Quality of life among schoolchildren with type 1 diabetes mellitus and the satisfaction of their guardians towards school health care in Saudi Arabia.

AIMS: This study aimed to assess the quality of life of schoolchildren with type 1 diabetes mellitus (T1DM) and determine their guardians' satisfaction of diabetes health care in Saudi Arabian schools. METHODS: A cross-section multicenter study was conducted from February to July 2022 among Schoolchildren with T1DM in Saudi Arabia. The study included T1DM school children aged 6-18 years. The patients' health-related quality of life (HRQoL) data were collected and determined using a modified version of the PedsQL 3.0 Diabetes Module. RESULTS: The grand total median PedQL-DM score among the included participants (N = 283) was 64.7, while items related to diabetes symptoms and diabetes management were 61.1 and 68.7, respectively. Schoolchildren who have lower HbA1c levels and take care of regular monitoring of their blood glucose showed significantly better quality of life concerning diabetes symptoms. A significant number of guardians claimed they were not satisfied with the current status of diabetes management at schools. CONCLUSIONS: The overall HRQoL among schoolchildren with T1DM was average and acceptable to some extent. The PedsQL-DM median score was higher among those who received health care during school time. The guardians' satisfaction of diabetes health care was low, emphasizing the role of health clinics in schools.

Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.

Persistent COVID-19 symptoms at least one month after diagnosis: A national survey.

BACKGROUND: Post-acute COVID-19 syndrome (PACS) is an important healthcare burden. We examined persistent symptoms in COVID-19 patients at least four weeks after the onset of infection, participants' return to pre-COVID-19 health status and associated risk factors. METHODS: Cross-sectional study was conducted (December 2020 to January 2021). A validated online questionnaire was sent to randomly selected individuals aged more than 14 years from a total of 1397,386 people confirmed to have COVID-19 at least 4 weeks prior to the start of this survey. This sample was drawn from the Saudi ministry of health COVID-19 testing registry system. RESULTS: Out of the 9507 COVID-19 patients who responded to the survey, 5946 (62.5%) of them adequately completed it. 2895 patients (48.7%) were aged 35-44 years, 64.4% were males, and 91.5% were Middle Eastern or North African. 79.4% experienced unresolved symptoms for at least 4 weeks after the disease onset. 9.3% were hospitalized with 42.7% visiting healthcare facility after discharge and 14.3% requiring readmission. The rates of main reported persistent symptoms in descending order were fatigue 53.5%, muscle and body ache 38.2%, loss of smell 35.0%, joint pain 30.5%, and loss of taste 29.1%. There was moderate correlation between the number of symptoms at the onset and post-four weeks of COVID-19 infection. Female sex, pre-existing comorbidities, increased number of baseline symptoms, longer hospital-stay, and hospital readmission were predictors of delayed return to baseline health state (p < 0.05). CONCLUSION: The symptoms of PACS are prevalent after contracting COVID-19 disease. Several risk factors could predict delayed return to baseline health state.

Sensitivity of DiaRem Scoring System in Predicting Type Two Diabetes Mellitus Resolution After Bariatric Surgery in Qassim Region.

INTRODUCTION: Type two diabetes mellitus (T2DM) remission has been observed as an additional benefit of bariatric surgery for morbidly obese diabetic patients. There are many scoring systems for identifying factors that predict diabetes remission; however, there is as yet no universally applicable scoring system. AIM: This study aims to test the sensitivity of the DiaRem scoring system for predicting the resolution of T2DM in morbidly obese patients who underwent bariatric surgery at King Fahad Specialist Hospital in Buraydah, Saudi Arabia. METHODS:  This was a non-randomized controlled trial conducted at King Fahd Specialist Hospital in Buraydah, Saudi Arabia. Visiting patients at first screening were enrolled based on eligibility criteria. Data were collected according to the given parameters such as gender, age, body mass index (BMI), duration of diabetes mellitus (DM), medications (insulin, oral antihyperglycemic agents, number of tablets if used, or no medications use), presence of comorbidities, such as hypertension and dyslipidemia, HbA1c level (before surgery and at third, sixth, and 12th months after surgery), and fasting blood glucose (FBG) level (before and after surgery). RESULTS: A total of 96 diabetic patients were enrolled (35 males vs 61 females) with a mean age of 46.5 years. Laparoscopic sleeve gastrectomy was the most commonly performed surgery. The most common associated comorbidities were hypertension (50%) and hypothyroidism (14.6%). Results of the DiaRem scoring system showed 0-2 points in 15.6% patients, 3-7 points in 39.6% patients, 8-12 in 26% patients, 13-17 in 9.4% patients, and 18-22 in 9.4% patients. The lowest DiaRem score was associated with a higher value of BMI, shorter DM duration, and lower mean values of HbA1c and FBG post-surgery. CONCLUSION: Consistent with the literature, our results indicated that those with an increased BMI, shorter duration of DM, and lower values of HbA1c post-FBG had a greater chance of diabetes remission postoperatively.