Assessment of the systemic immune-inflammation index in type 2 diabetic patients with and without dry eye disease: A case-control study.

Background: The inflammation plays a role in the pathophysiology of type-2 diabetes progression, and the mechanism remains unclear. The systemic immune-inflammation index (SII) is a novel inflammatory marker for type 2 diabetes patients and integrates multiple indicators in complete blood counts and routine blood tests. Aim: Since there is no international diagnostic standard for dry eye disease (DED), this study uses low-cost inflammatory blood biomarkers to investigate the correlation between SII and DM2-DED and determine the diagnosis indices of other biomarkers in DM2-DED. Methodology: A case-control retrospective analysis of totel patients n = 293 randomly selected and categorized into four groups: DED, DM2, DM2-DED, and healthy subjects. Demographic and blood biomarker variables were classified as categorical and continuous variables. The platelet-to-lymphocyte ratio (PLR), lymphocytes-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio (NLR), and SII were calculated platelet count multiply by NLR and analyzed for their correlation for all groups. Results: Focusing on DM2-DED patients was more common in females, 59.6%, than in males, 40.2%. The mean ages were 60.7 ± 11.85 years, a statistically significant difference with all groups. In the study group DM2-DED, there was an increase in all blood markers compared to all remaining groups except PLR. Only neutrophil, hemoglobin A1c (HbA1c), and fasting blood sugar levels were statistically significant differences in DM2-DED patients (p > 0.001, p < 0.001, and p < 0.001, respectively) compared to all groups. There was a positive correlation between HbA1c and PLR, HbA1c and NLR, and HbA1c and SII (r = 0.037, p = 0.705; r = 0.031, p = 0.754; and r = 0.066, p < 0.501, respectively) in the DM2-DED group. Conclusion: This study demonstrated that elevated SII values were linked to elevated HbA1c in DM2-DED patients. The potential of SII and HbA1c as early diagnostic indicators for ocular problems associated with diabetes mellitus is highlighted by their favorable connection in diagnosing DM2-DED.

The Significance of Disease Knowledge as a Risk Factor for Depression and Anxiety in Diabetic Patients.

Background: Diabetes mellitus is a chronic disease that can lead to depression and anxiety disorders if it is not controlled and managed properly. This study aimed to estimate the prevalence of depression and anxiety disorders among diabetic patients and to determine whether patient knowledge is a risk factor. Methods: The study included 220 patients with diabetes who attended the Diabetic Care Center. Socio-demographic data on the patients was collected. The Hospital Anxiety and Depression Scale questionnaire was used to assess patients' depression and anxiety levels. The Diabetes Knowledge Test 2 was used to assess the patients' knowledge of diabetes. Results: From 220 patients with diabetes, anxiety was detected in 78.2%. From the recordings of these patients with diabetes, it was observed that 32.7%, 29.5%, and 15.9% suffered from mild, moderate, and severe anxiety, respectively. Depression was diagnosed in 170 patients (77.2%), with the majority sowing a moderate degree (32.3%) of depression. Furthermore, 29.5% and 15.5% patients had recorded mild and severe degrees of depression, respectively. Patients' understanding of diabetes was inadequate in this study, with the majority of patients (70.5%) having a low level of overall diabetes knowledge. In terms of general knowledge of diabetes, 64.1% patients had a low knowledge level, whereas 74.5% patients had a low knowledge level related to insulin therapy. Conclusion: The patients' diabetes knowledge is significantly correlated with their anxiety and depression symptoms. Patients with diabetes should be regularly screened for anxiety and depression symptoms. Our findings indicate that the educational diabetes program could be a useful intervention for reducing depression and anxiety.

Knowledge about Breast Cancer and Barriers to Screening among Saudi Women in Al-Baha Region.

OBJECTIVE: The aim of this study was to measure the knowledge about breast cancer and to identify the barriers in screening among Saudi women in the Al-Baha region. To achieve this, a cross-sectional study was conducted, involving 468 women, to assess their understanding of breast cancer and to explore the obstacles they face in accessing breast cancer screening services. METHODS: The cross-sectional study included 468 women from Al Baha, Saudi Arabia, starting from May 17, 2022, to May 17, 2023. Participants were interviewed by well-trained team members of the research, and their responses were subsequently entered into a Google Form. This process aimed to evaluate their awareness, knowledge, and barriers to breast cancer screening. RESULTS: The majority of participants (48.9%) were in the 18-28 age group. The findings reveal a high level of awareness (96.4%) among participants regarding the significance of early breast cancer detection. For the effectiveness of breast cancer treatment, 59% believed there is an effective treatment, while 32.9% were uncertain or did not know. Knowledge about various risk factors for breast cancer varied. Smoking (73.5%), genetic factors (65.6%), and a family history of breast cancer (70.7%) were well-recognized as risk factors. Education and occupation significantly influenced knowledge about breast cancer (p-value of 0.000, and 0.035 respectively). CONCLUSION: this research highlights strong awareness of breast cancer's importance but gaps in knowledge regarding lesser-known factors. Education is crucial, requiring tailored campaigns and healthcare professional engagement.

Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis.

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. OBJECTIVE: This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. METHODS: The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. RESULTS: The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. CONCLUSION: The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

Association between Incidence of Prescriptions for Alzheimer's Disease and Beta-Adrenoceptor Antagonists: A Prescription Sequence Symmetry Analysis.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia, with a growing number of patients worldwide. The association between AD and treatment with drugs targeting the beta-adrenergic receptor is controversial. The aim of this study is to assess the association between the initiation of AD medication and beta-adrenoceptor antagonists (beta-blockers) in adults. MATERIALS AND METHODS: We conducted a prescription sequence symmetry analysis using the University of Groningen IADB.nl prescription database. We determined the order of the first prescription for treating AD and the first prescription for beta-blockers, with the dispensing date of the first prescription for AD defined as the index date. Participants were adults over 45 years old starting any AD medication and beta-blockers within two years. We calculated adjusted sequence ratios with corresponding 95% confidence intervals. RESULTS: We identified 510 users of both AD and beta-blockers, and 145 participants were eligible. The results were compatible with either a significant decrease in the incidence of AD after using beta-blockers (adjusted sequence ratio (aSR) = 0.52; 95% CI: 0.35-0.72) or, conversely, an increase in beta-blockers after AD medication (aSR = 1.96; 95% CI: 1.61-2.30). CONCLUSIONS: There is a relationship between the use of beta-blockers and AD medications. Further research is needed with larger populations to determine whether drug therapy for AD increases the risk of hypertension or whether beta-blockers have potential protective properties against AD development.

Treatment of Steroid-Resistant Nodular Episcleritis With Tacrolimus: A Case Report.

A 46-year-old male, with no chronic medical illness, complained of pain, tearing, and redness for one-month duration, with no photophobia, discharge, or decrease in visual acuity. Examination revealed a small, painful, red swelling in the left sclera. Slit-lamp examination using a narrow bright slit beam revealed edema of the episcleral layer and injection of the superficial episcleral blood vessels. The rest of the anterior segment exam and fundoscopy were normal. The laboratory investigations and systemic workup were normal. The patient was initially treated with prednisolone acetate (Pred Forte) 1% every three hours per day for one week, and then four times per day for another week, and tapered gradually over eight weeks with systemic nonsteroidal anti-inflammatory drugs (NSAIDs) as diclofenac sodium for eight weeks with mild improvement of clinical symptoms, but the size of the lesion remained without any change and the patient started to have a relapse of symptoms at the end of the course. Topical tacrolimus drops of 0.1% concentration were prepared in the pharmacy under complete sterile precautions and were used four times per day for the following six weeks duration instead of the initial therapy (steroids and NSAIDs). Tacrolimus drops were then tapered gradually over another six weeks duration. The patient showed dramatic suppression of inflammation and exceptional remission of symptoms with complete resolution of the episcleritis. Topical tacrolimus is very effective in the treatment of nodular episcleritis, which is resistant to steroid therapy. Patients with nodular episcleritis suffer from prolonged bouts of inflammation that are characteristically more painful than the diffuse type and may be associated with other systemic diseases. The case is steroid-resistant nodular episcleritis, which did not respond to the usual treatment and showed a good response to treatment with tacrolimus, which was first introduced in episcleritis. Tacrolimus is being used in other ocular diseases, but its use in episcleritis is unique.

Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

A promising sensing platform for explosive markers: Zeolite-like metal-organic framework based monolithic composite as a case study.

Preconcentration for on-site detection or subsequent determination is a promising technique for selective sensing explosive markers at low concentrations. Here, we report divinylbenzene monolithic polymer in its blank form (neat-DVB) and as a composite incorporated with sodalite topology zeolite-like metal-organic frameworks (3-ZMOF@DVB), as a sensitive, selective, and cost-effective porous preconcentrator for aliphatic nitroalkanes in the vapor phase as explosive markers at infinite dilution. The developed materials were fabricated as 18 cm gas chromatography (GC) monolithic capillary columns to study their separation performance of nitroalkane mixture and the subsequent physicochemical study of adsorption using the inverse gas chromatography (IGC) technique. A strong preconcentration effect was indicated by a specific retention volume adsorption/desorption ratio equal to 3 for nitromethane on the neat-DVB monolith host-guest interaction, and a 14% higher ratio was observed using the 3-ZMOF@DVB monolithic composite despite the low percentage of 0.7 wt.% of sod-ZMOF added. Furthermore, Incorporating ZMOF resulted in a higher percentage of micropores, increasing the degree of freedom more than bringing stronger adsorption and entropic-driven interaction more than enthalpic. The specific free energy of adsorption (ΔGS) values increased for polar probes and nitroalkanes, denoting that adding ZMOFs earned the DVB monolithic matrix a more specific character. Afterward, Lewis acid-base properties were calculated, estimating the electron acceptor (KA) and electron donor (KB) constants. The neat-DVB was found to have a Lewis basic character with KB/KA = 7.71, and the 3-ZMOF@DVB had a less Lewis basic character with KB/KA = 3.82. An increased electron-accepting nature can be directly related to incorporating sod-ZMOF into the DVB monolithic matrix. This work considers the initial step in presenting a portable explosives detector or preconcentrating explosive markers trace prior to more sophisticated analysis. Additionally, the IGC technique allows for understanding the factors that led to the superior adsorption of nitroalkanes for the developed materials.

The Lifetime Expenditure in People with Keratoconus in Saudi Arabia.

AIM: This study measured and evaluated the socioeconomic burden of people living with keratoconus in Saudi Arabia. METHODS: This study employed a cross-sectional design, a Keratoconus Economic Burden Questionnaire, and a convenient sample of 89 keratoconus patients (58.4% male) drawn from multiple regions in Saudi Arabia. It was conducted using online surveys, and the data were analyzed using appropriate quantitative techniques. RESULTS: The mean age and annual income of the participants were 33.24 (SD = 6.82) years and USD 9046.52 (SD = 16,866.48), respectively, with only 37% being employed for wages. Up to 94.4% needed glasses or contact lenses at least once a week, and 73.0% received care from optometrists. The condition forced 45.9% of the respondents to change careers or leisure activities, with a further 51.3% having to take time off work. The mean annual out-of-pocket expenses for buying and maintaining glasses or contact lenses, as well as traveling and accommodation for keratoconus-related treatment were USD 2341.76 (SD = 3053.09), with 48.32 incurring upwards of USD 3240 over the period. The treatment costs increased with disease duration, r(89) = 0.216, p < 0.05. Regression results showed that the existence of comorbid eye disease, changing glasses at least once a year, and wearing either glasses or contact lenses at least once a week individually had statistically significant negative effects on the total annual keratoconus treatment costs, while disease duration, utilization of optometrists, and taking time off had a statistically significant increase on the total cost (p < 0.05). CONCLUSION: With a prevalence rate of 1 in 375, progressive debilitation, and the lifetime nature of the disease, keratoconus is a critical public health concern in Saudi Arabia. The resulting visual impairment and discomfort, as well as both direct and indirect economic burdens, have considerable impacts on the patient's quality of life.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.

Prevalence and Risk Factors of Renal Stones Among the Bisha Population, Saudi Arabia.

BACKGROUND: In urolithiasis, urinary calculi are formed in the urinary system. Stone development does not initially result in any symptoms, but later renal colic, flank pain, hematuria, obstruction of urine flow, and/or hydronephrosis may indicate renal stone disease. In addition to age, gender, ethnicity, and local climate, urolithiasis can be caused by several other factors. The prevalence and recurrence rate of kidney stone disease is rising globally, while few effective treatment options currently exist. METHODS: Between June and October 2022, a cross-sectional study was conducted. An electronic questionnaire subdivided into three categories was used to determine the prevalence and identify the factors that increase the likelihood of developing urolithiasis among the population in Bisha. The collected data were reviewed and analyzed via IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. RESULTS: A total of 1,002 participants filled out the questionnaire. The age of the participants ranged from 18 to over 60 years, with an average age of 26.1 ± 13.9 years. There were 451 female participants (45%), and 927 (92.5%) were Saudis. According to the participants' body mass index, 98 (9.8%) were underweight, 388 (38.7%) were normal weight, 300 (29.9%) were overweight, and 216 (21.6%) were obese. The total number of participants with urolithiasis was 161 (16.1%), and 420 (41.9%) had a family history of renal stones. Urolithiasis was found to be significantly associated with family history, smoking, diabetes, hypertension, hyperthyroidism, gout, and chronic kidney disease. Older age and female gender were also associated with the risk of having urolithiasis. CONCLUSION: This study found urolithiasis to be highly prevalent among the Bisha population. In terms of risk factors, body mass index, smoking, and diabetes were the most significant. Based on the findings of this study, the authors recommend more public education regarding urolithiasis and its risk factors, emphasizing the importance of preventing the disease and the ways of treating urolithiasis through medical campaigns and social media.

Prevalence and Determinants Influencing the Use of Electronic Cigarette Smoking in Male Students of Taif University.

BACKGROUND AND AIM: Electronic cigarette (e-cigarette) smoking is a significant public health problem in Saudi Arabia, particularly among youth who use it as an alternative to traditional cigarettes. This study aimed to evaluate the prevalence and beliefs towards e-cigarette use among Taif University students in Saudi Arabia. METHODS: A cross-sectional study was conducted among male Taif University students using a self-administered questionnaire. A sample of 319 students was selected through stratified sampling. The questionnaire included questions about socio-demographic characteristics, smoking history, awareness of e-cigarettes, prevalence of e-cigarette use, beliefs towards e-cigarettes, and reasons for e-cigarette use. RESULTS: The study revealed a high prevalence of e-cigarette use among Taif University students, with 40.1% of participants having used e-cigarettes at least once during their lifetime and 43.7% believing that e-cigarettes are less dangerous than traditional cigarettes. Participants studying sciences had 0.76 times the odds of believing that e-cigarettes help smokers to quit compared to participants studying literature. Compared to smokers, ex-smokers had an OR of 34.1 (p<0.001) and non-smokers had an OR of 35.9 (p<0.001) for experimentation of e-cigarettes. Smokers who had friends that tried e-cigarettes had an OR of 6.6 (p<0.001) for trying e-cigarettes, compared to smokers who did not have such friends. CONCLUSION: The study found that 40.1% of participants have used e-cigarettes at least once during their lifetime with a significant proportion of participants unaware of the potential health hazards of e-cigarettes, and many believed that e-cigarettes are less dangerous than traditional cigarettes. These findings emphasize the need for targeted educational interventions to address misconceptions and promote awareness among university students.

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia.

The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018-2021 was collected, covering about 71,000 km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.

Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans.

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.

Knowledge and Awareness About Gastric Cancer Among the General Population in Al-Baha City, Saudi Arabia.

Background Gastric cancer is a significant health concern worldwide, and its incidence varies across different populations. This study aimed to assess the level of knowledge and awareness of gastric cancer among the general population in Al-Baha City, Saudi Arabia.  Methodology This is a cross-sectional study that was conducted among the residents of Al-Baha city older than 18 years. The study was conducted based on a questionnaire that has been developed by a previous study. Data were initially recorded in an Excel sheet before being exported to the SPSS program, version 25 for data analysis. Results The survey included 426 respondents from Al-Baha city, Saudi Arabia, with 56.8% being females and the majority being in the age groups (21-30 years). Alcohol consumption (mean=4.5, SD= 0.77), smoking cigarettes or Shisha (mean= 4.38, SD=0.852), family history of gastric cancer (mean= 4, SD=1.008), a past medical history of gastric cancer (mean= 3.99, SD=0.911), stomach ulcer (mean=3.76, SD=0.898), and consumption of smoked food (mean= 3.69, SD=0.956) are the most widely recognized risk factors. The most highly recognized symptoms are gastrointestinal bleeding (mean= 4.03, SD=0.875), abdominal lump (mean= 3.94, SD=0.926), weight loss (mean= 3.93, SD=0.963), recurrent nausea and vomiting (mean=3.76, SD=0.956), and abdominal pain (mean= 3.57, SD=0.995). The study also identified several subgroups of the population that may benefit from targeted educational programs, including individuals in the age group of 41-50 years and those in non-medical occupations. Conclusion The study found that participants had a moderate level of knowledge about the risk factors and symptoms of gastric cancer, with significant variability among different subgroups of the population. Further research is needed to investigate the prevalence and risk factors of gastric cancer in Saudi Arabia and other similar populations, to develop effective prevention and management strategies for this disease.

The use of medicinal plants in common ophthalmic disorders: A systematic review with meta-analysis.

Purpose: This study aimed to assess and compile the available research articles about medicinal plants used for ocular diseases. Principal results: A total of 2949 articles were retrieved, 35 full-text articles were assessed for eligibility, and seven studies (4 observational and three experimental) with low to moderate quality were eligible and involved in the systematic review, with a total of 600 plants from 4 countries. Among the 600 plants, only 24 (4%) were used to assess the status. Both the fixed and random models of the studies showed that the included studies tended to predict the results for the observational studies (OR = 0.062, CI = 0.043-0.090 OR = 0.039, CI = 0.012-0.122) for different plants used for ocular diseases. High heterogeneity (estimated as I2 = 87.078, Tau2 = 1.161 and Q-value = 23.217 with a p-value of 0.000), while for experimental studies (I2 = 94.928, Tau2 = 23.211 and Q-value = 39.434 with a p-value of 0.000) and publication bias were reported. Conclusion: Few articles representing approximately 600 plants of low to moderate quality reported using medicinal plants for ocular diseases. The meta-analysis confirmed the systematic review findings regarding the plants' traditional use with high heterogeneity and publication bias. A considerable gap was proven in the use of medicinal plants in ocular diseases requiring intensive research.

Climate of Online e-Learning During COVID-19 Pandemic in a Saudi Medical School: Students' Perspective.

INTRODUCTION: The circumstances of the COVID-19 lockdown offered an opportunity to develop remote educational strategies in medical education. OBJECTIVES: To assess medical students' experiences with online e-learning (OeL) satisfaction, intellectual environment, and communication during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted at the College of Medicine, University of Bisha, Saudi Arabia. A self-administered questionnaire (21 items) was used to evaluate OeL in three domains of satisfaction (nine items), intellectual environment (seven items), and communication (five items). Students from years one to six were invited to fill out the questionnaire form with five-point Likert scale responses. Descriptive statistics, one-way analysis of variance (ANOVA), and independent t-test were used to evaluate the association between variables. RESULTS: Out of 237 participants, 96.6% (158 male and 71 female) responded to the questionnaire. Most students (86.5%) preferred the blackboard for their e-learning. The mean total scores were 30.18 ± 6.9 out of 45 for satisfaction, 19.67 ± 5.4 out of 25 for communication 25.43 ± 5.1 out of 35 for the intellectual environment. Over 50% of the students rated moderate scores on satisfaction and intellectual environment domains. About 85% of the students rated moderate scores in the communication domain. Male students rated higher significant scores than female students for satisfaction (31.3 ± 6.3 vs 27.6 ± 7; P < .001) and intellectual environment (26.3 ± 4.32 vs 3.5 ± 6.1; P < .001). There were no significant differences in students' responses to the tested domains related to GPA level. Significantly higher levels of scores for satisfaction (33.3 ± 5.6 vs 28.8 ± 6.9; P < .001) and communication (21.2 ± 4.5 vs 18.9 ± 5.7; P = .019) were obtained by clerkship students than pre-clerkship. CONCLUSIONS: Medical students' experiences with e-learning are encouraging, which might be more effective with continuous training programs for students and tutors. Although OeL is an acceptable methodology, further studies are needed to analyze its impact on the target learning outcomes and students' academic achievement.

Study of Streptococcus mutans in Early Biofilms at the Surfaces of Various Dental Composite Resins.

BACKGROUND: Biofilm deposit on the composite restoration is a common phenomenon and bacterial growth follows the deposition. The study aims to evaluate Streptococcus mutans (S. mutans) early biofilm formation on the surfaces of various dental composite resins by using the real-time quantitative polymerase chain reaction (qPCR) technique. MATERIALS AND METHODS: Thirty-two discs, where eight discs were in each group of Filtek Supreme Ultra (FSU; 3M, St. Paul, MN), Clearfil AP-X (APX; Kuraray Noritake Dental Inc., Tokyo, Japan), Beautifil II (BE2; Shofu, Inc., Kyoto, Japan), and Estelite Sigma Quick (ESQ; Tokuyama Dental, Tokyo, Japan), were fabricated and subjected to S. mutans biofilm formation in an oral biofilm reactor for 12 hours. Contact angles (CA) were measured on the freshly fabricated specimen. The attached biofilms underwent fluorescent microscopy (FM). S. mutans from biofilms were analyzed using a qPCR technique. Surface roughness (Sa) measurements were taken before and after biofilm formation. Scanning electron microscopy (SEM), including energy dispersive X-ray spectrometer (EDS) analysis, was also performed for detecting relative elements on biofilms. RESULTS: The study showed that FSU demonstrated the lowest CA while APX presented the highest values. FM revealed that condensed biofilm clusters were most on FSU. The qPCR results indicated the highest S. mutans DNA copies in the biofilm were on FSU while BE2 was the lowest (p < 0.05). Sa test signified that APX was significantly the lowest among all materials while FSU was the highest (p < 0.05). SEM displayed areas with apparently glucan-free S. mutans more on BE2 compared to APX and ESQ, while FSU had the least. Small white particles detected predominantly on the biofilms of BE2 appeared to be Si, Al, and F extruded from the resin. CONCLUSION: Differences in early biofilm formation onto various composite resins are dependent on the differences in material compositions and their surface properties. BE2 showed the lowest quantity of biofilm accumulation compared to other resin composites (APX, ESQ, and FSU). This could be attributed to BE2 proprieties as a giomer and fluoride content.