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Background: Type 1 diabetes mellitus (T1DM) is on the rise globally. Patient education is a major cornerstone in diabetes management. Diabetes educators play a pivotal role in educating patients with diabetes mellitus. Objectives: To estimate the impact of a diabetes educator-led clinic on the control of T1DM among adults. This was measured through mean changes in HbA1c levels and alterations in perceived dietary and physical activity compliance among patients attending a Diabetes Centre in the Qassim region of Saudi Arabia. Methods: We enrolled 97 adult T1DM patients in a quasi-experimental study, conducted at a certified diabetes educator-led clinic. All patients were followed up for 6 months pre-intervention and for a minimum of 6 months post-intervention. Patients' weight, self-monitoring of blood glucose, hypoglycaemic events frequency, HbA1c level and lipid profiles were routinely recorded. In addition, compliance with advice on dietary plan and physical activity recommendations was also assessed at the pre-intervention, baseline and post-intervention stages. Results: Post-intervention, participants reported better compliance with dietary and physical activity advice. Fewer hypoglycaemic events and higher self-monitoring of blood glucose frequencies were also noted. While there was no significant difference between pre-intervention and baseline mean HbA1c levels, mean baseline and post-intervention HbA1c levels showed a clinical and statistically significant difference of 1.01 (95% confidence interval [CI]: 0.74-1.28). Mean low-density lipoprotein (LDL) and triglyceride differences also showed small but significant changes. Conclusion: Attendance at a diabetes educator-led clinic had a positive and significant impact on the control of T1DM among adults. We recommend diabetes educator employment at outpatient clinics for adults with T1DM.

This study builds upon previous research that utilised a vibration-based machine learning (VML) approach for diagnosing rotor-related faults in rotating machinery. The original method used artificial neural networks (ANN) to classify rotor-related faults based on optimised vibration parameters from the time and frequency domains. This study expands the application of this vibration-based machine learning approach to include the anti-friction bearing faults in addition to the rotor faults. The earlier suggested vibration-based parameters, both in time and frequency domains, are further revised to accommodate bearing-related defects. The study utilises the measured vibration data from a laboratory-scale rotating test rig with different experimentally simulated faults in the rotor and bearings. The proposed VML model is developed for both rotor and bearing defects at a rotor speed that is above the first critical speed. To gauge the robustness of the proposed VML model, it is further tested at two different rotating speeds, one below the first critical speed and the other above the second critical speed. The paper presents the methodology, the rig and measured vibration data, the optimised parameters, and the findings. [ABSTRACT FROM AUTHOR]

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu. [ABSTRACT FROM AUTHOR]

Objective: This study aims to identify the prevalence of aspirin use among type 2 diabetic (T2DM) patients and assess the concordance in aspirin use among these patients as prescribed by physicians and as recommended by the Aspirin-Guide app. Methods: A total of 301 T2DM patients from King Khalid University Hospital in Riyadh, Saudi Arabia participated in this cross-sectional study. Patient's electronic medical records through eSihi system were reviewed and all data included in the free online and mobile app called Aspirin-Guide were collected in a specially designed data checklist. Result: The prevalence of aspirin use was more common in patients who were in the age group of 51 to 59 and male participants' with T2DM. Males were nearly twice more likely to use aspirin compared to females (P = 0.001). Based on recommendations from the Aspirin-Guide app, 26% of the patients who were on aspirin (N = 51) were not eligible for aspirin therapy, while 37.7% (N = 40) of the patients eligible for aspirin therapy had not been put on aspirin by their physicians (P = 0.039). Male sex (P = 0.003), use of statins (P = 0.001), and being advised to use aspirin (P = 0.041), were significantly associated with aspirin use in T2DM patients. Conclusion: There was a significant difference in the proportion of patients currently on aspirin as prescribed by their physicians and those eligible for aspirin therapy as per the Aspirin-Guide app. The use of an app to uniformized aspirin use among eligible patients should be based on up-to-date guidelines and account for patient acceptability and willingness to commence treatment. [ABSTRACT FROM AUTHOR]

Background: Diabetes mellitus is a complex disorder that requires continuous management and medical care. The purpose of this review is to identify and summarize the barriers that affect diabetes management in Saudi Arabia. Methods: Studies that have examined the quality of diabetes management in Saudi Arabia were identified through online and manual literature searches. Two researchers independently searched and assessed for inclusion/exclusion criteria. All studies were screened by a specialist for the significance of the review. Studies that were included were evaluated for relevance, methodological rigor, and credibility by giving a quality score based on Russell and Gregory's criteria. Results: This review presents an overview of the quality of diabetes management and issues and barriers concerning the improvement of diabetes care in Saudi Arabia. The online literature search yielded 11 studies which met the inclusion criteria. Factors affecting the quality of diabetes care can be categorized into patient factors (such as adherence, compliance, attitudes, beliefs, knowledge, financial resources and co-morbidities) and healthcare providers' factors (including beliefs, attitudes and knowledge, patient -- provider interaction and communication). Conclusion: The identified barriers, both from patients and healthcare providers, will help healthcare authorities to improve diabetes management in Saudi Arabia. Improvement of health awareness about disease and disease management should be tailored through continuous patient education. Continuous training and seminars will also expand providers' knowledge that will ensure quality and effective diabetes management. [ABSTRACT FROM AUTHOR]

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Jackson Daniel et al.)