Gestational and Newborn Screening Markers of Cystic Fibrosis

Newborn Screening for Cystic Fibrosis Cystic Fibrosis (CF) is an autosomal recessive disorder that results in a shortened lifespan if appropriate treatment is not initiated sufficiently early. Approximately one in every 2,000 to one in every 3,500 Caucasians bom in Europe is affected by CF, which manifests itself in severe disorders of the lungs and the digestive system. Newborn screening for CF based on the analysis of bloodspot immunoreactive trypsinogen (IRT) has recently been introduced in a number of countries, including the UK. Also, it has been reported that pancreatitis associated protein (PAP) is elevated in bloodspots from neonates with CF and a strategy involving a combination of IRT and PAP may offer enhanced specificity. The study aims to develop an algorithm based on universal IRT measurements and subsequent PAP measurements in newborns with elevated IRT levels which will allow high detection rates to be maintained while at the same time decreasing the number of cases referred for DNA analysis and the accompanying detection of CF carriers. Prenatal Screening The aim of antenatal screening programmes is to offer couples reproductive choice, for example, termination of the pregnancy if the foetus is found to have a serious disorder such as CF. In this study, the physiological effects on women pregnant with a baby affected by CF were investigated. Several maternal serum markers were tested in this project in blood samples from women carrying a CF foetus. These markers are known to be associated with a number of adverse outcomes in pregnancy such as low birth weight, pre-term birth, pre-eclampsia, and stillbirth. The markers tested were alpha-fetoprotein (AFP), pregnancy-associated plasma protein (PAPP-A), human chorionic gonadotrophin (hCG), free beta subunit of hCG (FBhCG), unconjugated estriol (UE3) and Inhibin-A. (Abstract shortened by ProQuest.)

Pharmacy Students’ Perceptions and Attitudes towards Online Education during COVID-19 Lockdown in Saudi Arabia

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/In March 2020, a national lockdown in Saudi Arabia due to the pandemic forced all educational institutions to complete their academic year via online education. This study aims to explore pharmacy students’ perceptions and assess their attitude towards online education during the lockdown. A cross-sectional self-administered survey was designed to collect responses of pharmacy students (from one college of pharmacy in Saudi Arabia) from December 2020 through January 2021. A total of 241 students completed the survey. Students’ responses indicated that they had easy access to the technology, online skills, motivation and overall favorable acceptance for online learning and examinations. There was a significant difference in the mean scores between the students from different years of study (p = 0.013) related to technology access, and the male students were in significantly more favor of online examinations than female students (p = 0.009). The majority of the students indicated that the lockdown had no or negative impact on their learning and training. Students have general acceptance for online education delivery due to more technology access and online skills. More research should explore the factors affecting and the extent of the impact of online education on student learning and training.Peer reviewe

Effectiveness of low to moderate physical exercise training on the level of low-density lipoproteins: a systematic review

Background. Regular exercise reduces risk factors associated with cardiovascular disease (CVD). Elevated low-density lipoprotein (LDL) contributes to atherosclerosis formation, which is associated with an increased risk of CVD. The relationship between exercise therapy and lipid levels has been widely studied, but it is established that high-intensity exercise improves lipid profile. However, the effectiveness of low- to moderate-intensity exercise in altering LDL levels is controversial. This review aims to identify the current evidence and existing gaps in literature in this area. Methods. We searched and reviewed various randomized controlled clinical trials in the electronic databases EMBASE, CINAHL, the Web of Science, Cochrane, Pedro, Medline (PubMed), and Google Scholar using the keywords “low and moderate aerobic training,” “exercise”, “low-density lipoproteins,” “cholesterol,” “atherosclerosis,” and “coronary artery diseases markers.” We included studies that involved low- and/or moderate-intensity exercise training in apparently healthy adults over a period of 8 weeks and its effect on LDL levels. We selected a total of 11 studies from 469; nine were randomized controlled trials and two were systematic reviews. Results. Aerobic exercise of both low and moderate intensity resulted in a significant reduction of total cholesterol. Effects on low-density lipoprotein levels were significant, and most of the studies showed changes in the level without significant relation to the type of exercise. At the same time, exercise improved the health status and physical fitness of all the participants in the included studies. Conclusion. This study found that low- and moderate-intensity exercise and low-density lipoprotein levels were not proven to be significantly related, except in a few studies that were limited to dyslipidemia population

Studying the Role of Cerebrovascular Changes in Different Compartments in Human Brains in Hypertension Prediction

Hypertension is a major cause of mortality of millions of people worldwide. Cerebral vascular changes are clinically observed to precede the onset of hypertension. The early detection and quantification of these cerebral changes would help greatly in the early prediction of the disease. Hence, preparing appropriate medical plans to avoid the disease and mitigate any adverse events. This study aims to investigate whether studying the cerebral changes in specific regions of human brains (specifically, the anterior, and the posterior compartments) separately, would increase the accuracy of hypertension prediction compared to studying the vascular changes occurring over the entire brain’s vasculature. This was achieved by proposing a computer-aided diagnosis system (CAD) to predict hypertension based on cerebral vascular changes that occur at the anterior compartment, the posterior compartment, and the whole brain separately, and comparing corresponding prediction accuracy. The proposed CAD system works in the following sequence: (1) an MRA dataset of 72 subjects was preprocessed to enhance MRA image quality, increase homogeneity, and remove noise artifacts. (2) each MRA scan was then segmented using an automatic adaptive local segmentation algorithm. (3) the segmented vascular tree was then processed to extract and quantify hypertension descriptive vascular features (blood vessels’ diameters and tortuosity indices) the change of which has been recorded over the time span of the 2-year study. (4) a classification module used these descriptive features along with corresponding differences in blood pressure readings for each subject, to analyze the accuracy of predicting hypertension by examining vascular changes in the anterior, the posterior, and the whole brain separately. Experimental results presented evidence that studying the vascular changes that take place in specific regions of the brain, specifically the anterior compartment reported promising accuracy percentages of up to 90%. However, studying the vascular changes occurring over the entire brain still achieve the best accuracy (of up to 100%) in hypertension prediction compared to studying specific compartments

