Enhancing the Knowledge of Cervical Cancer Screening among Female Nursing Students: An Interventional Educational Program

Background: Cervical cancer is a growing health risk facing women worldwide with the human papillomavirus (HPV) as the primary underlying cause. Pap smear is a simple screening test that can detect early changes in cervical cells, which might develop into cancer cells. Raising awareness of cervical cancer prevention has a significant impact on decreasing the burden of the disease. The aim of the study is to assess female nursing students' knowledge on early detection and screening of cervical cancer, and to determine the effectiveness of an educational program. Methods: A quasi-experimental research design (one group for pre- and post-tests) was utilized with a convenience sample of 130 female nursing students in one of the nursing colleges in Saudi Arabia. The study’s educational intervention included information about anatomy of genital tract and the importance of regular check-ups. The pre- and post-tests were applied to identify changes after intervention measures. Results: The mean age of the participants were 21.32 years (SD: 1.34). The findings revealed a significant improvement of post-test students’ knowledge in all items related to risk factors, signs and symptoms, occurrence, identification of HPV as causative agent, vaccination against HPV, and finally Pap smear for early detection and screening of cervical cancer. Conclusion: The study results support implementing educational intervention to improve nursing students' knowledge and awareness about cervical cancer prevention. Furthermore, it is imperative that cervical cancer awareness education modules should be developed and integrated within the nursing curriculum. Further studies with large sample size are recommended to increase generalization of the results.  Key words: cervical cancer, education program, primary prevention, nursing students, Saudi Arabi

A multiscale model for the rupture of linear polymers in strong flows

Abstract Polymer-containing solutions used across research and industry are commonly exposed to mechanically harsh fluid processes, for example shear and extensional forces during flow through porous media or rapid micro-dispensing of biopharmaceutical molecules. These forces are strong enough to break the covalent bonds in the polymer backbone. As this scission phenomenon can change the functional and fluid-flow properties as well as introduce reactive radicals into the solution, it must be understood and controlled. Experiments and models to-date have only provided partial or qualitative insights into this behaviour. Here we build a link between the molecular-scale degradation models and the macro-scale laminar flow of dilute solutions in any given geometry. A free-draining bead-rod model is used to investigate rupture events at the molecular scale. It is shown by uniaxial extension simulations of an ensemble of chains that scission can be conveniently described at the macroscopic scale as a first order reaction whose rate is a function of the conformation tensor of the macromolecules and the velocity gradient of the flow. This approach is implemented in the finite volume code OpenFOAM by elaborating an appropriate constitutive equation for the conformation tensor. The macroscopic model is run and analysed for ultra-dilute solutions of poly(methyl methacrylate) in ethyl acetate and polyethylene oxide in water, using the geometry of an abrupt contraction flow and neglecting any viscoelastic effect. This multi-scale approach bridges the gap between phenomenological observations of mechanically-induced chemical degradation in large scale applications and the rich field of molecular-scale models of macromolecules under flow.King Abdulaziz City for Science and Technology (KACST

Laminar flow-induced scission kinetics of polymers in dilute solutions

AbstractKing Abdulaziz City for Science and Technology (KACST) EPSRC (Grant No. EP/S009000/1

Endotoxin and bone turnover markers in postmenopausal women with and without osteoporosis

Bacterial lipopolysaccharide (LPS) also known as endotoxin, which represents the outer cell wall membrane of gram-negative bacteria has been implicated as the major bacterial bone-resorbing factor. Recently, the most prevalent form of clinically significant osteopenia and osteoporosis involves periodontitis and otitis media by gram-negative bacteria. The aim of the study is to evaluate circulating endotoxin levels and to study the association amongst endotoxin and bone turnover markers in a cohort of Saudi postmenopausal women with or without osteoporosis. We determined the levels of endotoxin, bone turnover markers, 25-OH vitamin D total and corrected calcium in 100 Saudi postmenopausal women with osteoporosis and 100 women without osteoporosis were taken under the supervision of qualified physicians in the primary care centers in Riyadh. Serum endotoxin, NTx, osteocalcin, PTH, 25-OH vitamin D total and calcium were analyzed. Serum NTX and PTH levels in patients with osteoporosis were significant higher than controls. Serum endotoxin was significantly and positively associated with calcium in all subject and controls. Endotoxin was positively associated with NTX in both groups but not with osteocalcin, PTH or 25-OH vitamin D. Findings of the present study implicate a role for endotoxin-mediated inflammation in patients with osteoporosis

Modeling for the Relationship between Monetary Policy and GDP in the USA Using Statistical Methods

The Federal Reserve has played an arguably important role in financial crises in the United States since its creation in 1913 through monetary policy tools. Thus, this paper aims to analyze the impact of monetary policy on the United States' economic growth in the short and long run, measured by Gross Domestic Product (GDP). The Vector Autoregressive (VAR) method explores the relationship among the variables, and the Granger causality test assesses the predictability of the variables. Moreover, the Impulse Response Function (IRF) examines the behavior of one variable after a change in another, utilizing the time-series dataset from the first quarter of 1959 to the second quarter of 2022. This work demonstrates that expansionary monetary policy does have a positive impact on economic growth in the short term though it does not last long. However, in the long term, inflation, measured by the Consumer Price Index (CPI), is affected by expansionary monetary policy. Therefore, if the Federal Reserve wants to cease the expansionary monetary policy in the short run, this should be done appropriately, with the fiscal surplus, to preserve its credibility and trust in the US dollar as a global store of value asset. Also, the paper's findings suggest that continuous expansion of the Money Supply will lead to a long-term inflationary problem. The purpose of this research is to bring the spotlight to the side effects of expansionary monetary policy on the US economy, but also allow other researchers to test this model in different economies with different dynamics

A Bayesian approach to wavelet-based modelling of discontinuous functions applied to inverse problems

Inverse problems are examples of regression with more unknowns than the amount of information in the data and hence constraints are imposed through prior information. The proposed method defines the underlying function as a wavelet approximation which is related to the data through a convolution. The wavelets provide a sparse and multi-resolution solution which can capture local behaviour in an adaptive way. Varied prior models are considered along with level-specific prior parameter estimation. Archaeological stratigraphy data are considered where vertical earth cores are analysed producing clear piecewise constant function estimates

Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data

Assessing the state of health research in the Eastern Mediterranean Region.

Member states across the Eastern Mediterranean region face unprecedented health challenges, buffeted by demographic change, a dual disease burden, rising health costs, and the effects of ongoing conflict and population movements - exacerbated in the near-term by instability arising from recent political upheaval in the Middle East. However, health actors in the region are not well positioned to respond to these challenges because of a dearth of good quality health research. This review presents an assessment of the current state of health research systems across the Eastern Mediterranean based on publicly available literature and data sources. The review finds that - while there have been important improvements in productivity in the Region since the early 1990s - overall research performance is poor with critical deficits in system stewardship, research training and human resource development, and basic data surveillance. Translation of research into policy and practice is hampered by weak institutional and financial incentives, and concerns over the political sensitivity of findings. These problems are attributable primarily to chronic under-investment - both financial and political - in Research and Development systems. This review identifies key areas for a regional strategy and how to address challenges, including increased funding, research capacity-building, reform of governance arrangements and sustained political investment in research support. A central finding is that the poverty of publicly available data on research systems makes meaningful cross-comparisons of performance within the EMR difficult. We therefore conclude by calling for work to improve understanding of health research systems across the region as a matter of urgency