Study of lipid profiles high and normal body mass index in polycystic ovary syndrome women in Aljouf, Saudi Arabia

Background: For women of reproductive age, polycystic ovary syndrome (PCOS) is the most prevalent endocrinological condition. Hyperandrogenism, persistent ovulatory dysfunction, obesity, and insulin resistance have all been associated with PCOS. However, recently PCOS is detected in high-body weight and normal-body-weight women. No information was available to evaluate specific lipid profiles. The goal of this study is to analyze the lipid profiles of women with polycystic ovarian syndrome who have high or normal body weight. Methods: This polycystic ovarian syndrome (PCOS) retrospective study was carried out between January 2021 and January 2022 at Sakaka’s Maternity and Children Hospital (MCH), Aljouf, Saudi Arabia. A total of 68 PCOS women were included in the study; we divided them into high (n=34) and normal body weight (n=34) according to the calculation of BMI. We obtain the test results of lipid profiles and demographic data from hospital record files. Results: We noticed changes but no significance in our research of lipid profiles in high and normal PCOS participants. The CHOL, TG, HDL, and LDL, 159.30±4.193, 97.89±7.140, 60.91±9.564, and 99.47±9.22 shown in the high BMI PCOS women respectively. While, 129.28±3.702, 83.69±4.49, 46.84±1.68 and86.53±4.36 were detected in normal BMI PCOS women respectively. There were none that were statistically significant, with the exception of cholesterol p=0.001. Conclusions: Our results show that POCS women with normal body weight and PCOS women with high BMI have different changes in their lipid profiles, but no significance has been found other than higher cholesterol levels. Therefore, losing weight can stop lipid profiles from altering, which may reduce difficulties in the future

Causes of elective cesarean delivery on maternal request in Aljouf, Saudi Arabia

Background: Recently observed there is a steadily higher rate of cesarean delivery worldwide mostly due to the increasing number of women requesting an elective cesarean section on maternal request without valid indication. The aim of the study was to determine the causes of elective cesarean delivery on maternal requests in Aljouf Saudi Arabia.Methods: This was a descriptive cross-sectional study and data was evaluated by completing seven questionnaires and interviews with laboratory reports who were admitted for cesarean delivery at the Obstetrics department of Maternity and Children Hospital (MCH) Aljouf, Saudi Arabia from January 2020 to December 2020. A total of 141 Saudi women of age between 18 and over 35 years were enrolled, including those who have singleton pregnancy, no complications during pregnancy, and no medical indication for cesarean delivery.Results: 141 women reported willingness to request cesarean delivery. The mean systolic 120±6.23, diastolic 75±2.45 blood pressure mm of Hg, and fasting blood sugar level 4.1±1.1 mmol/l have been found within the normal limit. The ultrasound (US) confirmed singleton pregnancy without any abnormalities.  Data reveals that common causes of elective cesarean section on maternal request to avoid the episiotomy 77.3%, fear of labor pain 69.5%, trauma to the vagina 79.4%, uncertainty about timing 61.7%, losing a baby during vaginal delivery 54.6%, experience other members 41.8%, the risk for baby 39%, prolapse or incontinence24.1%, unsatisfactory sexual intercourse 17.7% and the undesirable experience of the previous vaginal delivery 12%.Conclusions: Maternal request for cesarean delivery is considered one of the reasons for increasing the rate of cesarean delivery in Saudi Arabia. To avoid the episiotomy and fear of labor pain may strong causes for choosing cesarean delivery

An overview of the public health challenges in diagnosing and controlling human foodborne pathogens

