Novel insights into the anti-asthmatic effect of Raphanus sativus L. (Raphani Semen): Targeting immune cells, inflammatory pathways and oxidative stress markers.

ETHNOPHARMACOLOGICAL RELEVANCE: Raphanus sativus L. is a well-known medicinal plant with traditional therapeutic applications in various common ailments including inflammation and asthma. AIMS OF THE STUDY: This study aimed to evaluate the chemical composition and anti-asthmatic potential of the hydro-methanolic extract of the leaves of R. sativus L. (Rs.Cr) using various in vitro and in vivo investigations. MATERIALS AND METHODS: The Rs.Cr was subjected to preliminary phytochemical analysis and HPLC profiling. The safety was assessed through oral acute toxicity tests in mice. The antiasthmatic effect of the extract was studied using milk-induced leukocytosis and ovalbumin (OVA)-induced allergic asthma models established in mice. While mast cell degranulation and passive paw anaphylaxis models were established in rats. Moreover, effect of the extract was studied on various oxidative and inflammatory makers. The antioxidant effect of the extract was also studied by in vitro DPPH method. RESULTS: The HPLC profiling of Rs.Cr showed the presence of important polyphenols in a considerable quantity. In toxicity evaluation, Rs.Cr showed no sign of morbidity or mortality with LD50 < 2000 mg/kg. The extract revealed significant mast cell disruption in a dose-dependent manner compared to the intoxicated group. Similarly, treatment with Rs.Cr and dexamethasone significantly (p < 0.001) reduced paw edema volume. Subcutaneous injection of milk at a dose of 4 mL/kg, after 24 h of its administration, showed an increase in the leukocyte count in the intoxicated group. Similarly, mice treated with dexamethasone and Rs.Cr respectively showed a significant decrease in leukocytes and eosinophils count in the ovalbumin-induced allergic asthma model. The extract presented a significant (p˂0.001) alleviative effect on the levels of SOD and GSH, MDA, IL-4, IL-5, and IL-13 in a dose-dependent manner as compared to the intoxicated group. Furthermore, the histological evaluation also revealed a notable decrease in inflammatory and goblet cell count with reduced mucus production. CONCLUSION: The current study highlights mechanism-based novel insights into the anti-asthmatic potential of R. sativus that also strongly supports its traditional use in asthma.

Numerical Solution of Inverse Problem in Functional Near Infrared Spectroscopy using L1-Norm Method.

It has been more than three decades since researchers began investigating functional near-infrared spectroscopy (fNIRs) and its applications with near-infrared light for use in both clinical and pre-clinical settings. In order to increase the accuracy of fNIRs of complex tissue structures, it is necessary to create more advanced image reconstruction methods. Real fNIRs data have been used to develop an implementation of the L1-Norm approach for tackling the inverse problem in this work. The Monte Carlo (MC) simulation is used to construct the sensitivity matrix for this research. Finally, a numerical algorithm for the L1-Norm approach of image reconstruction is developed and implemented in MATLAB to aid in the process. The results showed good agreement with the actual fNIRs data.

The Impact of 3D Prism Cavity for Enhanced Oil Recovery Using Different Nanomaterials.

Enhanced oil recovery (EOR) has been offered as an alternative to declining crude oil production. EOR using nanotechnology is one of the most innovative trends in the petroleum industry. In order to determine the maximum oil recovery, the effect of a 3D rectangular prism shape is numerically investigated in this study. Using ANSYS Fluent software(2022R1), we develop a two-phase mathematical model based on 3D geometry. This research examines the following parameters: flow rate Q = 0.01-0.05 mL/min, volume fractions = 0.01-0.04%, and the effect of nanomaterials on relative permeability. The result of the model is verified with published studies. In this study, the finite volume method is used to simulate the problem, and we run simulations at different flow rates while keeping other variables constant. The findings show that the nanomaterials have an important effect on water and oil permeability, increasing oil mobility and lowering IFT, which increases the recovery process. Additionally, it has been noted that a reduction in the flow rate improves oil recovery. Maximum oil recovery was attained at a 0.05 mL/min flow rate. Based on the findings, it is also demonstrated that SiO2 provides better oil recovery compared to Al2O3. When the volume fraction concentration increases, oil recovery ultimately increases.

The Impact of Cavities in Different Thermal Applications of Nanofluids: A Review.

