Variability in white blood cell count during uncomplicated malaria and implications for parasite density estimation: a WorldWide Antimalarial Resistance Network individual patient data meta-analysis

The role of immune-inflammatory markers in children with complicated and uncomplicated malaria in Enugu, Nigeria

BACKGROUND

There is currently insufficient data regarding immune parameters and relationship with severity of malaria infection in Enugu, Nigeria where the economic and social costs of the disease and its management are extremely high. This study was conducted to determine the relationship between malaria severity and some immune-inflammatory markers among malaria-infected children in Enugu, Nigeria.

METHODS

The study adopted a case control design. Eligible children were categorized into three groups - complicated, uncomplicated and healthy children. Pro-inflammatory cytokines -interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α); and anti-inflammatory cytokine - interleukin-10 (IL-10) were assayed using enzyme-linked immunosorbent assay (ELISA) technique, while immune cell ratios - neutrophil lymphocyte ratio (NLR) and monocyte lymphocyte ratio (MLR) were calculated from full blood count results.

RESULTS

The overall mean age of the participants was 7.3 ± 3.4 (range: 6 months - 12 years) and the male-female ratio was 1:1. There was no significant difference between the ages of the three groups (P = 0.44). The Mean levels of IFN-γ, TNF-α, and NLR were higher in complicated than uncomplicated malaria (266.9 ± 66.3pg/ml vs. 62.5 ± 6.4pg/ml, p < 0.001; 140.3 ± 30.0pg/ml vs. 42.0 ± 9.0pg/ml, p < 0.001; and 32.9 ± 16.2pg/ml vs. 17.8 ± 6.0pg/ml, p < 0.001, respectively); and higher in uncomplicated malaria than healthy children (62.5 ± 6.4pg/ml vs. 40.6 ± 9.1pg/ml, p < 0.001; 42.0 ± 9.0pg/ml vs. 105.7 ± 32.1, p < 0.001; 17.8 ± 6.0pg/ml vs. 18.7 ± 6.2pg/ml, p < 0.001, respectively). On the other hand, the mean level of IL-10 is higher in uncomplicated than complicated malaria (105.73 ± 32.06pg/ml vs. 40.60 ± 9.11pg/ml, p < 0.001). There was a positive correlation between NLR and IFN-γ (r = 0.815; p = 0.003), as well as NLR and TNF-α (r = 0.745; p = 0.002).

CONCLUSION

Complicated malaria is associated with higher levels of pro-inflammatory cytokines while uncomplicated malaria is associated with higher levels of anti-inflammatory cytokines. NLR correlates positively with pro-inflammatory cytokines, and could be useful in evaluation for the severity of malaria infection.

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy

Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating smartphones with microscopes. This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. Methods: This approach combines image segmentation and a convolutional neural network (CNN) to distinguish between white blood cells, artifacts, and malaria parasites. A portable system integrates a microscope with a graphical user interface to facilitate rapid malaria detection from smartphone images. We trained the CNN model using open-source data from the Chittagong Medical College Hospital, Bangladesh. Results: The validation process, using microscopic TBS from both the training dataset and an additional dataset from Sub-Saharan Africa, demonstrated that the proposed model achieved an accuracy of 97.74% ± 0.05% and an F1-score of 97.75% ± 0.04%. Remarkably, our proposed model with AlexNet surpasses the reported literature performance of 96.32%. Conclusions: This algorithm shows promise in aiding malaria-stricken regions, especially those with limited resources.