria. Additionally, light-activated Ga 3+ CHP significantly decreased the number of extracellular bacteria, reducing the potential for further infection. This study is the first to analyze aPDI toxicity in real time within an infection model, demonstrating that this method is neither cytotoxic nor phototoxic.

Natural cellulose fibers derived from Dracaena angolensis (Welw. ex Carrière) Byng & Christenh. demonstrate potential as a non-absorbable surgical suture biomaterial

Control optimization of air traffic emissions in a two-variable dynamic model

Leucine-rich alpha-2-glycoprotein 1 deficiency suppresses ischemia–reperfusion injury-induced renal fibrosis

Publisher Correction: COVID-19 and mental distress among health professionals in eight European countries during the third wave: a cross-sectional survey

A novel optimal design approach for bladeless wind turbines considering mechanical properties of composite materials used

AbstractBladeless wind turbines face operational limitations due to the lock-in phenomenon. This study introduces two novel mechanisms for designing bladeless wind turbines to address this issue, enabling operation across a broad wind speed range from 2 to 10 m/s while ensuring that lock-in conditions are satisfied at any wind speed within this range. The study aims to maintain optimal performance without any decline that is observed in conventional bladeless wind turbines by controlling the turbine’s natural frequency through implementing these mechanisms, either by adjusting the effective length of the stand or by incorporating an additional mass in the hollow mast, or both. A mathematical model including dynamic analysis is constructed to adjust natural frequency to match the shedding frequency at the specified wind speed. Validation of our model shows high accuracy. Numerical results demonstrate that applying these mechanisms ensures the turbine is optimally designed across varying parameters. Findings reveal that for lower flexural modulus values, the first mechanism alone can achieve a 99.2% increase in mechanical efficiency at 7 m/s. For higher flexural modulus values, incorporating the second mechanism is essential to reduce the turbine’s overall size. This integrated approach improves efficiency with a 55.7% increase.

Preoperative radiotherapy combined with surgery versus surgery alone for primary retroperitoneal sarcoma: a meta-analysis

Role of topological indices in predictive modeling and ranking of drugs treating eye disorders

Learning with noisy labels via clean aware sharpness aware minimization

Innovative method integrates play fairway analysis supported with GIS and seismic modeling for geothermal potential evaluation in a basement reservoir

AbstractThe growing demand for clean and sustainable energy sources has prompted the investigation of numerous renewable and ecologically friendly options. Among these, geothermal energy is particularly noteworthy because of its widespread availability, compact size, and consistent, weather-independent power production. A geothermal play fairway analysis (GPFA) model was created for the study area, which is located in Békés county, southeastern Hungary. The GPFA model approach in the current study is the first model developed in Hungary to achieve three main goals. firstly, to quantitatively assess the geothermal potential, secondly, to identify the most favorable areas for geothermal exploration and development, and thirdly, to evaluate the corresponding risk levels in the study area. The study focuses on identifying and assessing three main risk components associated with exploitable geothermal systems in the study area. The risk parameters consist of the heat source, reservoir fracture permeability, and seal. Advanced 3D seismic interpretation, geographic information system (GIS), and 3DHIP (heat in place) calculator techniques are used to evaluate subsurface structural and thermal models. Two phases of seismic interpretation are used; conventional interpretation phase focused on conventional seismic data interpretation and advanced attribute generation phase where various seismic attribute cube volumes are generated. Common Risk Segment Maps (CRS) for each risk parameter are created by combining data from all the elements contributing to that risk using GIS toolbox. The resulted CRS maps of the study area three risk parameters are summed to produce a Composite Common Risk Segment Map (CCRS) map. Based on the constructed CCRS map and the developed GPFA model, the study area holds valuable untapped geothermal potential, poses varying risk levels associated with geothermal exploration and development. The amount of risk resulting from the three risk components is not equal, and the reservoir fracture permeability is the main risk factor. The GPFA model is successfully narrowed down an expansive exploration area of around 350 km2 to just 4 highly promising targets with high geothermal favorability and low risk as future drilling targets. The constructed 3D thermal capacity model indicates that the average heat content in the study area is estimated to be 65,450 Petajoules per square kilometer (PJ/km2), with a recoverable heat energy of 6090 megawatt thermal per square kilometer (MWth/km2). The recoverable heat for the four selected targets is estimated under different production scenarios: a 30-year plan, a 20-year plan, and a 10-year plan and it ranges from 7.5 to 32 MWth/km2, 11 to 48 MWth/km2, 22.2 to 96.8 MWth/km2 respectively. The findings of this study have made important contributions to the field of geothermal exploration approaches and offer valuable insights for making well-informed decisions about sustainable energy development in the study area.