Nanofluids and nanotechnology are very important in enhancing heat transfer due to the thermal conductivity of their nanoparticles, which play a vital role in heat transfer applications. Researchers have used cavities filled with nanofluids for two decades to increase the heat-transfer rate. This review also highlights a variety of theoretical and experimentally measured cavities by exploring the following parameters: the significance of cavities in nanofluids, the effects of nanoparticle concentration and nanoparticle material, the influence of the inclination angle of cavities, heater and cooler effects, and magnetic field effects in cavities. The different shapes of the cavities have several advantages in multiple applications, e.g., L-shaped cavities used in the cooling systems of nuclear and chemical reactors and electronic components. Open cavities such as ellipsoidal, triangular, trapezoidal, and hexagonal are applied in electronic equipment cooling, building heating and cooling, and automotive applications. Appropriate cavity design conserves energy and produces attractive heat-transfer rates. Circular microchannel heat exchangers perform best. Despite the high performance of circular cavities in micro heat exchangers, square cavities have more applications. The use of nanofluids has been found to improve thermal performance in all the cavities studied. According to the experimental data, nanofluid use has been proven to be a dependable solution for enhancing thermal efficiency. To improve performance, it is suggested that research focus on different shapes of nanoparticles less than 10 nm with the same design of the cavities in microchannel heat exchangers and solar collectors.

Numerical investigation of treated brain glioma model using a two-stage successive over-relaxation method.

A brain tumor is a dynamic system in which cells develop rapidly and abnormally, as is the case with most cancers. Cancer develops in the brain or inside the skull when aberrant and odd cells proliferate in the brain. By depriving the healthy cells of leisure, nutrition, and oxygen, these aberrant cells eventually cause the healthy cells to perish. This article investigated the development of glioma cells in treating brain tumors. Mathematically, reaction-diffusion models have been developed for brain glioma growth to quantify the diffusion and proliferation of the tumor cells within brain tissues. This study presents the formulation the two-stage successive over-relaxation (TSSOR) algorithm based on the finite difference approximation for solving the treated brain glioma model to predict glioma cells in treating the brain tumor. Also, the performance of TSSOR method is compared to the Gauss-Seidel (GS) and two-stage Gauss-Seidel (TSGS) methods in terms of the number of iterations, the amount of time it takes to process the data, and the rate at which glioma cells grow the fastest. The implementation of the TSSOR, TSGS, and GS methods predicts the growth of tumor cells under the treatment protocol. The results show that the number of glioma cells decreased initially and then increased gradually by the next day. The computational complexity analysis is also used and concludes that the TSSOR method is faster compared to the TSGS and GS methods. According to the results of the treated glioma development model, the TSSOR approach reduced the number of iterations by between 8.0 and 71.95%. In terms of computational time, the TSSOR approach is around 1.18-76.34% faster than the TSGS and GS methods.

Feature preserving mesh network for semantic segmentation of retinal vasculature to support ophthalmic disease analysis.

Introduction: Ophthalmic diseases are approaching an alarming count across the globe. Typically, ophthalmologists depend on manual methods for the analysis of different ophthalmic diseases such as glaucoma, Sickle cell retinopathy (SCR), diabetic retinopathy, and hypertensive retinopathy. All these manual assessments are not reliable, time-consuming, tedious, and prone to error. Therefore, automatic methods are desirable to replace conventional approaches. The accuracy of this segmentation of these vessels using automated approaches directly depends on the quality of fundus images. Retinal vessels are assumed as a potential biomarker for the diagnosis of many ophthalmic diseases. Mostly newly developed ophthalmic diseases contain minor changes in vasculature which is a critical job for the early detection and analysis of disease. Method: Several artificial intelligence-based methods suggested intelligent solutions for automated retinal vessel detection. However, existing methods exhibited significant limitations in segmentation performance, complexity, and computational efficiency. Specifically, most of the existing methods failed in detecting small vessels owing to vanishing gradient problems. To overcome the stated problems, an intelligence-based automated shallow network with high performance and low cost is designed named Feature Preserving Mesh Network (FPM-Net) for the accurate segmentation of retinal vessels. FPM-Net employs a feature-preserving block that preserves the spatial features and helps in maintaining a better segmentation performance. Similarly, FPM-Net architecture uses a series of feature concatenation that also boosts the overall segmentation performance. Finally, preserved features, low-level input image information, and up-sampled spatial features are aggregated at the final concatenation stage for improved pixel prediction accuracy. The technique is reliable since it performs better on the DRIVE database, CHASE-DB1 database, and STARE dataset. Results and discussion: Experimental outcomes confirm that FPM-Net outperforms state-of-the-art techniques with superior computational efficiency. In addition, presented results are achieved without using any preprocessing or postprocessing scheme. Our proposed method FPM-Net gives improvement results which can be observed with DRIVE datasets, it gives Se, Sp, and Acc as 0.8285, 0.98270, 0.92920, for CHASE-DB1 dataset 0.8219, 0.9840, 0.9728 and STARE datasets it produces 0.8618, 0.9819 and 0.9727 respectively. Which is a remarkable difference and enhancement as compared to the conventional methods using only 2.45 million trainable parameters.