Use of social media for the improvement of safety knowledge and awareness among Saudi Arabian phlebotomists.

Purpose: The purpose of this study is to investigate the use of social media for the improvement of safety knowledge and awareness among phlebotomists. Methods: As this study was intended to arrive at specific conclusions using empirical evidence, a deductive quantitative cross-sectional online survey design was adopted. A total of 521 phlebotomists participated in the survey, and 86 incomplete responders were removed, resulting in a final sample of 435 considered in this study. T-tests and ANOVA were used to analyze the data. Results: A total of 41.6% stated that social media was very effective, and 31.5% stated that it was somewhat effective in improving safety knowledge and awareness. in addition, this study revealed no major differences between male and female participants (p > 0.05) with respect to the effectiveness of social media. However, statistically significant differences (p < 0.05) among the age groups were identified in relation to the effectiveness of social media and the intention to use it in the future. Conclusion: Social media applications are effective for knowledge dissemination among healthcare professionals.

ETSNet: A deep neural network for EEG-based temporal-spatial pattern recognition in psychiatric disorder and emotional distress classification.

The use of EEG for evaluating and diagnosing neurological abnormalities related to psychiatric diseases and identifying human emotions has been improved by deep learning advancements. This research aims to categorize individuals with schizophrenia (SZ), their biological relatives (REL), and healthy controls (HC) using resting EEG brain source signal data defined by regions of interest (ROIs). The proposed solution is a deep neural network for the cortical source signals of the ROIs, incorporating a Squeeze-and-Excitation Block and multiple CNNs designed for eyes-open and closed resting states. The model, called EEG Temporal Spatial Network (ETSNet), has two variants: ETSNets and ETSNetf. Two evaluations were conducted to show the effectiveness of the proposed model. The average accuracy for the classification of SZ, REL, and HC using EEG resting data was 99.57% (ETSNetf), and the average accuracy for the classification of eyes-open (EO) and eyes-closed (EC) resting states was 93.15% (ETSNets). An ablation study was also conducted using two public datasets for intellectual and developmental disorders and emotional states, showing improved classification accuracy compared to advanced EEG classification algorithms when using ETSNets.

AM-UNet: automated mini 3D end-to-end U-net based network for brain claustrum segmentation.

Recent advances in deep learning (DL) have provided promising solutions to medical image segmentation. Among existing segmentation approaches, the U-Net-based methods have been used widely. However, very few U-Net-based studies have been conducted on automatic segmentation of the human brain claustrum (CL). The CL segmentation is challenging due to its thin, sheet-like structure, heterogeneity of its image modalities and formats, imperfect labels, and data imbalance. We propose an automatic optimized U-Net-based 3D segmentation model, called AM-UNet, designed as an end-to-end process of the pre and post-process techniques and a U-Net model for CL segmentation. It is a lightweight and scalable solution which has achieved the state-of-the-art accuracy for automatic CL segmentation on 3D magnetic resonance images (MRI). On the T1/T2 combined MRI CL dataset, AM-UNet has obtained excellent results, including Dice, Intersection over Union (IoU), and Intraclass Correlation Coefficient (ICC) scores of 82%, 70%, and 90%, respectively. We have conducted the comparative evaluation of AM-UNet with other pre-existing models for segmentation on the MRI CL dataset. As a result, medical experts confirmed the superiority of the proposed AM-UNet model for automatic CL segmentation. The source code and model of the AM-UNet project is publicly available on GitHub: https://github.com/AhmedAlbishri/AM-UNET.

Clinical Characteristics and Laboratory Abnormalities of Hospitalized and Critically Ill Children with Coronavirus Disease 2019: A Retrospective Study from Saudi Arabia.

BACKGROUND: COVID-19 was reported in several studies characterized by milder clinical course, benign disease, and peculiar epidemiologic patterns among pediatric patients compared to adults' disease. However, other studies indicated that critical cases also exist and are associated with preexisting cardiopulmonary comorbidities and concurrent multisystem inflammatory syndrome in children. METHODS: The study period was six months, May-October 2020. Data on demographics, clinical manifestations, laboratory abnormalities were extracted from the patients' hospital records. During the study period, 644 pediatric patients attended the hospital. They were all screened for SARS-CoV-2 using RT-PCR. Only the confirmed positive patients were included in the subsequent study analysis. They were hospitalized either in the general pediatric wards (GPW) or pediatric intensive care unit (PICU). RESULTS: Out of the total patients screened, 79 (12.3%) children were confirmed to have COVID-19 infection. All the confirmed COVID-19 patients were either admitted to the general pediatric wards (58; 73.4%) or PICU (21; 26.6%). The admission diagnoses for these children were acute gastroenteritis (22.85%), acute pneumonia (19%), clinical sepsis (17.7%), and multisystem inflammatory syndrome in children (10.1%). A significantly higher percentage of the PICU admitted patients showed shortness of breath (SOB) (P= 0.016). Respiratory insufficiencies, prematurity, and congenital heart diseases are the most reported comorbid conditions among the admitted children. The oxygen saturation was significantly lower among PICU patients than those in GPW (P=0.001). The total hospital stays differ significantly between the two groups, which were ten days for the PICU group compared to 4.5 days for the GPW group with a statistical significance noted (P= 0.001). CONCLUSION: Despite the observable variations in the clinical and laboratory findings among the hospitalized pediatric COVID-19 patients, no serious consequences among all patients were observed. The history of SOB and the initial oxygen saturation level were significantly associated with PICU admissions.

Novel Genetic Variant of Ataxia Telangiectasia Presenting with Necrotising Pneumonia and Bronchopleural Fistulae at the Age of 4 Years.

Ataxia-telangiectasia (A-T) is a genetically inherited disease, which is transmitted as an autosomal recessive disorder. There is a high incidence of consanguineous marriages in our area, so we believe that A-T may have higher incidence. A-T is characterised clinically by triad of cerebellar degeneration, telangiectasia, and immunodeficiency.  We are reporting a 4-year girl with a novel genetic variant of AT, which is not reported before in local or international literature. She presented with necrotising pneumonia complicated by bronchopleural fistulae.  She was treated successfully with antimicrobials and intravenous immunoglobulins  and other supportive measures without surgical intervention. Key Words: Ataxia telangiectasia, Necrotising pneumonia, Bronchopleural fistulae.

The impact of law enforcement on dispensing antibiotics without prescription: a multi-methods study from Saudi Arabia.

Background: Dispensing of antibiotics without a prescription (DAwP) has been widely practised among community pharmacies in Saudi Arabia despite being illegal. However, in May 2018, the law and regulations were enforced alongside fines. Consequently, we wanted to evaluate the impact of these changes.Methods: A study was conducted among 116 community pharmacies in two phases. A pre-law enforcement phase between December 2017 and March 2018 and a post-law enforcement phase one year later. Each phase consisted of a cross-sectional questionnaire-based survey and a simulated client method (SCM) approach. In the SCM, clients presented with either pharyngitis or urinary tract infections (UTI). In SCM, for each phase, all 116 pharmacies were visited with one of the scenarios.Results: Before the law enforcement, 70.7% of community pharmacists reported that DAwP was common with 96.6% and 87.7% of participating pharmacies dispensed antibiotics without a prescription for pharyngitis and UTI respectively. After the law enforcement, only 12.9% reported that DAwP is still a common practice, with only 12.1% and 5.2% dispensing antibiotics without prescriptions for pharyngitis and UTI respectively.Conclusion: law enforcement was effective. However, there is still further scope for improvement. This could include further educational activities with pharmacists, physicians and the public.