Migration of primordial germ cells and their relationship of PGCs with sex development in transgenic germline-specific fluorescent freshwater angelfish (Pterophyllum scalare)

AbstractPrimordial germ cells (PGCs), the progenitors of gametes, are essential for teleost reproduction. While their formation is conserved across teleosts, the activation, migration routes, and localization periods vary among species. In this study, we developed a novel transgenic line, Tg(ddx4:TcCFP13-nanos3), based on the Nile tilapia genome, to label PGCs with clear fluorescent signals in the freshwater angelfish (Pterophyllum scalare). Our findings reveal a complex, multistage PGCs migration process in angelfish, with a significantly extended localization period (168 hpf) compared to zebrafish (24 hpf). Notably, individual differences in PGCs abundance were observed during early somite development. Analysis of PGCs counts and subsequent sexual maturation demonstrate a potential correlation between PGCs abundance and sex determination: 90% of PGCs-reduced individuals developed as males, while 83% of PGCs-rich individuals developed as females. This study provides a foundation for understanding PGCs migration and sex development in freshwater angelfish, offering valuable insights into reproductive biology and ornamental fish species. Furthermore, this in vivo PGCs tracking system for Cichlids provides a versatile tool for advancing research and applications in germ cell biology.

An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods

High resolution Brillouin spectroscopy of the surface acoustic waves in Sb2Te3 van der Waals single crystals

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

AbstractDeep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts. Traditional deep learning models rely on single-point predictions, limiting their ability to provide uncertainty measures essential for robust clinical decision-making. To solve this issue, Bayesian approximation approaches have evolved and are gaining market traction. In this work, we implemented a transfer learning approach, building upon the DenseNet-121 convolutional neural network to detect diabetic retinopathy, followed by Bayesian extensions to the trained model. Bayesian approximation techniques, including Monte Carlo Dropout, Mean Field Variational Inference, and Deterministic Inference, were applied to represent the posterior predictive distribution, allowing us to evaluate uncertainty in model predictions. Our experiments on a combined dataset (APTOS 2019 + DDR) with pre-processed images showed that the Bayesian-augmented DenseNet-121 outperforms state-of-the-art models in test accuracy, achieving 97.68% for the Monte Carlo Dropout model, 94.23% for Mean Field Variational Inference, and 91.44% for the Deterministic model. We also measure how certain the predictions are, using an entropy and a standard deviation metric for each approach. We also evaluated the model using both AUC and accuracy scores at multiple data retention levels. In addition to overall performance boosts, these results highlight that Bayesian deep learning does not only improve classification accuracy in the detection of diabetic retinopathy but also reveals beneficial insights about how uncertainty estimation can help build more trustworthy clinical decision-making solutions.

Effects of time-of-day on the noradrenaline, adrenaline, cortisol and blood lipidome response to an ice bath

Abstract While the effect of time-of-day (morning versus evening) on hormones, lipids and lipolysis has been studied in relation to meals and exercise, there are no studies that have investigated the effects of time-of-day on ice bath induced hormone and lipidome responses. In this crossover-designed study, a group of six women and six men, 26 ± 5 years old, 176 ± 7 cm tall, weighing 75 ± 10 kg, and a BMI of 23 ± 2 kg/m 2  had an ice bath (8–12 °C for 5 min) both in the morning and evening on separate days. Absence from intense physical exercise, nutrient intake and meal order was standardized in the 24 h prior the ice baths to account for confounders such as diet or exercise. We collected venous blood samples before and after (5 min and 30 min) the ice baths to measure hormones (noradrenaline, adrenaline, and cortisol) and lipid levels in plasma via liquid chromatography mass spectrometry shotgun lipidomics. We found that ice baths in the morning increase plasma fatty acids more than in the evening. Overall plasma lipid composition significantly differed in-between the morning and evening, and only in the morning ice bathing is accompanied by significantly increased plasma fatty acids from 5.1 ± 2.2% to 6.0 ± 2.4% ( P  = 0.029) 5 min after and to 6.3 ± 3.1% ( P  = 0.008) 30 min after. Noradrenaline was not affected by time-of-day and increased significantly immediately after the ice baths in the morning by 127 ± 2% (pre: 395 ± 158 pg/ml, post 5 min: 896 ± 562 pg/ml, P  = 0.025) and in the evening by 144 ± 2% (pre: 385 ± 146 pg/ml, post 5 min: 937 ± 547 pg/ml, P  = 0.015). Cortisol was generally higher in the morning than in the evening (pre: 179 ± 108 pg/ml versus 91 ± 59 pg/ml, P  = 0.013; post 5 min: 222 ± 96 pg/ml versus 101 ± 52 pg/ml, P  = 0.001; post 30 min: 190 ± 96 pg/ml versus 98 ± 54 pg/ml, P  = 0.009). There was no difference in the hormonal and lipidome response to an ice bath between women and men. The main finding of the study was that noradrenaline, adrenaline, cortisol and plasma lipidome responses are similar after an ice bath in the morning and evening. However, ice baths in the morning increase plasma fatty acids more than in the evening.

