Browse Articles
Discover research articles across all indexed journals
Characterization of rhesus macaque model for cobalt-60 gamma-radiation source without use of blood product
Abstract Despite significant radiobiological advancements following World War II, only a limited number of medical countermeasures (MCMs) have been approved by the United States Food and Drug Administration (US FDA) for acute radiation exposure related illnesses. Accordingly, well-characterized and validated animal models, both large and small, are still very much needed to develop safe and effective countermeasures. Animal models that are used for such purposes need to reflect not only the clinical and pathogenic features of those seen in radiation exposed humans, but also comparable radiation dose- and time-dependent relationships. The objective of the present study therefore was to further characterize the response patterns of rhesus nonhuman primates exposed to total-body, potentially lethal, radiation doses using the Armed Forces Radiobiology Research Institute high level cobalt-60 gamma-radiation source. Response patterns of male and female rhesus macaques were assessed following acute, total-body exposures to potentially lethal, gamma rays (5.8, 6.5, and 7.2 Gy). Groups of 15, 16, and 8 animals were exposed to the three radiation doses, respectively. All animals were provided a minimum, subject-based supportive care, that excluded the use of blood products. Blood products were excluded in order to replicate a large scale radiological/nuclear scenario treatment option in which access to blood products may be limited or unavailable. This is also relevant for military scenarios, in which medical facilities may not have the appropriate capabilities for blood transfusions. All animals were clinically monitored for 60 days post-irradiation. Survival was the primary endpoint of this study, while secondary endpoints included recovery of various hematopoietic elements. The mortality rates of the rhesus macaques were 33%, 37.5% and 50%, respectively, for the three radiation doses (i.e., 5.8, 6.5 and 7.2 Gy). Within the surviving animals, hematological blood values had returned largely to pre-exposure levels by the end of the study period. The results of this study provides foundational data on the use of the rhesus macaque model for subsequent development and testing of new radiation MCMs, as per required by the US FDA Animal Rule.
Standoff real-time detection of floating and submerged objects in ocean water
Screening of hub genes and immunocytes related to tendon injury based on bioinformatics and machine learning models
The association between self-checked visual field impairment and motor vehicle accidents among Japanese taxi drivers
Spectral phasor imaging on a commercial confocal microscope without a spectral detector
Integrated computational and preclinical evaluation of novel synthetic pyrazole pyrazoline thiazole derivative for breast cancer therapeutics
Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signals
Combining serologic biomarkers with the PAGE B score improves risk stratification for hepatocellular carcinoma development among chronic hepatitis B patients
The impact of rainfall variability on selected soil properties and ecophysiological traits in Prosopis juliflora invaded plots
Simulation and validation of hydraulic track drive systems for electrification of heavy-duty machinery
Abstract This work explores the challenges and necessity of replacing inefficient hydraulic systems in large-scale heavy-duty off-road machinery, such as mobile rock crushers, with innovative electric solutions. A mathematical and Simulink simulation model of a rock crusher’s hydraulic track drive is developed and validated against physical test data. The model investigates the power developed by the hydraulic system during operation via simulation and is validated using results obtained from physical testing of the machine. These findings can inform the specification of an equivalent electric system, enabling similar torque delivery without the idle energy losses typical of fossil fuel systems. The simulation also supports appropriate energy storage sizing to perform required manoeuvres. The simulated system accuracy ranges from 4 to 12%, with a 20% outlier attributed to real-world inefficiencies like fluid losses and transient response delays. This level of accuracy is considered sufficient for guiding electrification efforts, ensuring the proposed electric system is neither under- nor over-designed. The insights gained from this simulation work contribute to the transition of heavy-duty machinery from hydraulic to electric powertrains, supporting the development of more energy-efficient and sustainable solutions.
Machine learning models for the prediction of hydrogen solubility in aqueous systems
Abstract Hydrogen storage is integral to reducing CO2 emissions, particularly in the oil and gas industry. However, a primary challenge involves the solubility of hydrogen in subsurface environments, particularly saline aquifers. The dissolution of hydrogen in saline water can impact the efficiency and stability of storage reservoirs, necessitating detailed studies of fluid dynamics in such settings. Beyond its role as a clean energy carrier and precursor for synthetic fuels and chemicals, understanding hydrogen’s solubility in subsurface conditions can significantly enhance storage technologies. When hydrogen solubility is high, it can reduce reservoir pressure and alter the chemical composition of the storage medium, undermining process efficiency. Machine learning techniques have gained prominence in predicting physical and chemical properties across various systems. One of the most complex challenges in hydrogen storage is predicting its solubility in saline water, influenced by factors such as pressure, temperature, and salinity. Machine learning models offer substantial promise in improving hydrogen storage by identifying intricate, nonlinear relationships among these parameters. This study uses machine learning algorithms to predict hydrogen solubility in saline aquifers, employing techniques such as Bayesian inference, linear regression, random forest, artificial neural networks (ANN), support vector machines (SVM), and least squares boosting (LSBoost). Trained on experimental data and numerical simulations, these models provide precise predictions of hydrogen solubility, which is strongly influenced by pressure, temperature, and salinity, under a wide range of thermodynamic conditions. Among these methods, RF outperformed the others, achieving an R2 of 0.9810 for test data and 0.9915 for training data, with RMSE values of 0.048 and 0.032, respectively. These findings emphasize the potential of machine learning to significantly optimize hydrogen storage and reservoir management in saline aquifers.
The role of cognitive flexibility and psychological well-being in the effect of mindfulness on problematic internet use
Convolutional neural network and wavelet composite against geometric attacks a watermarking approach
Employing diclofenac sodium as a novel therapeutic frontier for Staphylococcus epidermidis infections
Abstract The spread of biofilm-forming multidrug-resistant pathogenic bacteria is an alarming public health issue requiring significant research. Drug repurposing is a novel approach to combating bacterial infections that is currently being studied. Here, we explored diclofenac sodium’s potential antibacterial and antibiofilm action on Staphylococcus epidermidis bacteria. Diclofenac sodium revealed antibacterial action on S. epidermidis isolates with minimum inhibitory concentrations of 500 to 2000 µg/mL. It also exposed antibiofilm action using the crystal violet assay and scanning electron microscope. Using qRT-PCR, diclofenac sodium has downregulated the expression of the biofilm genes (cna, fnbA, and ica) in 20% of the isolates. An animal model revealed the effect of diclofenac sodium on a systemic infection with S. epidermidis in mice. Diclofenac sodium has improved the liver, spleen, and kidney architecture. Molecular docking was used to explore the possible mechanism for the activity of diclofenac sodium against S. epidermidis, which revealed the high affinity of diclofenac sodium toward S. epidermidis protein and TcaR enzymes. Thus, diclofenac sodium could be a clinical solution for disseminating resistance among S. epidermidis and could be investigated for its combination with different antibiotics in future studies.