Rheumatoid arthritis (RA) is characterized by persistent joint inflammation, which is a defining feature of this chronic inflammatory condition. Considerable advancements have been made in the field of disease-modifying anti-rheumatic medicines (DMARDs), which effectively mitigate inflammation and forestall further joint deterioration. Anti-tumor necrosis factor-alpha (TNF-α) drugs, which are a class of biological DMARDs (bDMARDs), have been efficaciously employed in the treatment of RA in recent times Adalimumab, a TNF inhibitor, has demonstrated significant efficacy in reducing disease symptoms and halting disease progression in patients with RA. However, its use is associated with major side effects and high costs. In addition,

The nephrotoxicity induced by methotrexate is a severe condition that greatly affects its therapeutic potential and has a significant inflammatory component. Fimasartan is an angiotensin receptor blocker that offers organ-protective effects and may be useful in mitigating renal injury. The present study explored the anti-inflammatory potential of two doses of fimasartan against methotrexate-mediated nephrotoxicity. Albino rats were intraperitoneally administered a single methotrexate (20 mg/kg). Intraperitoneal treatment with fimasartan (5 or 10 mg/kg/day) was initiated on day two after methotrexate injection and continued for seven consecutive days. Methotrexate significantly increased serum urea, creatinine, and NGAL concentrations. It al

This study aimed to assess the possible association of oxytocin (OXT) gene with reproductive traits in two groups of Awassi ewes that differ in their reproductive potentials. Sheep were genotyped using PCR—single-stranded conformation polymorphism approach. Three genotypes were detected in exon 2, CC, CA, and AA, and a novel SNP was identified with a missense effect on oxytocin (c.188C > A → p.Arg55Leu). A significant (p < 0.01) association of p.Arg55Leu with the twinning rate was found as ewes with AA and CA genotypes exhibited, respectively a lower twinning ratio than those with the wild-type CC genotype. The deleterious impact of p.Arg55Leu was demonstrated by all in silico tools that were utilized to assess the effect of this varian

Oil well drilling fluid rheology, lubricity, swelling, and fluid loss control are all critical factors to take into account before beginning the hole's construction. Drilling fluids can be made smoother, more cost-effective, and more efficient by investigating and evaluating the effects of various nanoparticles including aluminum oxide (Al₂O₃) and iron oxide (Fe₂O₃) on their performance. A drilling fluid's performance can be assessed by comparing its baseline characteristics to those of nanoparticle (NPs) enhanced fluids. It was found that the drilling mud contained NPs in concentrations of 0,0.25, 0. 5, 0.75 and 1 g. According to the results, when drilling fluid was used without NPs, the coeff

Symmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a rand

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

The necessary optimality conditions with Lagrange multipliers  are studied and derived for a new class that includes the system of Caputo–Katugampola fractional derivatives to the optimal control problems with considering the end time free. The formula for the integral by parts has been proven for the left Caputo–Katugampola fractional derivative that contributes to the finding and deriving the necessary optimality conditions. Also, three special cases are obtained, including the study of the necessary optimality conditions when both the final time  and the final state  are fixed. According to convexity assumptions prove that necessary optimality conditions are sufficient optimality conditions.