A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air

Prevalence And Risk Factors of Eye Allergies Among Adults In Ksa: A Cross-Sectional Study

Objective: To determine the prevalence of eye allergies and associated risk factors among adults in KSA. Methods: This research employs a cross-sectional study design to assess the prevalence and risk factors of eye allergies among adults in the Kingdom of Saudi Arabia (KSA). A cross-sectional approach allows for the collection of data at a single point in time, providing a snapshot of the condition's status within the study population. Results: The study included 640 participants. The most frequent age among them was 18-28 years (n= 331, 51.7%), followed by 40-50 years (n= 139, 21.7%). The most frequent gender among study participants was female (n= 389, 60.8%) followed by male (n= 251, 39.2 The most frequent nationality among study participants was Saudi (n= 613, 95.8%) followed by non-Saudi (n= 27, 4.2%). The educational level among study participants with most of them being the university (n= 553, 86.4%) followed by the school (n= 85, 13.3%). The work nature among study participants with most of was inside the building. Participants were asked if they had an eye problem that affected their daily life. The most frequent answer was moderately (n= 309, 48.3%) followed by never (n= 271, 42.3%), and the least was a lot (n=60, 9.4%).  Conclusion: The results of the study showed that most of the study participants are Saudis and most of them work inside the building. The majority have university education, and the largest percentage of participants are women. Most of the participants are non-smokers. Most study participants had good social communication

Stimulatory effects of Lycium shawii on human melanocyte proliferation, migration, and melanogenesis: In vitro and in silico studies

There is no first-line treatment for vitiligo, a skin disease characterized by a lack of melanin produced by the melanocytes, resulting in an urgent demand for new therapeutic drugs capable of stimulating melanocyte functions, including melanogenesis. In this study, traditional medicinal plant extracts were tested for cultured human melanocyte proliferation, migration, and melanogenesis using MTT, scratch wound-healing assays, transmission electron microscopy, immunofluorescence staining, and Western blot technology. Of the methanolic extracts, Lycium shawii L. (L. shawii) extract increased melanocyte proliferation at low concentrations and modulated melanocyte migration. At the lowest tested concentration (i.e., 7.8 μg/mL), the L. shawii methanolic extract promoted melanosome formation, maturation, and enhanced melanin production, which was associated with the upregulation of microphthalmia-associated transcription factor (MITF), tyrosinase, tyrosinase-related protein (TRP)-1 and TRP-2 melanogenesis-related proteins, and melanogenesis-related proteins. After the chemical analysis and L. shawii extract-derived metabolite identification, the in silico studies revealed the molecular interactions between Metabolite 5, identified as apigenin (4,5,6-trihydroxyflavone), and the copper active site of tyrosinase, predicting enhanced tyrosinase activity and subsequent melanin formation. In conclusion, L. shawii methanolic extract stimulates melanocyte functions, including melanin production, and its derivative Metabolite 5 enhances tyrosinase activity, suggesting further investigation of the L. shawii extract-derived Metabolite 5 as a potential natural drug for vitiligo treatment

Interaction Analysis of MRP1 with Anticancer Drugs Used in Ovarian Cancer: In Silico Approach

Multidrug resistance (MDR) is one of the major therapeutic challenges that limits the efficacy of chemotherapeutic response resulting in poor prognosis of ovarian cancer (OC). The multidrug resistance protein 1 (MRP1) is a membrane-bound ABC transporter involved in cross resistance to many structurally and functionally diverse classes of anticancer drugs including doxorubicin, taxane, and platinum. In this study, we utilize homology modelling and molecular docking analysis to determine the binding affinity and the potential interaction sites of MRP1 with Carboplatin, Gemcitabine, Doxorubicin, Paclitaxel, and Topotecan. We used AutoDock Vina scores to compare the binding affinities of the anticancer drugs against MRP1. Our results depicted Carboplatin \u3c Gemcitabine \u3c Topotecan \u3c Doxorubicin \u3c Paclitaxel as the order of binding affinities. Paclitaxel has shown the highest binding affinity whereas Carboplatin displayed the lowest affinity to MRP1. Interestingly, our data showed that Carboplatin, Paclitaxel, and Topotecan bind specifically to Asn510 residue in the transmembrane domains 1 of the MRP1. Our results suggest that Carboplatin could be an appropriate therapeutic choice against MRP1 in OC as it couples weakly with Carboplatin. Further, our findings also recommend opting Carboplatin with Gemcitabine as a combinatorial chemotherapeutic approach to overcome MDR phenotype associated with recurrent OC. View Full-Tex

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder