Gender Environment Adjustment of Saudi Arabian Students in the United States of America

Saudi students come from a country where gender norms are conventional, conservative, and governed by religious principles. Some of the challenges Saudi students face when they meet more permissive Western gender expressions have been outlined in other studies. By concentrating on how couples adjust, this study builds on prior studies. This study aimed to look at how Saudi Arabian students adapt to the diverse gender environments in the United States. Twenty Saudi students (10 couples) participated in qualitative interviews, and three major groups of tactics emerged. First, Cultural Strategies document the ways by which the students attempted to negotiate American daily life (religion, education, food, dress, etc). Second, Gender Strategies address gender relations, taking into account both changes brought on by American culture (such as women driving) and difficulties posed. By the co-education of men and women. Last but not least, social strategies deal with Saudi students choices in their social lives, including tactics for interacting with and avoiding Americans. Together, the results typically lend support to earlier studies. However, the emphasis on couples also offers fresh perspectives. Most notably, the study reveals that being in the US impacts both men and women. This is particularly true when it comes to childcare and housework. Additionally, even while some of the students who were questioned hoped to bring some of their fresh perspectives and experiences back to Saudi Arabia (such as womens increased independence and chances for meaningful employment), in other areas their ties to Saudi society were strengthened (the modesty of women; no drugs and alcohol)

Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity.Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity.Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen.Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment

Municipal mortality due to thyroid cancer in Spain

BACKGROUND: Thyroid cancer is a tumor with a low but growing incidence in Spain. This study sought to depict its spatial municipal mortality pattern, using the classic model proposed by Besag, York and Mollié. METHODS: It was possible to compile and ascertain the posterior distribution of relative risk on the basis of a single Bayesian spatial model covering all of Spain's 8077 municipal areas. Maps were plotted depicting standardized mortality ratios, smoothed relative risk (RR) estimates, and the posterior probability that RR > 1. RESULTS: From 1989 to 1998 a total of 2,538 thyroid cancer deaths were registered in 1,041 municipalities. The highest relative risks were mostly situated in the Canary Islands, the province of Lugo, the east of La Coruña (Corunna) and western areas of Asturias and Orense. CONCLUSION: The observed mortality pattern coincides with areas in Spain where goiter has been declared endemic. The higher frequency in these same areas of undifferentiated, more aggressive carcinomas could be reflected in the mortality figures. Other unknown genetic or environmental factors could also play a role in the etiology of this tumor

A Deep-Learning Model for Real-Time Red Palm Weevil Detection and Localization

Background and motivation: Over the last two decades, particularly in the Middle East, Red Palm Weevils (RPW, Rhynchophorus ferruginous) have proved to be the most destructive pest of palm trees across the globe. Problem: The RPW has caused considerable damage to various palm species. The early identification of the RPW is a challenging task for good date production since the identification will prevent palm trees from being affected by the RPW. This is one of the reasons why the use of advanced technology will help in the prevention of the spread of the RPW on palm trees. Many researchers have worked on finding an accurate technique for the identification, localization and classification of the RPW pest. This study aimed to develop a model that can use a deep-learning approach to identify and discriminate between the RPW and other insects living in palm tree habitats using a deep-learning technique. Researchers had not applied deep learning to the classification of red palm weevils previously. Methods: In this study, a region-based convolutional neural network (R-CNN) algorithm was used to detect the location of the RPW in an image by building bounding boxes around the image. A CNN algorithm was applied in order to extract the features to enclose with the bounding boxes—the selection target. In addition, these features were passed through the classification and regression layers to determine the presence of the RPW with a high degree of accuracy and to locate its coordinates. Results: As a result of the developed model, the RPW can be quickly detected with a high accuracy of 100% in infested palm trees at an early stage. In the Al-Qassim region, which has thousands of farms, the model sets the path for deploying an efficient, low-cost RPW detection and classification technology for palm trees

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range

Serum Proteomic Analysis of Cannabis Use Disorder in Male Patients

Cannabis use has been growing recently and it is legally consumed in many countries. Cannabis has a variety of phytochemicals including cannabinoids, which might impair the peripheral systems responses affecting inflammatory and immunological pathways. However, the exact signaling pathways that induce these effects need further understanding. The objective of this study is to investigate the serum proteomic profiling in patients diagnosed with cannabis use disorder (CUD) as compared with healthy control subjects. The novelty of our study is to highlight the differentially changes proteins in the serum of CUD patients. Certain proteins can be targeted in the future to attenuate the toxicological effects of cannabis. Blood samples were collected from 20 male individuals: 10 healthy controls and 10 CUD patients. An untargeted proteomic technique employing two-dimensional difference in gel electrophoresis coupled with mass spectrometry was employed in this study to assess the differentially expressed proteins. The proteomic analysis identified a total of 121 proteins that showed significant changes in protein expression between CUD patients (experimental group) and healthy individuals (control group). For instance, the serum expression of inactive tyrosine protein kinase PEAK1 and tumor necrosis factor alpha-induced protein 3 were increased in CUD group. In contrast, the serum expression of transthyretin and serotransferrin were reduced in CUD group. Among these proteins, 55 proteins were significantly upregulated and 66 proteins significantly downregulated in CUD patients as compared with healthy control group. Ingenuity pathway analysis (IPA) found that these differentially expressed proteins are linked to p38MAPK, interleukin 12 complex, nuclear factor-κB, and other signaling pathways. Our work indicates that the differentially expressed serum proteins between CUD and control groups are correlated to liver X receptor/retinoid X receptor (RXR), farnesoid X receptor/RXR activation, and acute phase response signaling

DataSheet1_Physiologically-based pharmacokinetic modeling for single and multiple dosing regimens of ceftriaxone in healthy and chronic kidney disease populations: a tool for model-informed precision dosing.PDF

Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity.Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity.Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen.Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment.</p

Auranofin Modulates Thioredoxin Reductase/Nrf2 Signaling in Peripheral Immune Cells and the CNS in a Mouse Model of Relapsing–Remitting EAE

Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory autoimmune diseases. It causes the demyelination of neurons and the subsequent degeneration of the central nervous system (CNS). The infiltration of leukocytes of both myeloid and lymphoid origins from the systemic circulation into the CNS triggers autoimmune reactions through the release of multiple mediators. These mediators include oxidants, pro-inflammatory cytokines, and chemokines which ultimately cause the characteristic plaques observed in MS. Thioredoxin reductase (TrxR) and nuclear factor erythroid 2-related factor 2 (Nrf2) signaling plays a crucial role in the regulation of inflammation by modulating the transcription of antioxidants and the suppression of inflammatory cytokines. The gold compound auranofin (AFN) is known to activate Nrf2 through the inhibition of TrxR; however, the effects of this compound have not been explored in a mouse model of relapsing–remitting MS (RRMS). Therefore, this study explored the influence of AFN on clinical features, TrxR/Nrf2 signaling [heme oxygenase 1 (HO-1), superoxide dismutase 1 (SOD-1)] and oxidative/inflammatory mediators [IL-6, IL-17A, inducible nitric oxide synthase (iNOS), myeloperoxidase (MPO), nitrotyrosine] in peripheral immune cells and the CNS of mice with the RR type of EAE. Our results showed an increase in TrxR activity and a decrease in Nrf2 signaling in SJL/J mice with RR-EAE. The treatment with AFN caused the amelioration of the clinical features of RR-EAE through the elevation of Nrf2 signaling and the subsequent upregulation of the levels of antioxidants as well as the downregulation of oxidative/pro-inflammatory mediators in peripheral immune cells and the CNS. These data suggest that AFN may be beneficial in the treatment of RRMS