MATERIALS AND METHOD: In this research, we employ an open-access EEG signal public dataset containing three distinct classes: AD, FD, and control subjects. We then constructed a newly proposed EEGConvNeXt model comprised of a 2-dimensional CNN algorithm that firstly converts the EEG signals into power spectrogram-based images. Secondly, these images were used as input for the proposed EEGConvNeXt model for automated classification of AD, FD, and a control outcome. The proposed EEGConvNeXt model is therefore a lightweight model that contributes to a new image classification CNN structure based on the transformer model with four primary stages: a stem, a main model, downsampling, and an output stem.
RESULTS: The EEGConvNeXt model achieved a classification accuracy of ∼95.70% for three-class detection (AD, FD, and control), validated using a hold-out strategy. Binary classification cases, such as AD versus FD and FD versus control, achieved accuracies exceeding 98%, demonstrating the model's robustness across scenarios.
CONCLUSIONS: The proposed EEGConvNeXt model demonstrates high classification performance with a lightweight architecture suitable for deployment in resource-constrained settings. While the study establishes a novel framework for AD and FD detection, limitations include reliance on a relatively small dataset and the need for further validation on diverse populations. Future research should focus on expanding datasets, optimizing architecture, and exploring additional neurological disorders to enhance the model's utility in clinical applications.
METHODS: This retrospective study included 215 women who gave birth between 2018 and 2023. Hemoglobin levels were measured at three time points during pregnancy: first trimester (approximately 12 weeks), second trimester (13-27 weeks), and third trimester (28-36 weeks). The primary outcomes were the associations between hemoglobin levels and birth weight, birth weight Z-score, placental ratio, and placental weight. Statistical analyses were conducted to control for maternal and fetal factors and to determine the correlations between hemoglobin levels and delivery outcomes.
RESULTS: Hemoglobin levels in the first trimester were the best predictors of anemia in the third trimester (area under the curve (AUC), 0.63; sensitivity, 65 %; specificity, 65 %). Hemoglobin levels were inversely associated with birth weight, birth weight Z-score, placental ratio, and placental weight. The overall accuracy of predicting iron-deficiency anemia was high (sensitivity, 71 %; specificity, 76 %; AUC, 0.76). Significant associations were observed at p
METHODS: We analysed data from 2930 patients with SLE across 13 countries, collected over 38 754 clinic visits between 2013 and 2020. Clinic visit records were converted to panel data with 1-year intervals. The time-adjusted mean disease activity, termed AMS, was calculated. The yearly change in [Formula: see text], denoted as [Formula: see text], was regressed onto [Formula: see text] and other potential predictors using random-effects models. Some variables were split into a person-mean component to assess between-patient differences and a demeaned component to assess within-patient variability.
RESULTS: Overall, variability in SLE disease activity exhibited stabilisation over time. A significant inverse relationship emerged between a patient's disease activity in a given year and variability in disease activity in the subsequent year: a 1-point increase in person-mean disease activity was associated with a 0.27-point decrease (95% CI -0.29 to -0.26, p<0.001) in subsequent variability. Additionally, a 1-point increase in within-patient disease activity variability was associated with a 0.56-point decrease (95% CI -0.57 to -0.55, p<0.001) in the subsequent year. Furthermore, each 1-point increase in the annual average time-adjusted mean Physician Global Assessment was associated with a 0.08-point decrease (90% CI -0.13 to -0.03, p=0.002) in disease activity variability for the following year. Prednisolone dose and the duration of activity in specific organ systems exhibited negative and positive associations, respectively, with disease activity variability in the subsequent year. Patients from less affluent countries displayed greater disease activity variability compared with those from wealthier nations.
CONCLUSION: Disease activity tends to be less variable among patients with higher or more variable disease activity in the previous year. Within-patient variability in disease activity has a stronger impact on subsequent fluctuations than differences between individual patients.
RESULTS: The study included 24 participants and examined three muscles (m. Orbicularis Oris, m. Zygomaticus Major, and m. Mentalis) during five different facial expressions. Prior to thorough statistical analysis, features were extracted from the acquired electromyographs. Finally, classification was done with the use of logistic regression, random forest classifier and linear discriminant analysis. A statistically significant difference in muscle activity amplitudes was demonstrated between muscles, enabling the tracking of individual muscle activity for diagnostic and therapeutic purposes. Additionally other time domain and frequency domain features were analyzed, showing statistical significance in differentiation between muscles as well. Examples of pattern recognition showed promising avenues for further research and development.
