Al Muthanna University is an Iraqi university located in Samawah, Al Muthanna Province, Iraq. It was established in 2007..
One well-known hazardous pollutant that damages testicles in both humans and animals is chromium hexavalent (CrVI). It has been established that zinc (Zn) is essential for spermatogenesis. Thus, the current work examines how Zn might shield rat testis from the detrimental effects of CrVI. Twenty-eight male rats were assigned to four groups the first was the control group; the second was given zinc sulfate (Zn; 1 mg/kg BW); the third received hexavalent chromium (CrVI; 2.5 mg/kg BW); and the fourth served as the protective group (Zn was given 60 min before CrVI). All treatments were given orally every day for four weeks. According to the results, rats intoxicated with CrVI exhibited a considerable decrease in body weight, enzymatic antioxidants, reduced glutathione, hydroxysteroid dehydrogenases (3β HSD, and 17β HSD), aminotransferases, and acid phosphatase activities, and a significant increase in the oxidative stress profile (TBARS, H2O2, PCC, XO, and NO). Testicular Bax, Cas-3, Bcl-2, Beclin-1, Nrf2, hormone levels, sperm quality, histopathological, and P53 immunohistochemical examinations were also shown to have significant changes compared to the control. Besides that, Zn pretreatment before CrVI intoxication improved the architecture of testicular tissue and P53 expression. Also, it significantly restored the majority of biochemical and molecular markers compared to the CrVI group. Additionally, oxidative stress indicators showed a notable change in response to individual Zn intake compared to the control. In conclusion, Zn significantly protects against CrVI-induced testicular failure, making it a unique strategy for processing heavy metal poisoning.
Organic semiconductors have revolutionized electronics, but their amorphous nature hinders performance and stability. Crystallinity overcomes these limitations; however, the design of materials that combine high crystallinity, optimal thermal properties, and ease of synthesis remains a significant challenge. A machine learning (ML)-assisted approach combined with the density functional theory (DFT) study was employed to generate a vast chemical space of crystallizable organic semiconductors. By breaking retrosynthetically interesting chemical species (BRICS), over 1700 new organic semiconductors with promising synthetic accessibility (SA) were designed. ML algorithms, specifically extra trees (ET) and random forest, were used to predict the melting temperatures (T-m) of these semiconductors, yielding good R-squared (R-2) values of 0.94-0.96. Dimensionality reduction analysis reveals that the t-distributed stochastic neighbor embedding (t-SNE) components 1 and 2 of these semiconductors ranged within the same value (-5 to 5), indicating a high degree of similarity. Furthermore, analysis of SA showed that new organic semiconductors with SMILES lengths between 15 and 40 are more likely to be easily synthesized. Based on these findings, 20 new candidate semiconductors were identified for practical synthesis and analysis. DFT calculations were employed to study the optoelectronic properties of chromophores. This study demonstrates the power of ML-assisted design in generating crystallizable organic semiconductors with enhanced SA. The findings are expected to contribute significantly to the development of high-performance organic electronics.
The integration of Aluminum Gallium Arsenide (AlGaAs) into microstructured Photonic Crystal Fibers (PCFs) creates a highly nonlinear platform which is promising for compact photonic devices. This work presents a comprehensive numerical investigation of ultrashort pulse propagation in AlGaAs glass PCFs using a generalized nonlinear Schr & ouml;dinger equation model that incorporates higher-order dispersion, self-steepening, stimulated Raman scattering, and a competing cubic-quintic nonlinearity. The designed PCF geometry, featuring a seven-ring hexagonal lattice with graded air-hole diameters, is first analyzed to demonstrate strong modal confinement and engineerable anomalous dispersion at telecommunication wavelengths. The exceptionally high nonlinear coefficients, reaching gamma(1) = 24359W(-1)km(-1 )and gamma(2) = -285W(-2)km(-1), facilitate nonlinear processes at femtojoule energy levels. Subsequent analysis reveals the profound influence of higher-order effects on Modulational Instability (MI) gain spectra and soliton dynamics. Self-steepening introduces spectral asymmetry with blue-side suppression, while the Raman response causes a continuous red-shift and temporal delay of solitons via the soliton self-frequency shift. A parametric study further elucidates how pump power, dispersion, and nonlinearity systematically control MI gain bandwidth and amplitude. These findings provide a foundational framework for designing and optimizing AlGaAs PCF-based devices for applications in supercontinuum generation, wavelength conversion, and integrated nonlinear photonics. The results underscore the critical importance of including higher-order nonlinear terms when modeling pulse propagation in semiconductor-doped microstructured fibers.
