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"Mohammed Al"
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Free Radical-Scavenging, Anti-Inflammatory/Anti-Fibrotic and Hepatoprotective Actions of Taurine and Silymarin against CCl4 Induced Rat Liver Damage
The present study aims to investigate the hepatoprotective effect of taurine (TAU) alone or in combination with silymarin (SIL) on CCl4-induced liver damage. Twenty five male rats were randomized into 5 groups: normal control (vehicle treated), toxin control (CCl4 treated), CCl4+TAU, CCl4+SIL and CCl4+TAU+SIL. CCl4 provoked significant increases in the levels of hepatic TBARS, NO and NOS compared to control group, but the levels of endogenous antioxidants such as SOD, GPx, GR, GST and GSH were significantly decreased. Serum pro-inflammatory and fibrogenic cytokines including TNF-α, TGF-β1, IL-6, leptin and resistin were increased while the anti-inflammatory (adiponectin) cytokine was decreased in all treated rats. Our results also showed that CCl4 induced an increase in liver injury parameters like serum ALT, AST, ALP, GGT and bilirubin. In addition, a significant increase in liver tissue hydroxyproline (a major component of collagen) was detected in rats exposed to CCl4. Moreover, the concentrations of serum TG, TC, HDL-C, LDL-C, VLDL-C and FFA were significantly increased by CCl4. Both TAU and SIL (i.e., antioxidants) post-treatments were effectively able to relieve most of the above mentioned imbalances. However, the combination therapy was more effective than single applications in reducing TBARS levels, NO production, hydroxyproline content in fibrotic liver and the activity of serum GGT. Combined treatment (but not TAU- or SIL-alone) was also able to effectively prevent CCl4-induced decrease in adiponectin serum levels. Of note, the combined post-treatment with TAU+SIL (but not monotherapy) normalized serum FFA in CCl4-treated rats. The biochemical results were confirmed by histological and ultrastructural changes as compared to CCl4-poisoned rats. Therefore, on the basis of our work, TAU may be used in combination with SIL as an additional adjunct therapy to cure liver diseases such as fibrosis, cirrhosis and viral hepatitis.
Journal Article
Adoption of AI writing tools among academic researchers: A Theory of Reasoned Action approach
This research explores the determinants affecting academic researchers’ acceptance of AI writing tools using the Theory of Reasoned Action (TRA). The impact of attitudes, subjective norms, and perceived barriers on researchers’ intentions to adopt these technologies is examined through a cross-sectional survey of 150 researchers. Structural Equation Modeling (SEM) is employed to evaluate the measurement and structural models. Findings confirm the positive influence of favorable attitudes and subjective norms on intentions to use AI writing tools. Interestingly, perceived barriers did not significantly impact attitudes or intentions, suggesting that in the academic context, potential benefits may outweigh perceived obstacles to AI writing tool adoption. Contrarily, perceived barriers do not significantly affect attitudes and intentions directly. The TRA model demonstrates considerable explanatory and predictive capabilities, indicating its effectiveness in understanding AI writing tool adoption among researchers. The study’s diverse sample across various disciplines and career stages provides insights that may be generalizable to similar academic contexts, though further research with larger samples is needed to confirm broader applicability. Results offer practical guidance for tool developers, academic institutions, and publishers aiming to foster responsible and efficient AI writing tool use in academia. Findings suggest strategies such as demonstrating clear productivity gains, establishing AI Writing Tool programs, and developing comprehensive training initiatives could promote responsible adoption. Strategies focusing on cultivating positive attitudes, leveraging social influence, and addressing perceived barriers could be particularly effective in promoting adoption. This pioneering study investigates researchers’ acceptance of AI writing tools using a technology acceptance model, contributing to the understanding of technology adoption in professional contexts and highlighting the importance of field-specific factors in examining adoption intentions and behaviors.
Journal Article
Does blockchain technology matter for supply chain resilience in dynamic environments? The role of supply chain integration
This study aims to empirically investigate the effect of blockchain technology (BCT) adoption on supply chain resilience (SCR), with the mediating role of supply chain integration (SCI) and the crucial effect of environmental dynamism (ED) as a moderator. Based on data collected from firms operating in the automotive industry in India, the proposed model was tested using Partial Least Squares Structural Equations Modelling (PLS-SEM) via SmartPLS software. The empirical results showed a positive effect of BCT on SCI, which in turn affects SCR. Importantly, SCI acts as a full mediator in the BCT-SCR relationship, which is moderated by ED, that is, the effect of BCT on SCR via SCI is strong when ED is high. This study offers the groundwork for operationalizing BCT in a supply chain context. It also contributes to SCR research by investigating how SCI mediates the effect of BCT on SCR. In addition, this study found a moderating effect of ED on the relationship between BCT and SCI. These results provide insights to auto manufacturers on ways to enhance SCR and ensure safe supply chain operations.
Journal Article
An improved deep learning model for predicting daily PM2.5 concentration
Over the past few decades, air pollution has caused serious damage to public health. Therefore, making accurate predictions of PM2.5 is a crucial task. Due to the transportation of air pollutants among areas, the PM2.5 concentration is strongly spatiotemporal correlated. However, the distribution of air pollution monitoring sites is not even making the spatiotemporal correlation between the central site and surrounding sites vary with different density of sites, and this was neglected by previous methods. To this end, this study proposes a weighted long short-term memory neural network extended model (WLSTME), which addressed the issue that how to consider the effect of the density of sites and wind conditions on the spatiotemporal correlation of air pollution concentration. First, a number of nearest surrounding sites were chosen as the neighbor sites to the central site, and their distance, as well as their air pollution concentration and wind condition, were input to multilayer perception (MLP) to generate weighted historical PM2.5 time series data. Second, historical PM2.5 concentration of the central site and weighted PM2.5 series data of neighbor sites were input into a long short-term memory (LSTM) to address spatiotemporal dependency simultaneously and extract spatiotemporal features. Finally, another MLP was utilized to integrate spatiotemporal features extracted above with the meteorological data of the central site to generate the forecasts future PM2.5 concentration of the central site. Daily PM2.5 concentration and meteorological data on Beijing–Tianjin–Hebei from 2015 to 2017 were collected to train models and to evaluate its performance. Experimental results with three existing methods showed that the proposed WLSTME model has the lowest RMSE (40.67) and MAE (26.10) and the highest p (0.59). Further experiments showed that in all seasons and regions, WLSTME performed the best. This finding confirms that WLSTME can significantly improve PM2.5 prediction accuracy.
Journal Article
Automatic clustering method to segment COVID-19 CT images
Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.
Journal Article