Acrodermatitis dysmetabolica secondary to isoleucine deficiency in infant with maple syrup urine disease

Acrodermatitis dysmetabolica (AD) describes eruptions characterized by the clinical triad of acral dermatitis, diarrhea, and alopecia. AD can be caused by various metabolic disorders one of which is maple syrup urine disease (MSUD). We present a 2-month-old boy diagnosed with MSUD by the age of 5 days and treated with branched-chain amino acid (BCAA) restricted diet, BCAAs formula, and thiamine supplementation. He was referred to dermatology with a 3-week history of diarrhea, progressive acrodermatitis enteropathica like cutaneous eruption and hair loss over the scalp treated with topical mometasone ointment, isoleucine supplementation and leucine restriction. Complete resolution of skin eruption was achieved by 4 weeks, which correlates with normalization of BCAA levels based on close monitoring of biochemical lab values and growth. This case emphasizes the dangers of limiting BCAA intake when treating MSUD, as well as the importance of close monitoring during the amino acid depleting period of growth

Arabic translation, cultural adaptation, and pre-testing of neighborhood environment walkability scale for adults-abbreviated (NEWS-A): Arabic NEWS-A

BackgroundImproving neighborhood walkability is critical for sustainable and livable urban development and is associated with increased physical activity. The Neighborhood Environment Walkability Scale-Abbreviated (NEWS-A) is a tool for assessing neighborhood walkability among adults. Currently, no Arabic version is available for this important walkability scale.ObjectiveTo translate the NEWS-A to Arabic, culturally adapt, and pre-test it for adults.MethodsThe NEWS-A was translated and culturally adapted using Cross-cultural Survey Guidelines and then pre-tested using 65 households selected randomly from the neighborhoods of three districts in Riyadh. After answering the survey, 55 participants took part in a semi-structured cognitive interview, which sought their understanding of the words in the Arabic questionnaire, the clarity of each item, and their suggestions for improvement.ResultsTranslation and adaptation of NEWS-A to Arabic resulted in adding two items (mosque and healthcare center) to section B of the scale (stores, facilities, and other things in your neighborhood) and one related item to section C (access to services). The total individual items became 57 instead of the original 54 items in the English version. Also, we added “ATM machine” to item 14 (Bank/credit union) in section B. In addition, the overall results of the cognitive interview showed that most of the participants understood the intended meaning of the questionnaire (99.5%). Further, 99.8% of the respondents stated that the items were not difficult to comprehend, while 100% of the sample indicated that the sentences were comfortable and not sensitive to them.ConclusionTranslating, adapting, and pre-testing the NEWS-A resulted in retaining all the original items and adding three additional items. The Arabic NEWS-A provides an important tool for future research on neighborhood environment walkability among adults

Exploring the Efficacy of Deep Learning Techniques in Detecting and Diagnosing Alzheimer’s Disease: A Comparative Study

Transfer learning has become extremely popular in recent years for tackling issues from various sectors, including the analysis of medical images. Medical image analysis has transformed medical care in recent years, enabling physicians to identify diseases early and accelerate patient recovery. Alzheimer’s disease (AD) diagnosis has been greatly aided by imaging. AD is a degenerative neurological condition that slowly deprives patients of their memory and cognitive abilities. Computed tomography (CT) and brain magnetic resonance imaging (MRI) scans are used to detect dementia in AD patients. This research primarily aims to classify AD patients into multiple classes using ResNet50, VGG16, and DenseNet121 as transfer learning along with convolutional neural networks on a large dataset as compared to existing approaches as it improves classification accuracy. The methods employed utilize CT and brain MRI scans for AD patient classification, considering various stages of AD. The study demonstrates promising results in predicting AD phases with MRI, yet challenges persist, including processing large datasets and cognitive workload involved in interpreting scans. Addressing image quality variations is crucial, necessitating advancements in imaging technology and analysis techniques. The different stages of AD are early mental retardation, mild mental impairment, late mild cognitive impairment, and final AD stage. The novel approach gives results with an accuracy of 96.6% and significantly improved outcomes compared to existing models

Arabic Translation, Cultural Adaptation, and Pretesting of Neighborhood Environment Walkability Scale for Adults-Abbreviated (NEWS-A): Arabic NEWS-A

Background: Improving neighborhood walkability is critical for sustainable and livable urban development and is associated with increased physical activity. The Neighborhood Environment Walkability Scale-Abbreviated (NEWS-A) is a tool for assessing neighborhood walkability among adults. Currently, no Arabic version is available for this important walkability scale. Objective: To translate the NEWS-A to Arabic, culturally adapt, and pre-test it for adults. Methods: The NEWS-A was translated and culturally adapted using Crosscultural Survey Guidelines and then pre-tested using 65 households selected randomly from the neighborhoods of three districts in Riyadh. After answering the survey, 55 participants took part in a semi-structured cognitive interview, which sought their understanding of the words in the Arabic questionnaire, the clarity of each item, and their suggestions for improvement. Results: Translation and adaptation of NEWS-A to Arabic resulted in adding two items (mosque and healthcare center) to section B of the scale (stores, facilities, and other things in your neighborhood) and one related item to section C (access to services). The total individual items became 57 instead of the original 54 items in the English version. Also, we added “ATM machine” to item 14 (Bank/credit union) in section B. In addition, the overall results of the cognitive interview showed that most of the participants understood the intended meaning of the questionnaire (99.5%). Further, 99.8% of the respondents stated that the items were not difficult to comprehend, while 100% of the sample indicated that the sentences were comfortable and not sensitive to them. Conclusion: Translating, adapting, and pre-testing the NEWS-A resulted in retaining all the original items and adding three additional items. The Arabic NEWS-A provides an important tool for future research on neighborhood environment walkability among adults

Exploring the Potential of Convolutional Neural Networks in Classifying Alzheimer’s Stages with Multi-biomarker Approach

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this study, we utilized a combination of biomarkers, including magnetic resonance imaging (MRI), electronic health records, and cerebrospinal fluid (CSF), to classify subjects into three groups based on clinical tests—normal cognitive controls (CN), mild cognitive impairment (MCI), and AD. To determine the significant parameters, we employ a novel technique that utilizes sparse autoencoders to extract features from CSF, clinical data, and convolutional neural networks’ (CNN’s) MRI imaging data. Our results indicate that deep learning methods outperform traditional machine learning models such as decision trees, support vector machines, random forests and K-nearest neighbors. The proposed method significantly outperforms traditional models, achieving an accuracy of 0.87 for CN versus AD, a precision of 0.93 for CN, and a recall of 0.88 for AD on the external test set. The integration of various data modalities and the application of deep learning techniques enhance the prediction accuracy, demonstrating the potential for improved diagnostic tools in clinical settings

Health and economic burden of insufficient physical activity in Saudi Arabia

BackgroundInsufficient physical activity (PA) was estimated to cause 4.8% of deaths and 2.6% of disability- adjusted life-years (DALYs) due to noncommunicable diseases in Saudi Arabia in 2019. While Saudi Arabia is already achieving great improvements, we predict the health and economic burden of insufficient PA up to 2040 to present a case for policy makers to invest more in the uptake of PA. MethodsUsing a population health model to estimate avoidable health loss, we identified four causes of health loss related to low PA (cardiovascular diseases, diabetes, breast cancer, and colorectal cancer) and estimated the deaths and DALYs from these causes. We projected the expected disease burden until 2040 under alternative assumptions about future PA levels and trends by using three health scenarios: baseline (no change in 2019 PA levels), intervention (81% of the population achieving sufficient PA levels), and ideal (65% of population: moderate PA, 30%: high PA, and 5%: inactive). We applied an "intrinsic value"approach to estimate the economic impact of each scenario. ResultsOverall, we estimate that between 2023 and 2040, about 80,000 to 110,000 deaths from all causes and 2.0 million to 2.9 million DALYs could be avoided by increasing PA levels in Saudi Arabia. The average annual economic loss from insufficient PA is valued at 0.49% to 0.68% of the current gross domestic product, with an average of US$5.4 billion to US$7.6 billion annually till 2040. The most avoidable disease burden and economic losses are expected among males and because of ischemic heart disease. ConclusionsThis study highlights that low PA levels will have considerable health and economic impacts in Saudi Arabia if people remain inactive and do not start following interventions. There is an urgent need to develop innovative programs and policies to encourage PA among all age and sex groups.</p

Effect of Virtual Reality and Artificial Intelligence on Anxiety and Behavior Among Individuals with Mental Disabilities in a Dental Setting

Over one million individuals suffer from mental disabilities in Saudi Arabia. These individuals are unable to express their dental treatment needs. This study focuses on assessing the impact of interventions based on virtual reality (VR) and artificial intelligence (AI) on the anxiety levels and behavioral responses of individuals with mental disabilities when undergoing dental treatments. Our findings indicate that VR and AI interventions have great potential in reducing anxiety and behavioral responses of individuals with mental disabilities. The reduction in anxiety levels was determined using a galvanic skin response sensor, whereas the improvement in behavior was evaluated using the Venham and Frankl behavior rating scales. Descriptive statistics were used to calculate the means, standard deviations, and ranges of the scores on various scales. The mean scores of pretreatment and posttreatment were analyzed. Our results from the above-mentioned tests revealed lowered anxiety and positive behavior when VR and AI interventions were used in dental care settings. In conclusion, our research indicates that distraction using VR and AI interventions is an effective method for lowering anxiety and improving behavior among individuals with mental disabilities during dental treatments. Moreover, a positive correlation between anxiety and behavior was observed

The Saudi Critical Care Society practice guidelines on the management of COVID-19 in the ICU: Therapy section

BACKGROUND: The rapid increase in coronavirus disease 2019 (COVID-19) cases during the subsequent waves in Saudi Arabia and other countries prompted the Saudi Critical Care Society (SCCS) to put together a panel of experts to issue evidence-based recommendations for the management of COVID-19 in the intensive care unit (ICU). METHODS: The SCCS COVID-19 panel included 51 experts with expertise in critical care, respirology, infectious disease, epidemiology, emergency medicine, clinical pharmacy, nursing, respiratory therapy, methodology, and health policy. All members completed an electronic conflict of interest disclosure form. The panel addressed 9 questions that are related to the therapy of COVID-19 in the ICU. We identified relevant systematic reviews and clinical trials, then used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach as well as the evidence-to-decision framework (EtD) to assess the quality of evidence and generate recommendations. RESULTS: The SCCS COVID-19 panel issued 12 recommendations on pharmacotherapeutic interventions (immunomodulators, antiviral agents, and anticoagulants) for severe and critical COVID-19, of which 3 were strong recommendations and 9 were weak recommendations. CONCLUSION: The SCCS COVID-19 panel used the GRADE approach to formulate recommendations on therapy for COVID-19 in the ICU. The EtD framework allows adaptation of these recommendations in different contexts. The SCCS guideline committee will update recommendations as new evidence becomes available

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication