Diabetic Nephropathy(DN) is a complex disease manifested by persistence microalbuminuria   occurring due to the interaction between hemodynamic and metabolic pathway that activates the local renin-angiotensin-aldosterone system resulting in a decline in renal functions.

This study aimed to quantify the associations between serum aldosterone concentration and fetuin- A as a marker of calcification in type 2 diabetic patients with and without microalbuminuria from one side, and study the possible relationship between aldosterone and fetuin-A with glycemic indices, serum electrolyte, renal function and microalbuminuria and body mass index from the other side.

A case-control study involved eighty-six adult subjects

Ganciclovir (GCV) is a drug included in BCS-Class III, having high solubility and low permeability. It is a synthetic acyclic nucleoside analog of 2′-deoxyguanosine, considered a potent inhibitor of herpes viruses and cytomegalovirus (CMV) infection. Herpes simplex virus (HSV) infections are very common and are also considered a major cause of corneal blindness. This study intended to advance a pioneering nanostructured lipid carriers (NLCs) system for improving the ocular permeability of GCV. Several procedures were used for the preparation. Cold homogenization, solvent injection, and emulsifi cationultrasonication methods. A mixture of palmitic acid (PA) and oleic acid (OA) as a lipid matrix, cremophore EL, and transcutol HP wer

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Chitinase-3-like 1 protein (YKL-40) is a glycoprotein primarily produced in the arthritic joint and plays a crucial role in inflammatory processes. The aim of the study is to establish the role of YKL-40 as a biomarker for rheumatoid arthritis (RA) compared to proinflammatory biomarkers and disease activity. The study included 58 patients and 18 control. Diseases activity score (DAS-28) and clinical disease activity index (CDAI) were measured. Serum level of YKL-40, tumor necrosis factor-α (TNF-α), interleukin-1B (IL-1β), erythrocyte sedimentation (ESR), rheumatoid factor (RF), C-reactive protein (CRP), and anti-citrullinated protein antibody (ACPA) were assessed. The results showed that the median serum YKL-40 level which was 5.42 

Photonic crystal fiber interferometers (PCFIs) are widely used for sensing applications. This work presented solid core-PCFs based on Mach-Zehnder modal interferometer for sensing refractive index. The general structure of sensor was applied by splicing short lengths of PCF in both sides with conventional single mode fiber (SMF-28).To apply modal interferometer theory collapsing technique based on fusion splicing used to excite higher order modes (LP01 and LP11). A high sensitive optical spectrum analyzer (OSA) was used to monitor and record the transmitted wavelength. This work studied a Mach-Zahnder interferometer refractive index sensor based on splicing point tapered SMF-PCF-SMF. Relation between refractive index sensitivity and tape

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing