Validation of the Arabic Version of General Medication Adherence Scale (GMAS) in Sudanese Patients with Diabetes Mellitus

Objective: The aim of this study was to validate the Arabic version of General Medication Adherence Scale (GMAS) in Sudanese patients with type 2 diabetes mellitus (T2DM). Methods: A 3-month cross-sectional study was conducted among patients with T2DM at Al- Daraja Health Center, located in Wad Medani, Sudan. A convenient sample of patients was selected, and the study sample size was calculated using the item response ratio. Factorial, known group, and construct validities were determined. Internal consistency and reliability were also determined. Results: Responses were provided by 500 patients. The average medication adherence score was 30 (median 31). The normed fit index (NFI) was 0.950, the comparative fit index (CFI) was 0.963, the incremental fit index (IFI) was 0.963, and the root-mean-square error of approximation (RMSEA) was 0.071. The results from these fit indices indicated a good model. Factorial, known group and construct validities were all established. A significant association was found between adherence score and age (P = 0.03) since a larger proportion of older patients were found to have high adherence compared to patients in other age groups. The reliability (α) of the questionnaire was 0.834. Conclusion: The Arabic version of GMAS was validated in Sudanese patients with T2DM making it a suitable scale to be used in this population

ICAR: endoscopic skull‐base surgery

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Kinetics of Oxidation of Aliphatic Alcohols by Potassium Dichromate in Aqueous and Micellar Media

The kinetics of oxidation of four aliphatic alcohols in acidic aqueous and micellar media were investigated. The reaction was found to be first-order with respect to both alcohol and oxidant. Pseudo-first-order kinetics were found to be perfectly applicable with ethanol, 1-propanol and 2-propanol while deviation was observed at intermediate stages of the reaction with methanol. The pseudo-first-order rate constants were found to be independent of concentration of the oxidant. The presence of TX-100 enhanced the rate of the reaction for all alcohols. Negative salt effects were observed with addition of KCl to the reaction mixture. A suitable mechanism for the reaction was suggested which agrees with the experimental findings.Keywords: Oxidation, dichromate, alcohol, pseudo-first-order, micellar effec

Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications

As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy

Beta blockers may be protective in COVID-19; findings of a study to develop an interpretable machine learning model to assess COVID-19 disease severity in light of clinical findings, medication history, and patient comorbidities

The coronavirus disease 2019 (COVID-19) has overwhelmed healthcare systems and continues to pose a significant threat worldwide. Predicting disease severity would enhance treatment provision and resource allocation. Although multiple studies were conducted to assess COVID-19's severity using machine learning (ML) models, few studies focus on patient medication history and comorbidities. In this study, ML algorithms were trained using a comprehensive dataset comprising medication history, comorbidities, and clinical findings. Patient data was gathered from King Fahad University Hospital (KFUH) in Saudi Arabia (IRB#: 2021-05-480). The dataset comprised 622 positive COVID-19 with 49 features. Three experiments were conducted to train four ML algorithms, including random forest (RF), gradient boosting machine (GMB), extreme gradient boost (XGBoost), and extra trees (ET). Findings revealed that GBM outperformed other models with 96.30% accuracy, 95.80% precision, 97.64% recall, and 96.69% F-score, with 23 features. Moreover, the permutation feature importance technique suggested that the five most influential features for forecasting disease severity were “CRP level”, “CO2 level”, “SrCr”, “Tocilizumab”, and “Age”. In addition, the shapley additive explanation (SHAP) recommended that the “D-Dimer level”, “CrCl”, and “Hypertension” were also influential. The development of an effective GBM model has the potential to aid medical specialists in the assessment of disease severity. While several models take into account patient presentation and laboratory findings, this study is unique in its scope, considering a far more comprehensive patient profile. The developed model was able to accurately predict features that have been clinically shown to correlate with disease severity. Of interest the model was able to identify a pattern of association between the use of certain medications such and disease severity. We report that the use of beta blockers may be associated with reduced severity, whereas the use of immune modulating drugs namely tocilizumab appeared to be associated with poor disease outcomes in this patient population

Beating the empty pelvis syndrome: the PelvEx Collaborative core outcome set study protocol

Introduction The empty pelvis syndrome is a significant source of morbidity following pelvic exenteration surgery. It remains poorly defined with research in this field being heterogeneous and of low quality. Furthermore, there has been minimal engagement with patient representatives following pelvic exenteration with respect to the empty pelvic syndrome. ‘PelvEx—Beating the empty pelvis syndrome’ aims to engage both patient representatives and healthcare professionals to achieve an international consensus on a core outcome set, pathophysiology and mitigation of the empty pelvis syndrome.Methods and analysis A modified-Delphi approach will be followed with a three-stage study design. First, statements will be longlisted using a recent systematic review, healthcare professional event, patient and public engagement, and Delphi piloting. Second, statements will be shortlisted using up to three rounds of online modified Delphi. Third, statements will be confirmed and instruments for measurable statements selected using a virtual patient-representative consensus meeting, and finally a face-to-face healthcare professional consensus meeting.Ethics and dissemination The University of Southampton Faculty of Medicine ethics committee has approved this protocol, which is registered as a study with the Core Outcome Measures in Effectiveness Trials Initiative. Publication of this study will increase the potential for comparative research to further understanding and prevent the empty pelvis syndrome.Trial registration number NCT05683795