Theranostic radionuclides: recent advances in production methodologies

Journal of Radioanalytical and Nuclear Chemistry - Various concepts involved in the quantification of radiation dose while following the theranostic approach in nuclear medicine are outlined. The...     

Role of Daucus carota in Enhancing Antiulcer Profile of Pantoprazole in Experimental Animals

The carrot plant (Daucus carota) and its components are traditionally reported for the management of gastric ulcers. This study was performed to evaluate the role of carrot when administered concurrently with a conventional antiulcer treatment, pantoprazole, in alleviating gastric and duodenal ulcers in female experimental animals. The study involved standard animal models to determine the ulcer preventive effect using pylorus ligation, ethanol, and stress induced acute gastric ulcer models and duodenal ulcer models involving cysteamine. Acetic acid-induced chronic gastric ulcer and indomethacin-induced gastric ulcer models were used to evaluate the ulcer healing effect. Carrot fruit (500 mg/kg) and its co-administration with pantoprazole produced significant protection in an ethanol- and stress-induced acute gastric ulcer and cysteamine-induced duodenal ulcer. The healing of the acetic acid-induced chronic gastric ulcer was also augmented with this combination. Both total proteins and mucin contents were significantly increased in indomethacin-induced gastric ulcers. Similarly, in pylorus ligation, the pepsin content of gastric juice, total acidity, and free acidity were reduced. Overall, both ulcer preventive effects and ulcer healing properties of the pantoprazole were significantly enhanced in animals who received the co-administration of carrot fruit (500 mg/kg).     

Experimental study on broadband radiofrequency electromagnetic radiations near cellular base stations: a novel perspective of public health

Journal of Thermal Analysis and Calorimetry - The purpose of this study is to measure and document the maximum level of broadband radiofrequency electromagnetic radiations in the vicinity of...     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

A calibrated model of local food system of Hawaii: What are the economic implications of the state's food goals and policies?

We develop a fully calibrated positive mathematical programming model for Hawaii's local food systems—which captures the production and the consumer sides of the market. Then we use the model to assess two proposed policies—a general excise tax (GET) exemption on locally produced foods, and an investment in agricultural infrastructure. For the GET exemption case, our results indicate an economic gain of $118 per $100 cost. On the other hand, an investment in 1,200 acres of land injected to support local production may generate an economic gain of up to $357 per $100 annual cost of the investment. However, these estimates should be considered preliminary, and thus viewed with caution. Although the model is used to capture Hawaii's local food systems, we believe that our model is generalizable and can be adopted by other economies to assess their respective food localization policies. Recommendations for Resource Managers

• Local food policies need to be based on quantitative terms instead of mere armchair speculation because often their potential outcomes may vary significantly.

• The current modeling framework demonstrates the potential of using positive mathematical programming (PMP) in capturing the intricacies of local food systems. However, this exploratory exercise should be viewed as preliminary in nature and the ensuing results were taken with caution because many important factors such as labor availability may have been left out.

• Thus, further model refinements are necessary to better capture the complexities of local food systems such as farm heterogeneity, availability of farm labor, water availability, and interisland transportation of farm products in the case of Hawaii.

     

Binder-less and free-standing Co–Fe metal nanoparticles-decorated PVdF-HFP nanofiber membrane as an electrochemical probe for enzyme-less glucose sensors

Co–Fe bimetallic nanoparticles-affixed polyvinylidene fluoride-co-hexafluoropropylene (PVdF-HFP) nanofiber membrane is fabricated using the electrospinning and chemical reduction techniques. The semicrystalline polymeric backbone decorated with the highly crystalline Co–Fe bimetallic nanoparticles enunciates the mechanical integrity, while the incessant and swift electron mobility is articulated with the consistent dissemination of bimetallic nanoparticles on the intersected and multi-layered polymeric nanofibers. The diffusion and adsorption of glucose are expedited in the extended cavities and porosities of as-formulated polymeric nanofibers, maximizing the glucose utilization efficacy, while the uniformly implanted Co⁴⁺/Fe³⁺ active centers on PVdF-HFP nanofibers maximize the electrocatalytic activity toward glucose oxidation under alkaline regimes. Thus, the combinative sorts including nanofiber and nanocomposite strategies of PVdF-HFP/Co–Fe membrane assimilate the enzyme-less electrochemical glucose detection concerts of high sensitivity (375.01 μA mM^?1 cm^?2), low limit of detection (0.65 μm), and wide linear range (0.001 to 8 mM), outfitting the erstwhile enzyme-less glucose detection reports. Additionally, the endowments of high selectivity and real sample glucose-sensing analyses of PVdF-HFP/Co–Fe along with the binder-less and free-standing characteristics construct the state-of-the-art paradigm for the evolution of affordable enzyme-less electrochemical glucose sensors.