Background: One way to target polypharmacy and inappropriate medication in hemodialysis (HD) patients is with medication deprescribing. Objective: To assess the impact of implementing a pharmacist-led deprescribing program on medication adherence among HD patients. Method: A prospective interventional, one-group pretest-posttest-only design study was conducted at a hemodialysis center in Wasit Governorate, Iraq. Medication reconciliation followed by medication review based on the deprescribing program was done for all eligible patients, and the patients were monitored for three months for any possible complications. Results: Two hundred and seventy patients were screened for eligibility. Only one hundred and eighteen were enrolled i

The ground state charge, neutron, proton and matter densities, the associated nuclear radii and the binding energy per nucleon of 8B, 17Ne, 23Al and 27P halo nuclei have been investigated using the Skyrme–Hartree–Fock (SHF) model with the new SKxs25 parameters. According to the calculated results, it is found that the SHF model with these Skyrme parameters provides a good description on the nuclear structure of above proton-rich halo nuclei. The elastic charge form factors of 8B and 17Ne halo nuclei and those of their stable isotopes 10B and 20Ne are calculated using plane-wave Born approximation with the charge density distributions obtained by SHF model to investigate the effect of the extended charge distributions of proton-rich nucl

Purpose: This study aimed to evaluate the influence of vitamin D3 levels on bone density, primary dental implant stability, and successful osseointegration.

Materials and Methods: Clinical and radiological examination with a standardized cone‐beam computed tomography (CBCT) machine and laboratory investigation for serum levels of vitamin D3 were performed for all patients. Only patients in need of single or multiple straightforward dental implant surgery in either jaw with no history of systemic disease or condition that may interfere with bone healing were included in this study to receive the dental implant by the same oral and maxillofacial surgeon, which re‐opened

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,

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