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.     

Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

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.

     

Medicinal Potential of Isoflavonoids: Polyphenols That May Cure Diabetes

In recent years, there is emerging evidence that isoflavonoids, either dietary or obtained from traditional medicinal plants, could play an important role as a supplementary drug in the management of type 2 diabetes mellitus (T2DM) due to their reported pronounced biological effects in relation to multiple metabolic factors associated with diabetes. Hence, in this regard, we have comprehensively reviewed the potential biological effects of isoflavonoids, particularly biochanin A, genistein, daidzein, glycitein, and formononetin on metabolic disorders and long-term complications induced by T2DM in order to understand whether they can be future candidates as a safe antidiabetic agent. Based on in-depth in vitro and in vivo studies evaluations, isoflavonoids have been found to activate gene expression through the stimulation of peroxisome proliferator-activated receptors (PPARs) (α, γ), modulate carbohydrate metabolism, regulate hyperglycemia, induce dyslipidemia, lessen insulin resistance, and modify adipocyte differentiation and tissue metabolism. Moreover, these natural compounds have also been found to attenuate oxidative stress through the oxidative signaling process and inflammatory mechanism. Hence, isoflavonoids have been envisioned to be able to prevent and slow down the progression of long-term diabetes complications including cardiovascular disease, nephropathy, neuropathy, and retinopathy. Further thoroughgoing investigations in human clinical studies are strongly recommended to obtain the optimum and specific dose and regimen required for supplementation with isoflavonoids and derivatives in diabetic patients.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Synthesis of Fe—Ni—Ce trimetallic catalyst nanoparticles via impregnation and co-precipitation and their application to dye degradation

In this study, trimetallic catalysts were prepared via the co-precipitation and impregnation methods. In order to investigate the effect of impregnation on the catalytic activity and crystallite size, a trimetallic catalyst, Fe—Ni—Ce, was prepared through the co-precipitation method in one set of experiments, and cerium was impregnated with the Ni—Fe mixture in the final stage of the preparation in another set. Fourier transform infrared spectroscopy was employed to confirm the formation of trimetallic catalysts and the success of the impregnation method. The Brunauer-Emmett-Teller nitrogen adsorption isotherm exhibits a high specific surface area (approximately 39 m² g^?1) for the nanoparticles obtained by the impregnation method. The crystallography and morphology of the trimetallic catalysts thus prepared were characterised by X-ray diffraction and scanning electron microscopy. UV-VIS spectroscopy and methylene blue dye degradation tests were also performed to investigate the catalytic activity of the synthesised catalysts. The crystalline size was found to be smaller for the catalysts prepared by the impregnation method. In addition, the samples synthesised using the cerium impregnation method showed superior activity in the methylene blue dye degradation test. The effect of the catalyst dosage on dye degradation, as well as the effect of the initial dye concentration on the catalyst activity, was also studied for both methods.