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Fulltext YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Diagnostics (Basel), 2023 Jul 05;13(13).
PMID: 37443674 DOI: 10.3390/diagnostics13132280

Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.
Fulltext Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Healthcare (Basel), 2023 Jun 15;11(12).
PMID: 37372880 DOI: 10.3390/healthcare11121762

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.
Fulltext A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M

Diagnostics (Basel), 2023 Jul 21;13(14).
PMID: 37510187 DOI: 10.3390/diagnostics13142442

Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
Fulltext Global Burden of Anorexia Nervosa and Bulimia Nervosa, 1990 to 2023, With Forecasts to 2050: A Global Burden of Disease Study 2023 Analysis

Bai R, Dong W, Wang X, Zhang A, Santomauro DF, GBD Eating Disorders Collaborators, et al.

JAMA Psychiatry, 2026 Oct 28.
PMID: 42949727 DOI: 10.1001/jamapsychiatry.2026.3457

IMPORTANCE: Eating disorders are a major cause of nonfatal mental health burden, yet global evidence on their long-term trends, geographic distribution, and future trajectories remains limited.
OBJECTIVE: To estimate the global, regional, and national prevalence and disability burden of anorexia nervosa and bulimia nervosa-and mortality attributable to anorexia nervosa-from 1990 to 2023, and to forecast prevalence to 2050.
DESIGN, SETTING, AND PARTICIPANTS: Using Global Burden of Disease (GBD) 2023 methods, data were synthesized from systematic literature reviews, population surveys, and vital registration systems. Estimates were produced for 204 countries and territories from 1990 to 2023, with forecasts through 2050. These data were analyzed from October 2025 to August 2026.
EXPOSURE: Anorexia nervosa or bulimia nervosa diagnosis.
MAIN OUTCOMES AND MEASURES: Counts and age-standardized rates with 95% uncertainty intervals (UIs) for anorexia nervosa and bulimia nervosa prevalence, years lived with disability (YLDs), and disability-adjusted life-years (DALYs), and anorexia nervosa mortality, reported by age, sex, and location.
RESULTS: In 2023, an estimated 18.1 million people (95% UI, 13.9-24.0) globally were living with anorexia nervosa or bulimia nervosa, of whom 12.6 million (69.4%) were female, 5.5 million were male (30.6%), and 41.2% were aged 10 to 24 years. These cases accounted for 3.83 million DALYs (95% UI, 2.23-5.69). Since 1990, the global number of prevalent cases increased by 71.0%, while age-standardized prevalence and DALY rates rose by about 21%. Females experienced substantially higher prevalence and DALY rates than males. The highest age-standardized prevalence rates were observed in high-income regions, particularly Australasia and Western Europe, whereas the largest relative increases in age-standardized prevalence occurred in East Asia, South Asia, and Southeast Asia. Forecasts indicate continued increases in prevalent cases through 2050.
CONCLUSIONS AND RELEVANCE: This GBD 2023 analysis found that anorexia nervosa and bulimia nervosa contribute substantially to global nonfatal health loss. These findings support the integration of eating disorders into mental health surveillance, prevention, and service planning worldwide.