BACKGROUND:Oral squamous cell carcinoma (OSCC) represents a significant global health burden with complex pathophysiology involving chronic inflammation and oxidative stress. Systemic inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), have emerged as potential prognostic indicators, while oxidative stress biomarkers such as 8-hydroxy-2'-deoxyguanosine (8-OHdG) reflect DNA damage associated with carcinogenesis. OBJECTIVE:This study aimed to evaluate the diagnostic potential of NLR, PLR, and oxidative stress biomarkers in OSCC patients, investigating the relationship between systemic inflammation, oxidative DNA damage, and antioxidant status in the context of oral carcinogenesis. METHODS:A case-control study was conducted involving 138 participants (82 OSCC patients and 56 healthy controls) aged 28-48 years. Comprehensive hematological analysis was performed using automated analyzers, while serum concentrations of interleukin-6 (IL-6), C-reactive protein (CRP), 8-OHdG, and vitamin C were quantified using enzyme- linked immunosorbent assay (ELISA) techniques. Statistical analysis included independent t-tests and Pearson correlation analysis. RESULTS:OSCC patients demonstrated significantly elevated levels of white blood cells (13.01±4.31 vs. 4.54±7.32 ×109/L), NLR (6.84±0.88 vs. 1.91±0.34), PLR (185.02±40.10 vs. 91.88±17.77), and inflammatory biomarkers, including IL-6 (142.31±5.24 vs. 38.32±6.32 pg/mL) and CRP (43.30±3.42 vs. 8.11±2.21 mg/L), compared to controls (all p<0.01). Oxidative stress marker 8-OHdG was markedly elevated (31.82±2.32 vs. 5.78±1.76 ng/dL, p<0.001), while vitamin C levels were significantly reduced (3.53±2.35 vs. 4.88±2.42 mg/dL, p<0.001). Strong positive correlations were observed between CRP and IL-6 (r=0.544, p<0.005) and 8-OHdG (r=0.386, p<0.007). DISCUSSION:The significant elevations in inflammatory and oxidative stress biomarkers, coupled with their strong correlations with tumor stage, suggest these markers reflect the complex interplay between chronic inflammation and oxidative damage in OSCC pathogenesis. The exceptional diagnostic accuracy of the combined biomarker panel (NLR + IL-6 + 8-OHdG; AUC = 0.995) demonstrates the potential clinical utility of integrating multiple pathophysiological pathways for improved OSCC detection and risk stratification. CONCLUSION:Elevated NLR and PLR values, combined with increased oxidative stress markers and diminished antioxidant capacity, reflect the complex interplay between chronic inflammation and oxidative damage in OSCC pathogenesis. These biomarkers may serve as valuable adjunctive tools for early detection and prognostic assessment in oral cancer management.
A dataset of 2406 ternary transition metal compounds (TTMCs) was compiled to predict chemical stability using machine learning (ML). Important molecular descriptors were calculated, including Heavy Atom Count, Ring Count, Topological Polar Surface Area (TPSA), Kier's shape indices (Kappa2, Kappa3), and Labute's Approximate Surface Area (LabuteASA), to correlate with stability indicators such as Stability Order Group (SOG), Photobleaching Quantum Yield, and Photostability Index. The Convex Hull Diagram (CHD) reveals the distribution of chemical energy and structural trends. Multiple ML models were employed to train the dataset and evaluate predictive performance for chemical stability parameters, utilizing both classification and regression techniques. t-distributed Stochastic Neighbor Embedding (t-SNE) and K-Means clustering were used to uncover complex relationships between descriptors and chemical stability, facilitating material categorization. Feature importance analysis highlighted Ring Count, TPSA, Kappa2, Kappa3, and LabuteASA as the most significant descriptors for defining chemical stability. This approach streamlines material design, reduces experimental trial-and-error, and informs the development of novel materials with enhanced stability and performance.
