Human fecal metabolomic profiling could inform Clostridioides difficile infection diagnosis and treatment

Decreased secondary faecal bile acids in children with ulcerative colitis and Clostridioides difficile infection

BACKGROUND

Patients with ulcerative colitis (UC) have an increased risk of Clostridioides difficile infection (CDI). There is a well-documented relationship between bile acids and CDI.

AIMS

To evaluate faecal bile acid profiles and gut microbial changes associated with CDI in children with UC.

METHODS

This study was conducted at Children's Hospital Los Angeles. Faecal bile acids and gut microbial genes related to bile acid metabolism were measured in 29 healthy children, 23 children with mild to moderate UC without prior CDI (UC group), 16 children with mild to moderate UC with prior CDI (UC+CDI group) and 10 children without UC with prior CDI (CDI group).

RESULTS

Secondary faecal bile acids, especially lithocholic acid (3.296 vs 10.793, P ≤ 0.001) and ursodeoxycholic acid (7.414 vs 10.617, P ≤ 0.0001), were significantly lower in children with UC+CDI when compared to UC alone. Secondary faecal bile acids can predict disease status between these groups with 84.6% accuracy. Additionally, gut microbial genes coding for bile salt hydrolase, 7α-hydroxysteroid dehydrogenase and 7α/β-dehydroxylation were all diminished in children with UC+CDI compared to children with UC alone.

CONCLUSIONS

Bile acids can distinguish between children with UC based on their prior CDI status. Bile acid profile changes can be explained by gut microbial genes encoding for bile salt hydrolase, 7α-hydroxysteroid dehydrogenase and 7α/β-dehydroxylation. Bile acid profiles may be helpful as biomarkers to identify UC children who have had CDI and may serve as future therapeutic targets.

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.