Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of Helminth eggs and its application online

Helminth-induced immune modulation in colorectal cancer: exploring therapeutic applications

Colorectal cancer is one of the most lethal tumors, posing a financial and healthcare burden. This study investigates how helminths and pre-existing diseases such as colitis, obesity, diabetes, and gut microbiota issues influence colon cancer development and prognosis. The immune system's protective immunosuppressive response to helminth invasion minimizes inflammation-induced cell damage and DNA mutations, lowering the risk of colorectal cancer precursor lesions. Helminth infection-mediated immunosuppression can hasten colorectal cancer growth and metastasis, which is detrimental to patient outcomes. Some helminth derivatives can activate immune cells to attack cancer cells, making them potentially useful as colorectal cancer vaccines or therapies. This review also covers gene editing approaches. We discovered that using CRISPR/Cas9 to inhibit live helminths modulates miRNA, which limits tumor growth. We propose more multicenter studies into helminth therapy's long-term effects and immune regulation pathways. We hope to treat colorectal cancer patients with helminth therapy and conventional cancer treatments in an integrative setting.

Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks

Background

Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist's abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid.

Methods

Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection.

Results

The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947.

Conclusion

This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model's performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.

Potentials and challenges in the isolation and detection of ascarid eggs in complex environmental matrices

Ascarid infections constitute a major concern for both human and animal health risk assessment. Although being effectively transmitted by soil, water and contaminated food, reliable detection of ascarid eggs in environmental media often remains challenging. However, contamination of the environment with ascarid ova has gained more attention as a decisive part of proper risk assessment in recent years. Due to various factors, such as sample matrices, dissociation detergents and flotation solutions, defined and standardised protocols for the isolation of eggs from complex environmental matrices are difficult to establish and therefore limited. Thus, this study reviews common techniques used for the recovery of ascarid eggs from environmental media with special emphasis on sampling strategies, purification procedures and microscopic as well as molecular detection of egg contamination. Despite various advancements, mainly in the field of molecular methods leading to more reliable and sensitive detection, it can be concluded that there is still a need for unified guidelines for sampling and recovery of ascarid eggs derived from complex environmental matrices.

Current status of Clonorchis sinensis and clonorchiasis in Korea: epidemiological perspectives integrating the data from human and intermediate hosts

Clonorchis sinensis is a carcinogenic liver fluke that causes clonorchiasis in humans. Clonorchiasis is prevalent in East Asian countries, and approximately 15–20 million individuals are estimated to be infected with this fluke globally. This review highlights the current status of C. sinensis and clonorchiasis in Korea from the epidemiological perspective involving the analysis of humans and intermediate hosts. Despite the recent decline in C. sinensis infection rate in Korea, C. sinensis infections remain endemic in 5 major river basins (Han-gang, Geum-gang, Seomjin-gang, Yeongsan-gang and Nakdong-gang; gang means river) with a high incidence of cholangiocarcinoma. A noticeable pattern involves increasing mild infections among patients diagnosed positive for C. sinensis eggs. The infection rate of C. sinensis metacercariae in the second intermediate host, freshwater fish, is also maintained at a substantial level. Thus, the One Health approach integrating different sectors and disciplines is recommended to accelerate and sustain control of C. sinensis, thereby leading to successful eradication. Health promotion via information dissemination and health education should be extended to prevent the consumption of raw freshwater fish by residents living in high-risk areas.