The Association Between Hypertension and Insomnia Among Saudi Population: A Cross-Sectional Study

Objective To assess the relationship between hypertension and insomnia among the Saudi population. Methods The study will employ a cross-sectional design to investigate the association between hypertension and insomnia among the Saudi population. This design allows for the collection of data at a single point in time, offering insights into the relationship between the variables. Results The study included 581 participants. The most frequent age among them was 18-28 (n= 266, 45.8 Per Cent), followed by 29-39 (n= 129, 22.2 Per Cent). The most frequent gender among study participants was female (n= 320, 55.1 Per Cent), followed by male (n= 261, 44.9 Per Cent). Study participants' most frequent body mass index was normal 18.5-24.9 kg/m2 (n= 231, 39.8 Per Cent) followed by overweight 25-29.9 kg/m2 (n= 200, 34.4 Per Cent). Marital status among study participants, with most of them being single (n=283, 48.7 Per Cent) followed by married (n= 238, 41 Per Cent). The number of hours of sleep during the day among study participants with most of them had 6-8 hours. Participants were asked if they had difficulty sleeping. There most of the participants were nothing (n= 201, 34.6 Per Cent). On the other hand, 154 participants had middle (26.5 Per Cent). They asked if they had difficulty staying asleep. There most of the participants were nothing (n= 227, 39.1 Per Cent). On the other hand, 152 participants had middle (26.2 Per Cent). Participants were asked if they had trouble waking up early. There most of the participants were nothing (n= 189, 32.5 Per Cent). Followed by middle (n=148, 25.5 Per Cent). Conclusion The results of the study showed that most of the study participants were of normal weight according to their body mass index. Most of them are single. Most participants sleep approximately 6-8 hours a day. The largest percentage of participants work in the government or private sector. Most of them had good social contact

Neurofibroma of the external genitalia, extreme enlargement of the clitoris

Neurofibromatosis of the genitourinary tract is rare, with a prevalence of 0.65%, and it is exceedingly rare to involve the external genitalia. Involvement of the clitoris, labia majora, and prepuce was reported with clitoromegaly being the most frequently occurring. Herein, we are reporting the case of a 6-year-old girl who was diagnosed with a neurofibroma of the clitoris; measuring 9.4 cm in its largest dimension. To the best of our knowledge, this is the largest clitoral neurofibroma reported in the literature. Due to the rarity of such cases and reports limitations in the literature, the diagnosis of neurofibroma of the external genitalia requires a high index of suspicion by health-care providers. Surgical excision and postoperative follow-up for possible recurrence remain the gold standard of management

Predicting Colorectal Cancer Using Machine and Deep Learning Algorithms: Challenges and Opportunities

One of the three most serious and deadly cancers in the world is colorectal cancer. The most crucial stage, like with any cancer, is early diagnosis. In the medical industry, artificial intelligence (AI) has recently made tremendous strides and showing promise for clinical applications. Machine learning (ML) and deep learning (DL) applications have recently gained popularity in the analysis of medical texts and images due to the benefits and achievements they have made in the early diagnosis of cancerous tissues and organs. In this paper, we intend to systematically review the state-of-the-art research on AI-based ML and DL techniques applied to the modeling of colorectal cancer. All research papers in the field of colorectal cancer are collected based on ML and DL techniques, and they are then classified into three categories: the aim of the prediction, the method of the prediction, and data samples. Following that, a thorough summary and a list of the studies gathered under each topic are provided. We conclude our study with a critical discussion of the challenges and opportunities in colorectal cancer prediction using ML and DL techniques by concentrating on the technical and medical points of view. Finally, we believe that our study will be helpful to scientists who are considering employing ML and DL methods to diagnose colorectal cancer

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population