Investigating the association of acute kidney injury (AKI) with COVID-19 mortality using data-mining scheme.

COVID-19 has caused significant challenges in kidney research and disease management. Data mining techniques such as logistic regression (LR) and decision tree (DT) were used to model data. All analyses were performed using SPSS 25 and Python 3. The incidence of acute kidney injury (AKI) was 14.1% and the overall mortality risk was 13% among COVID-19 patients. The mortality was associated with, AKI, age, marital status, smoking status, heart failure, chronic obstructive pulmonary disease, malignancy, and SPO2 level using LR. The accuracy, sensitivity, specificity, and area under the curve of the DT (and LR) classifier were 70% (85%), 73% (75%), 78% (79%), and 77% (81%), respectively. Based on the DT model, the variable most significantly associated with COVID-19 mortality was AKI followed by age, high WBC count, BMI, and lymphocyte count. It was concluded that the incidence of AKI was high, and AKI was identified as one of the important factors that played an effective role in mortality due to COVID-19.

Investigation of hand muscle fatigue and its influential factors in manual tasks.

Muscle fatigue (MF) can lead to musculoskeletal disorders (MSDs) in the long term; however, it can be managed if the causes are well known. This study aimed to examine the grip force (GF) and grip fatigue (GFa) of employees with light, moderate and heavy manual tasks using a dynamometer and find their possible relationship with other factors. The nature of heavy manual tasks led to more experience of GFa and GF of the right hand. Moreover, the equal need for both hands in occupations with light and moderate manual tasks is the reason for more GFa in the left hand. In this primary study, the height, weight and age of subjects and their exposure to vibration had a decisive effect on GF. In order to determine the accurate effects of the aforementioned risk factors on MF, it is recommended for future studies to be performed on larger populations.

Upper trapezius fatigue in carpet weaving: the impact of a repetitive task cycle.

INTRODUCTION: Shoulder disorders are one of the most prevalent musculoskeletal disorders among carpet weavers. The most important cause of these disorders is muscle fatigue. The aim of the present study is to investigate the effect of carpet weaving characteristics on upper trapezius (UTr) muscle fatigue during a task cycle. METHOD: In this cross-sectional study, 9 women and 3 men participated. During an 80-min cycle of carpet weaving, a times-series model was applied to assess electromyography amplitude and frequency changes. RESULT: According to the joint analysis of electromyogram spectrum and amplitude method, the participants experienced 0% force decrease, 0.9% recovery, 18% force increase and 72% fatigue in the left UTr. Furthermore, the rates of force decrease, recovery, force increase and fatigue in the right UTr were 18%, 18%, 18% and 45%, respectively. Fatigue in the right and the left UTr was reported to be the dominant state during one carpet weaving task cycle. CONCLUSION: Task cycle appears to have a significant impact on UTr fatigue in participants, and UTr fatigue can be considered a serious risk factor in shoulder musculoskeletal disorders. Hence, further studies should focus on better workstations and work-rest periods during various subtasks.

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

OBJECTIVES: This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial neural networks approach. METHODS: A total of 92 workers with LBP as the case group and 68 healthy workers as a control group were selected in various industrial units with similar occupational conditions. The demographic information and personal, occupational, and psychosocial factors of the participants were collected via interview, related questionnaires, consultation with occupational medicine, and also the Rapid Entire Body Assessment worksheet and National Aeronautics and Space Administration Task Load Index software. Then, 16 risk factors for LBP were used as input variables to develop the prediction model. Networks with various multilayered structures were developed using MATLAB. RESULTS: The developed neural networks with 1 hidden layer and 26 neurons had the least error of classification in both training and testing phases. The mean of classification accuracy of the developed neural networks for the testing and training phase data were about 88% and 96%, respectively. In addition, the mean of classification accuracy of both training and testing data was 92%, indicating much better results compared with other methods. CONCLUSION: It appears that the prediction model using the neural network approach is more accurate compared with other applied methods. Because occupational LBP is usually untreatable, the results of prediction may be suitable for developing preventive strategies and corrective interventions.