Pathogens found in food are believed to be the leading cause of foodborne illnesses; and they are considered a serious problem with global ramifications. During the last few decades, a lot of attention has been paid to determining the microorganisms that cause foodborne illnesses and developing new methods to identify them. Foodborne pathogen identification technologies have evolved rapidly over the last few decades, with the newer technologies focusing on immunoassays, genome-wide approaches, biosensors, and mass spectrometry as the primary methods of identification. Bacteriophages (phages), probiotics and prebiotics were known to have the ability to combat bacterial diseases since the turn of the 20th century. A primary focus of phage use was the development of medical therapies; however, its use quickly expanded to other applications in biotechnology and industry. A similar argument can be made with regards to the food safety industry, as diseases directly endanger the health of customers. Recently, a lot of attention has been paid to bacteriophages, probiotics and prebiotics most likely due to the exhaustion of traditional antibiotics. Reviewing a variety of current quick identification techniques is the purpose of this study. Using these techniques, we are able to quickly identify foodborne pathogenic bacteria, which forms the basis for future research advances. A review of recent studies on the use of phages, probiotics and prebiotics as a means of combating significant foodborne diseases is also presented. Furthermore, we discussed the advantages of using phages as well as the challenges they face, especially given their prevalent application in food safety

Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models

BackgroundStroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests. ObjectiveThe aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data. MethodsWe used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models. ResultsWe found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used. ConclusionsThe predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type

Awareness Level of Huntington Disease: Comprehensive Analysis of Tweets During Huntington Disease Awareness Month

Background: Unawareness of Huntington disease is prevalent where patients might have a denial of illness, less reporting of symptoms such as changes in behavior or cognitive impairment, or poor coping with the disease. Understanding the awareness level of Huntington disease is crucial to provide more suggestions for public health campaigns. Objective: This study explores the level of awareness of Huntington's disease among users of social media. We will also explore the tweeting behavior during Huntington disease awareness month, and search any missing area related to the awareness by following the framework of Social Media-Based Public Health Campaigns. Method: We extracted tweets from April 2021-Jun 2021. We used both quantitative and qualitative methods to analyze the data. We used Python programming and various natural language processing tools to process and analyze data for a quantitative investigation. We also carried out a qualitative content analysis to identify themes and subthemes in the data. Result: We discovered that the most popular hashtag is #LetsTalkAboutHD, and after looking over the data, it seemed to us that the word ''support'' was used more than 54 times during that time. According to the findings of our analysis of the twitter distribution pattern in terms of time, the most tweets were sent between May 13 and May 16, particularly on Wednesday, which was the busiest day. Also, the United States and Alaska had the highest levels of engagement when the pattern of tweets based on geographic location was examined. The most common pattern in the tweets that we separated based on patterns was news, which was followed by research and clinical trials. Conclusion: Awareness campaigns needs to follow the framework of social media-Based Public Health Campaigns to provide more comprehensive information about Huntington disease and increase the awareness level among patients and families

Revealing the dynamical behavior of stars in a prolate or oblate elliptical galaxy

We consider the motion of a test particle (star) on the meridian plane (R,z) of an elliptical galaxy with an additional central nucleus component. The shape of the elliptical galaxy can be either prolate or oblate, or even spherical by simply changing the numerical value of the flattening parameter entering the equation of the potential of the elliptical galaxy. By means of numerical integration of both the motion equations and the equations of variations of the system, we conduct a systematic orbit classification of the starting conditions of the test particle, thus revealing how the flattening parameter affects its orbital dynamics. Our simulations suggest that in the scenario where the elliptical galaxy has a prolate shape the motion of a star is much more complicated with respect to the case where we have a usual oblate elliptical galaxy. Additionally, we also investigate how the total orbital energy of the test particle affects its dynamics in relation to the flattening parameter of the elliptical galaxy

Revealing the dynamical behavior of stars in a prolate or oblate elliptical galaxy

We consider the motion of a test particle (star) on the meridian plane of an elliptical galaxy with an additional central nucleus component. The shape of the elliptical galaxy can be either prolate or oblate, or even spherical by simply changing the numerical value of the flattening parameter entering the equation of the potential of the elliptical galaxy. By means of numerical integration of both the motion equations and the equations of variations of the system, we conduct a systematic orbit classification of the starting conditions of the test particle, thus revealing how the flattening parameter affects its orbital dynamics. Our simulations suggest that in the scenario where the elliptical galaxy has a prolate shape the motion of a star is much more complicated with respect to the case where we have a usual oblate elliptical galaxy. Additionally, we also investigate how the total orbital energy of the test particle affects its dynamics in relation to the flattening parameter of the elliptical galaxy

Orbital dynamics in the Hill problem with oblateness

In this paper, we investigate the impact of the oblateness of the secondary body on the motion of a test particle in the near vicinity of the secondary body, specifically in the context of the extended version of the planar Hill problem. To achieve this objective, we conduct a comprehensive survey by systematically classifying the initial conditions of trajectories and scanning the phase space using two-dimensional (2D) maps on various planes. We then numerically integrate the starting conditions on these maps and classify the final states of the test particles as either bounded or unbounded. Bounded orbits are further subclassified as collision orbits and regular (or chaotic), while unbounded orbits are sub-classified according to the sector of the (x,y) plane through which the particle escapes the potential well. Our results reveal that increasing values of the oblateness coefficient reduce the number of escaping orbits and limit the extension of islands of regular motion (in the Liouville–Arnold sense). Regular bounded motion occurs only at larger distances from the secondary body

Does a mobile phone depression-screening app motivate mobile phone users with high depressive symptoms to seek a health care professional's help?

Background: The objective of disease screening is to encourage high-risk subjects to seek health care diagnosis and treatment. Mobile phone apps can effectively screen mental health conditions, including depression. However, it is not known how effective such screening methods are in motivating users to discuss the obtained results of such apps with health care professionals. Does a mobile phone depression-screening app motivate users with high depressive symptoms to seek health care professional advice? This study aimed to address this question. Method: This was a single-cohort, prospective, observational study of a free mobile phone depression app developed in English and released on Apple's App Store. Apple App Store users (aged 18 or above) in 5 countries, that is, Australia, Canada, New Zealand (NZ), the United Kingdom (UK), and the United States (US), were recruited directly via the app's download page. The participants then completed the Patient Health Questionnaire (PHQ-9), and their depression screening score was displayed to them. If their score was 11 or above and they had never been diagnosed with depression before, they were advised to take their results to their health care professional. They were to follow up after 1 month. Results: A group of 2538 participants from the 5 countries completed PHQ-9 depression screening with the app. Of them, 322 participants were found to have high depressive symptoms and had never been diagnosed with depression, and received advice to discuss their results with health care professionals. About 74% of those completed the follow-up; approximately 38% of these self-reported consulting their health care professionals about their depression score. Only positive attitude toward depression as a real disease was associated with increased follow-up response rate (odds ratio (OR) 3.2, CI 1.38-8.29). Conclusions: A mobile phone depression-screening app motivated some users to seek a depression diagnosis. However, further study should investigate how other app users use the screening results provided by such apps.10 page(s

Cross-Regulation between Autophagy and Apoptosis Induced by Vitamin E and Lactobacillus Plantarum through Beclin-1 Network

Autophagy and apoptosis are two important regulatory mechanisms for how the body can respond to diseases. This study was designed to investigate the protective actions of vitamin E (Vit-E) and lactobacillus plantarum (Lac-B) against mercuric chloride (HgCl2)-induced kidney injury. Thirty albino rats were divided into five groups: group 1 served as the normal group; rats in group 2 received high doses of HgCl2; rats in groups 3, 4 and 5 were given Vit-E, Lac-B and the combination of Vit-E and Lac-B, respectively along with HgCl2 for two weeks. HgCl2 provoked renal injury, manifested by elevation in serum urea, urea nitrogen and creatinine. Kidney levels of oxidative stress and inflammation were markedly increased post HgCl2 administration. Moreover, HgCl2 significantly elevated the gene expression levels of VCAM-1 and cystatin C, while podocin was downregulated. Additionally, it markedly decreased the protein expression of Beclin-1 and Bcl-2. Histopathological examination revealed massive degeneration with congested blood vessels following HgCl2 administration. Treatment with Vit-E or/and Lac-B restored the normal levels of the previously mentioned parameters, as well as improved the morphology of kidney tissues. Both Vit-E and Lac-B provided a protective effect against HgCl2-induced kidney damage by regulating autophagy and apoptosis