Validated HPLC method for simultaneous quantification of mutant IDH1/2 inhibitors (enasidenib,ivosidenib and vorasidenib) in mouse plasma: Application to a pharmacokinetic study

Isocitrate dehydrogenase (IDH) inhibitors comprise a novel class of anticancer drugs, which are approved to treat acute myeloid leukemia patients having mutations on IDH1/2. We report the development and validation of a high‐performance liquid chromatography (HPLC) method for the simultaneous quantitation of IDH inhibitors, namely enasidenib (EDB), ivosidenib (IDB) and vorasidenib (VDB), in mouse plasma as per the US Food and Drug Administration regulatory guidelines. The method involves extraction of EDB, IDB and VDB along with internal standard (IS; phenacetin) from mouse plasma (100 μl) using a simple protein precipitation process. The chromatographic analysis was performed on an HPLC system using a gradient mobile phase (comprising 10 mm ammonium acetate and acetonitrile in a flow‐gradient) and an X‐Terra Phenyl column. The UV detection wave length was set at λ_max 265 nm. EDB, IDB, VDB and the IS eluted at 7.36, 8.60, 9.50 and 5.12 min, respectively, with a total run time of 10 min. The calibration curve was linear over a concentration range of 0.20–12.5 μg/ml for EDB and 0.50–12.5 μg/ml for IDB and VDB (r² = ≥0.998 for all of the analytes). Validation results met the acceptance criteria. The validated HPLC method was successfully applied to a pharmacokinetic study in mice.     

Electrical Impedance Spectroscopic Study of CNT/Ethylene-alt-CO/Propylene-alt-CO Polyketones Nanocomposite

Impedance spectroscopy was utilized to investigate the dielectric properties, ac conductivity and charge transport mechanisms in propylene-alt-CO/ethylene-alt-CO (EPEC) random terpolymer filled with multi-walled carbon nanotubes (MWCNT) as a function of nanofiller content, frequency, and temperature. Equivalent resistor-capacitor (RC) circuit models were proposed to describe the impedance characteristics of the unfilled terpolymer and the nanocomposite at different temperatures. For the nanocomposites, the ac conductivity tended to be frequency independent at low frequencies. At high frequencies, the ac conductivity increased with frequency. The dc conductivity (i.e., plateau of the ac conductivity at low frequencies) at room temperature increased from 10^?9 (Ω·m)^?1 for the unfilled polymer to l0^?3 (Ω·m)^?1 for the 6 wt% MWCNT/EPEC nanocomposite. At low temperatures, the equivalent RC model for EPEC-0 and EPEC-2 was found to consist of a parallel RC circuit. However, for 6 wt% MWCNT/EPEC nanocomposite, an RC model consisting of an R/constant phase element (CPE) circuit and a resistor in series was required to describe the impedance behavior of the nanocomposite.     

Determination of the Heat Transfer Coefficient of Metal Oxide Based Water Nanofluids in a Laminar Flow Regime Using an Adaptive Neuro-Fuzzy Inference System

In order to enhance the thermal properties of turbine oil (TO), three different nanoparticles (CuO, Al₂O₃, and TiO₂) are loaded into the TO. To measure the thermal performance of nanoparticle-based TO nanofluids at laminar flow and under constant heat flux boundary conditions, an experimental setup was applied. The obtained data clearly demonstrate the positive effect of all nanoparticles on the heat transfer rate of TO. As the most important factor, the heat transfer coefficient of the abovementioned two-phase systems is increased upon increasing both the volume concentration and the flow rate. An adaptive neuro-fuzzy inference system (ANFIS) is applied for modeling the effect of critical parameters on the heat transfer coefficient of nanoparticle-TO based nanofluids numerically. The results are compared with experimental ones for training and test data. The results suggest that the developed model is valid enough and promising for predicting the extant of the heat transfer coefficient. R² and MSE values for all data were 0.990208751 and 108.1150734, respectively. Based on the results, it is obvious that our proposed modeling by ANFIS is efficient and valid, which can be expanded for more general states.     

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.

     

Graphene and Carbon Nanotube-based Electrochemical Sensing Platforms for Dopamine

Dopamine (DA) is an important neurotransmitter, which is created and released from the central nervous system. It plays a crucial role in human activities, like cognition, emotions, and response to anything. Maladjustment of DA in human blood serum results in different neural diseases, like Parkinson's and Schizophrenia. Consequently, researchers have started working on DA detection in blood serum, which is undoubtedly a hot research area. Electrochemical sensing techniques are more promising to detect DA in real samples. However, utilizing conventional electrodes for selective determination of DA encounters numerous problems due to the coexistence of other materials, such as uric acid and ascorbic acid, which have an oxidation potential close to DA. To overcome such problems, researchers have put their focus on the modification of bare electrodes. The aim of this review is to present recent advances in modifications of most used bare electrodes with carbonaceous materials, especially graphene, its derivatives, and carbon nanotubes, for electrochemical detection of DA. A brief discussion about the mechanistic phenomena at the electrode interface has also been included in this review.