Peony diagram modeling for improving the easy disassembly design of mobile electronic products

AbstractDesigning mobile electronic products for easy disassembly is crucial for promoting resource recycling. However, many current approaches overlook the need to consider the disassembly of parts from the perspective of the overall product structure and practical recycling requirements, leading to potentially suboptimal or unnecessary optimization strategies. This study introduces a novel visual disassembly information model called the “Peony Diagram.” This model uses a multi-layer ring diagram to depict part hierarchies and constraint relationships and employs specific symbols to convey part disassembly information and highlight key components. The difficulty of disassembling individual parts is assessed by calculating difficulty coefficients based on quantified disassembly information in the model. To validate the model’s effectiveness, a smartphone was used as a case study to determine the shortest disassembly sequence and evaluate disassembly difficulty for both single and multiple parts. The findings demonstrate that the proposed method can accurately assess the ease of disassembly of key components during product repair and recycling. Consequently, it can suggest strategies to enhance recycling and disassembly efficiency and reduce the difficulty of disassembling critical parts.

Observer- and sequence variability in personalized 4D flow MRI-based cardiovascular models

Abstract Subject-specific parameters in lumped hemodynamic models of the cardiovascular system can be estimated using data from experimental measurements, but the parameter estimation may be hampered by the variability in the input data. In this study, we investigate the influence of inter-sequence, intra-observer, and inter-observer variability in input parameters on estimation of subject-specific model parameters using a previously developed approach for model-based analysis of data from 4D Flow MRI acquisitions and cuff pressure measurements. The investigated parameters describe left ventricular time-varying elastance and aortic compliance. Parameter reproducibility with respect to variability in the MRI input measurements was assessed in a group of ten healthy subjects. The subject-specific parameters had coefficient of variations between 2.6 and 35% in the intra- and inter-observer analysis. In comparing parameters estimated using data from the two MRI sequences, the coefficients of variation ranged between 3.3 and 41%. The diastolic time constant of the left ventricle and the compliance of the ascending aorta were the parameters with the lowest and the highest variability, respectively. In conclusion, the modeling approach allows for estimating left ventricular elastance parameters and aortic compliance from non-invasive measurements with good to moderate reproducibility concerning intra-user, inter-user, and inter-sequence variability in healthy subjects.

RNA binding protein ALYREF regulates ferroptosis to facilitate LUAD growth and metastasis via promoting SLC7A11 mRNA stability

Balancing fairness and efficiency in dynamic vaccine allocation during major infectious disease outbreaks

Neutron imaging and molecular simulation of systems from methane and p-xylene

AbstractBulk properties of two-phase systems comprising methane and liquid p-xylene were derived experimentally using neutron imaging and theoretically predicted using molecular dynamics (MD). The measured and predicted methane diffusivity in the liquid, Henry’s law constant, apparent molar volume, and surface tension compared well within the experimentally studied conditions (273.15 to 303.15 K, ≤ 100 bar). Since MD is a physical model, extrapolations of the two-phase systems properties were performed for a broader temperature range (260 to 400 K, ≤ 100 bar). Moreover, the species diffusivities in single phases formed by infinitely diluted p-xylene in methane were predicted under conditions relevant to the methane liquefaction (90 to 290 K, 50 bar). The predicted p-xylene diffusivity in the supercritical methane was one order of magnitude higher than that calculated using Wilke–Chang and He–Yu correlations. This study provides novel experimental and MD-simulated characteristics for this industrially relevant system, for which intensive freeze-out formation from the supercritical methane is predicted.