Browse Articles

Solvent-induced photophysical properties and stability of clonazepam and Chlordiazepoxide

Comparative assessment of landslide susceptibility in Fugu town using machine learning models at multiple grid resolutions

The nephroprotective potential of russelioside B isolated from Caralluma quadrangula in gentamicin-induced acute kidney injury via modulation of SIRT-1 pathway

Abstract Using nephrotoxic antibiotics such as gentamicin may result in acute renal damage. This study aimed to investigate the nephroprotective effect of russelioside B (RB) isolated from C ralluma quadrangula (Forssk.) N.E.Br against gentamicin-induced acute kidney injury. Twenty male rats were randomly distributed into four groups (five animals each). Normal, Gentamicin, Genta + 50RB, and Genta + 75RB groups. The RB was taken orally for 3 days at a dose of 50 and 75 mg/Kg B.wt) before injection with gentamicin, followed by the same dose along with gentamicin injection (I/P) for 7 consecutive days. At the end of the study, kidneys and blood samples were collected. The blood samples were tested for BUN and Creatinine. Tissue samples were tested for SOD, MDA, and NO. Genes expression of IGF-1, iNOS and Bcl2 by RT-PCR were tested. Histopathological evaluation was performed along with immunohistochemistry for SIRT1, NQO1, Nrf-2, TNF-α, and NF-қB. RB treated groups showed reduction in serum BUN and creatinine levels. Along with reduced MDA and nitric oxide while there was an elevation in SOD, SIRT1, NQO-1, Nrf2, and IGF-1 with a decrease in TNF-α, NF-қB and iNOS. Confirming the anti-oxidant and anti-inflammatory properties of RB. RB treatment resulted in elevation in the anti-apoptotic protein Bcl2 and reduction in BAX confirming the anti-apoptotic potential of RB. Gentamicin-induced histopathological alterations were also alleviated with RB treatment. The findings of the study proved the nephroprotective potential of RB.

Dynamic behavior and micro–meso scale fracture mechanisms of sandstone under long-term water immersion

Enhanced mechanical and thermal properties of polyester/glass/sheep wool fiber hybrid composites by successful replacement glass with wool

Prediction of rapid chloride permeability using silica fume, fly ash, GGBS and micro fibers based geopolymer concrete

Development and validation of a predictive model for cervical insufficiency incorporating AMH and androstenedione

Abstract This study aims to develop a predictive model for cervical insufficiency (CI) in women who undergo in vitro fertilization and embryo transfer (IVF-ET) based on relevant indicators measured prior to pregnancy. A total of 2,494 women who received IVF-ET at the Reproductive Medical Center of the Third Hospital of Peking University between 2016 and 2022 were included. All participants ultimately delivered at the same institution. 1,745 patients were assigned to the training cohort and 749 to the validation cohort. Both univariate logistic regression analysis and multiple logistic regression analysis were conducted to establish the CI prediction model. Among the 2,494 cases, the incidence rate of CI was 3.2%. Risk factors identified to be associated with CI included body mass index (BMI) > 22.83 kg/m 2 , testosterone (T) level > 0.74 nmol/L, androstenedione (A) level > 11.45 nmol/L, anti-Müllerian hormone (AMH) level > 3.50 ng/ml, frequency of hysteroscopic surgery, number of previous pregnancies (gravidity), and pre-pregnancy diabetes. Cervical length > 3.15 cm is a protective factor for cervical insufficiency. Conversely, factors such as the endometrial preparation regimen, occurrence of an intrauterine operation within six months before pregnancy, and the uterine length were not found to be significant risk factors for CI. The area under the curve (AUC) for this model achieved 0.819, with a 95% confidence interval of 0.758 to 0.881. Despite the lack of external validation from independent cohorts, this study successfully developed a comprehensive predictive model for CI in women undergoing IVF-ET, providing a preliminary exploration for early prediction and intervention strategies.

Dynamic response analysis of semi-rigid asphalt pavement under combined low-temperature and heavy-load conditions

Pollen morphology of three invasive Impatiens species in Europe under varying habitat conditions—a case study from Poland

Operational characteristics and blade fatigue life analysis of a novel variable-pitch wind turbine under natural wind conditions

An evidence theory based multiple model fusion method for fault diagnosis of distribution line

The RNA-binding protein CPEB1 marks healthy adult β cells in mice but is dispensable for β cell identity and function

Enhanced PAM50 subtyping of breast cancer implemented in the PCAPAM50 R package

Integrating biochar, compost, and chemical fertilizer improves maize yield and soil health in the guinea savannah: evidence from two cropping seasons in Northern Ghana

Syndemic perspective of how people living with HIV faced the COVID-19 crisis in North Africa

Clinical features and subgroup patterns in elderly and super-elderly TMD patients

Characterization of spatial and temporal variations of CO2 concentration on tropical Island and analysis of influencing factors

Abstract In this study, the spatial and temporal variations and distribution characteristics of the carbon dioxide (CO 2 ) concentration on Hainan Island are analyzed using GOSAT L3 data from 2011 to 2024, and the effects of various factors impacting the CO 2 concentration on Hainan Island are discussed. The results indicate that from 2011 to 2024, the CO 2 concentration on Hainan Island showed an increasing trend, with a fast growth rate in the early period and a slow growth rate in recent years with the implementation of the dual-carbon strategy. The spatial distribution is affected by anthropogenic activities, topography, vegetation and solar radiation, and the overall CO 2 concentration pattern is high in the north and low in the south. Human activities are the most important source of carbon on Hainan Island, vegetation is the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation play roles in suppressing CO 2 . The CO 2 concentration on Hainan Island is expected to continue to increase at a slow rate and may display a decreasing trend in the future.

Effect of a PEN-3 model-based educational intervention on cigarette smoking in Iranian patients with myocardial infarction

Percolation effect induced significant changes in the complex permittivity and permeability of silver-epoxy nano-composites

Optimized YOLOv11m for real-time high-speed railway catenary defect detection

Abstract Real-time defect detection of high-speed railway catenary components remains challenging due to the prevalence of small-sized parts (e.g., cotter pins, fasteners) and the computational constraints of deployment platforms. While existing YOLO-based models offer a balance between speed and accuracy, they often struggle with small object detection and suffer from high computational costs. To address these limitations, this paper proposes an optimized YOLOv11m model, termed MSIM-YOLOv11m, which integrates three novel modules: large separable kernel attention (LSKA) for enhanced feature extraction, bidirectional feature pyramid network (BiFPN) for efficient multi-scale fusion, and adaptive kernel convolution (AKConv) for flexible feature learning. Experimental results on a dedicated catenary dataset show that the proposed model achieves a mAP50-95 of 78.3% and a small-target AP of 64.7%, while reducing computational cost by 50.5% compared to YOLOv9m. The model provides a lightweight and accurate solution suitable for real-time inspection applications.The code has been uploaded to https://github.com/1748125472/MSIM-Yolov11m/tree/master .