CONCLUSION: Surface electromyography is a useful method for assessing the function of facial expression muscles, significantly contributing to the diagnosis and treatment of oral motor function disorders. Results of this study show potential for further research and development in this field of research.
METHODS: A cross-sectional study was conducted among medical ad non-medical students at Qassim University during September 2022-April 2023 after obtaining the Ethics Committee's permission. Raosoft was used for calculating the sample size, and participants were selected through convenience sampling. Both descriptive and inferential statistics were used to analyze and interpret the results, using SPSS version 25.
RESULTS: A total of 269 students participated in the study. The majority of them were male (52%), with a mean age of 22.28. The main purpose of SM usage was entertainment, followed by communication. More than 30% of them were using SM for 4-6 h per day, accessing SM 1-10 times in a day, with more than half of them feeling that they had SM addiction and that it was affecting their daily activities and sleep. The majority of them agreed that SM can be used for group discussion (78.1% vs. 71.6%) and knowledge sharing (93.7% vs. 90%). However, a statistically significant difference was observed about anxiety level between the two groups. A negative correlation was found between cumulative grade point average (CGPA) and anxiety level.
CONCLUSION: The findings suggest that SM has both positive and negative effects on academic performance and social anxiety. Continuous education and motivation about wise use of SM is warranted among students by parents, university authorities, and policymakers.
METHODS: This quasi-experimental study investigates the impact of mindfulness-based interventions on occupational stress and mental health among junior high school teachers in China. A total of 118 teachers participated in the study, with a randomly assigned experimental group undergoing an 4-week mindfulness training program, while the control group received no intervention. Standardized measures of occupational stress, mental health, coping self-efficacy, and mindfulness were used to assess the outcomes before and after the intervention.
FINDINGS: The findings revealed that teachers who participated in the mindfulness program experienced significant reductions in occupational stress and improvements in mental health and coping self-efficacy compared to the control group. Additionally mindfulness levels increased significantly among participants who underwent the training.
DISCUSSION: The results suggest that mindfulness-based interventions can effectively alleviate occupational stress and enhance psychological wellbeing among junior high school teachers in China, highlighting the importance of implementing such programs to support educators in managing stress and maintaining mental health.
METHODS: This study analyzed death records from January 2017 to June 2022, sourced from Malaysia's Health Informatics Centre, coded into ICD-10. Data anonymization adhered to ethical standards, with 387,650 death registrations included after quality checks. The dataset, limited to three-digit ICD-10 codes, underwent cleaning and an 80:20 training-testing split. Preprocessing involved HTML tag removal and tokenization. ML approaches, including BERT (Bidirectional Encoder Representations from Transformers), Gzip+KNN (K-Nearest Neighbors), XGBoost (Extreme Gradient Boosting), TensorFlow, SVM (Support Vector Machine), and Naive Bayes, were evaluated for automated ICD-10 coding. Models were fine-tuned and assessed across accuracy, F1-score, precision, recall, specificity, and precision-recall curves using Amazon SageMaker (Amazon Web Services, Seattle, WA). Sensitivity analysis addressed unbalanced data scenarios, enhancing model robustness.
RESULTS: In assessing ICD-10 coding with ML, Gzip+KNN had the longest training time at 10 hours, with BERT leading in memory use. BERT performed best for the F1-score (0.71) and accuracy (0.82), closely followed by Gzip+KNN. TensorFlow excelled in recall, whereas SVM had the highest specificity but lower overall performance. XGBoost was notably less effective across metrics. Precision-recall analysis showed Gzip+KNN's superiority. On an unbalanced dataset, BERT and Gzip+KNN demonstrated consistent accuracy.
CONCLUSION: Our study highlights that BERT and Gzip+KNN optimize ICD-10 coding, balancing efficiency, resource use, and accuracy. BERT excels in precision with higher memory demands, while Gzip+KNN offers robust accuracy and recall. This suggests significant potential for improving healthcare analytics and decision-making through advanced ML models.