ABSTRACT
Background Patients with subclinical hypothyroidism (SCH) have a high serum concentration of thyroid-stimulating hormone (TSH), whereas their serum-free thyroxine concentrations are normal. Lipid metabolism is regulated in large part by thyroid hormones. It could be connected to a changed lipid profile. This study aimed to evaluate the relationship between SCH and alterations in the lipid profile. Methodology Data from 99 patients with SCH and 109 euthyroid cases were collected from King Abdulaziz Medical City, Jeddah, Saudi Arabia, from 2016 to 2022. Patients older than 18 years were included in the study. The groups were matched in terms of gender, age, and body mass index. SCH was defined as a TSH value of 4.5 to 10 mIU/L, and normal T4 as 5 to 18 µg/dL. Control cases had a normal TSH ranging from 0.45 to 4.5 mIU/L. The total serum cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) levels in both groups were examined and the results were recorded. Results In comparison to the control group, SCH patients had greater median glycated hemoglobin (HbA1C) (p = 0.001) and lower median vitamin D levels (p = 0.004) before therapy. Before therapy, SCH patients also showed considerably lower HDL levels and significantly higher LDL and TG levels (p < 0.001). Conclusions There is a substantial correlation between SCH and reduced HDL and vitamin D levels. It was linked to increased TG, LDL, and HbA1c levels. Only vitamin D and LDL were pathologically high. Treatment with levothyroxine raised total and LDL cholesterol levels. Future research should look into the affordability of treating SCH.
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Introduction One of the world's most pressing problems right now is childhood obesity. After potassium, magnesium (Mg) is the second most prevalent intracellular cation and the fourth most prevalent mineral in the human body. Numerous symptoms of magnesium insufficiency can include hypocalcemia, hypokalemia, as well as cardiac and neurological symptoms. Additionally, chronically low Mg levels have been associated with a number of chronic diseases such as diabetes, hypertension, coronary heart disease, and osteoporosis. Objectives This study aimed to compare the magnesium (Mg) level between normal-weight and obese children in a tertiary center in Saudi Arabia over the past seven years and evaluate the vitamin D and phosphorus between the two groups as a secondary objective. Methods This is a single-center, case-control study conducted on patients followed up in our center from January 2016 to December 2022. All pediatric patients were between two and 14 years of age. They were divided into two groups: one with children whose body mass index (BMI) was over the 85th percentile and the other with children whose BMI was between the 3rd and 85th percentiles. Results Mean serum Mg levels showed no significant correlation between the obese group (0.82 mg/dl) and the normal-weight group (0.83 mg/dl). However, vitamin D and phosphorus demonstrate a significant difference between the two groups. The obese group revealed a vitamin D of 1.6±0.24 and phosphorus of 4.2±0.46. On the other hand, the normal group had a vitamin D of 44.0±28.2 and phosphorus of 1.5±0.26. Conclusion There was a negative correlation between Mg levels and weight in pediatric patients. However, a positive relationship was observed between the Mg intake and Mg levels. Moreover, sodium, phosphorus, and vitamin D levels showed significant differences.
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BACKGROUND: Annual influenza vaccine is recommended among health professionals especially, health science undergraduates; although, studies suggest that there is a low level of vaccine coverage among health care undergraduates. Thus, the study aimed to investigate students' knowledge, attitudes, and practices regarding the seasonal influenza vaccine at King Saud bin Abdulaziz University for Health Sciences (KSAU-HS) in Jeddah, Saudi Arabia. METHODS: A cross-sectional, unicentral study was carried out during the period of April to November 2021. A total of 341 validated questionnaires that included four sections were distributed in all four different colleges of KSAU-HS. Data management and analyses were carried out using Statistical Software for Social Sciences (SPSS) version 25. RESULTS: Out of 341 respondents, 336 participants completed the full survey (98.53% completion rate). The majority of participants were males (67.6% and the most participants were from the college of medicine (60.4%). The mean knowledge score of students was found to be (11.3 ± 2.0) out of 14 points. The most common barrier to vaccination among healthcare students was not having enough time to take the vaccine (23.8%), while the second one was the COVID-19 pandemic (22.32%). The vaccination rate in 2020 was 103 (30.7%), while 235 (80.4%) students stated that they have received the vaccine previously. School/work requirements and the student's awareness of the vaccine's importance were the most common reasons for taking the influenza vaccine. CONCLUSION: Even though students showed a high level of knowledge and a positive attitude towards the influenza vaccine, they generally had a low level of vaccine uptake. To enhance immunization rates, we encourage vaccine campaigns in all healthcare colleges. Further studies are needed to identify influences on students' attitudes and practices regarding influenza vaccination.
ABSTRACT
Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field.