Bacteria in rumen play pivotal roles in the digestion of nutrients to support energy for the host. In this study, metagenomic deep sequencing of bacterial metagenome extracted from the goats’ rumen generated 48.66 GB of data with 3,411,867 contigs and 5,367,270 genes. The genes were mainly functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) Carbohydrate-Active enZYmes (CAZy), and HMMER database, and taxonomically classified by MEGAN. As a result, 65,554 genes encoding for 30 enzymes/proteins related to lignocellulose conversion were exploited, in which nine enzymes were seen for the first time in goat rumen. Prevotella was the most abundant genus, contributing 30% hemicellulases and 36% enzymes/proteins for lignocellulose pretreatment, and supporting 98.8% of feruloyl esterases and 71.7% acetylxylan esterases. In addition, 18 of the 22 most lignocellulose digesting- potential contigs belonged to Prevotella. Besides, Prevotella possessed many genes coding for amylolytic enzymes. One gene encoding for endoxylanase was successfully expressed in E. coli. The recombinant enzyme had high Vmax, was tolerant to some salts and detergents, worked better at pH 5.5–6.5, temperature 40–50 °C, and was capable to be used in practices. Based on these findings, we confirm that Prevotella plays a pivotal role for hemicellulose digestion and significantly participates in starch, cellulose, hemicellulose, and pectin digestion in the goat rumen.

Colonoscopy is considered the gold-standard investigation for colorectal cancer screening. However, the polyps miss rate in clinical practice is relatively high due to different factors. This presents an opportunity to use AI models to automatically detect and segment polyps, supporting clinicians to reduce the number of polyps missed. Inspired by the success of UNets, a popular strategy for solving medical image segmentation tasks, this article proposes a novel framework for polyp segmentation called CRF-EfficientUNet, which enhances UNet using the EfficientNet encoder, a combined asymmetric loss function, and Conditional Random Field as a Recurrent Neural Network (CRF-RNN) layer on top. A novel loss function that combines pixel-wise cross-entropy loss and asymmetric similarity loss to solve the unbalanced imaging data problem is proposed. Training the proposed network with this loss function can achieve a considerably higher Dice score and better polyp segmentation prediction. In addition, we add the CRF-RNN layer to the proposed framework to improve the quality of semantic segmentation. Experimental results on popular benchmark datasets show that CRF-EfficientUNet achieves state-of-the-art accuracy compared to existing methods. The results of the experiments, which are performed on the CVC-ClinicDB dataset for training and testing, are 95.55% Dice and 92.23% IoU. While the experimental results on cross-dataset using Kvasir-SEG as the training set, CVC-ColonDB as the test set are 85.59% Dice and 76.19% IoU. These results indicate that the proposed method has high generalization capability and learning ability, and it can be a compelling choice for practical applications with considerable data variations.

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we adapt U-net and evaluate its performance with different modern convolutional neural networks as its encoder for polyp segmentation. One of the major challenges in training networks for polyp segmentation raises when the data are unbalanced, polyp pixels are often much lower in numbers than non-polyp pixels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased toward the non-polyp class which is particularly undesired because false negatives are more important than false positives. We propose an asymmetric similarity loss function to address this problem and achieve a much better tradeoff between precision and recall. Finally, we propose an ensemble method for further performance improvement. We evaluate the performance of well-known polyp datasets CVC-ColonDB and ETIS-Larib PolypDB. The best results are 89.13% dice, 79.77% IOU, 90.15% recall, and 86.28% precision. Our proposed method outperforms the state-of-the-art polyp segmentation methods.

We aimed to investigate the microbial diversity, mine lignocellulose-degrading enzymes/proteins, and analyze the domain structures of the mined enzymes/proteins in humus samples collected from the Cuc Phuong National Park, Vietnam. Using a high-throughput Illumina sequencer, 52 Gbs of microbial DNA were assembled in 2,611,883 contigs, from which 4,104,872 open reading frames (ORFs) were identified. Among the total microbiome analyzed, bacteria occupied 99.69%; the five ubiquitous bacterial phyla included Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, and Acidobacteria, which accounted for 92.59%. Proteobacteria (75.68%), the most dominant, was 5.77 folds higher than the second abundant phylum Bacteroidetes (13.11%). Considering the enzymes/proteins involved in lignocellulose degradation, 22,226 ORFs were obtained from the annotation analysis using a KEGG database. The estimated ratio of Proteobacteria/Bacteroidetes was approximately 1:1 for pretreatment and hemicellulases groups and 2.4:1 for cellulases. Furthermore, analysis of domain structures revealed their diversity in lignocellulose-degrading enzymes. CE and PL were two main families in pretreatment; GH1 and GH3-FN3 were the highest domains in the cellulase group, whereas GH2 and GH43 represented the hemicellulase group. These results validate that natural tropical forest soil could be considered as an important source to explore bacteria and novel enzymes/proteins for the degradation of lignocellulose.

In Vietnam, diarrhea, especially persistent diarrhea, is one of the most common diseases in children, while a significant proportion of cases are negative with pathogens; thus, there is an urgent need to understand gut bacterial dysbiosis. In this study, bacteria in the fecal samples of five healthy and ten diarrheal children were separated from other residues, then adopted to extract their metagenomic DNA for evaluating their diversity based on V3 and V6–V8 regions and the 16S rRNA gene by PCR-RFLP and PCR-DGGE. As a result, bacterial metagenomic DNAs with high quality, quantity and diversity were successfully extracted using a GeneJET kit and a chemical protocol. A sequence analysis of 73 representative DNA fragments from gels indicated a remarkable bacterial dysbiosis in all groups of diarrhea. Viral diarrhea was characterized by extremely reduced bacterial diversity with the blossom of Bifidobacterium and Streptococcus. Streptococcus was also the most abundant in persistent diarrhea. Beneficial bacteria that may play a role in the self- rebalance in intestinal bacterial communities, such as Bifidobacterium, Lactobacillus, and Enterococcus, were seen in all diarrheal groups, while Bacteroides and Akkermansia muciniphila were seen in the healthy group but absent in the diarrheal groups. This study provides additional evidence for a relationship between intestinal bacterial dysbiosis and diarrhea in children, emphasizing an increase in Streptococcus.

Persistent diarrhea remains a significant cause of morbidity in young children, yet the role of gut microbiota has not been fully clarified. This prospective study evaluated the diversity and predicted functions of the gut microbiota in 30 children aged 6–24 months with persistent diarrhea of unknown etiology (patient group, PG) and 30 healthy controls (healthy group, HG). Nearly full-length 16S rRNA genes from fecal bacterial metagenomic DNA were sequenced and taxonomically annotated. Subsequently, all downstream analyses, including diversity assessment, differential abundance and functional prediction analyses, and data visualization, were performed using R software (version 4.5.0, 2025). The PG showed lower Shannon and higher Simpson indices than the HG (p < 0.05), reflecting reduced microbial diversity. At the phylum level, Firmicutes predominated in the PG, whereas Actinobacteriota, Bacteroidota, and Verrucomicrobiota were more abundant in the HG (|log₂FC| > 1 and FDR < 0.05). At the genus and species levels, the PG exhibited a marked depletion of essential commensals such as Bifidobacterium longum, Faecalibacterium, Lactobacillus, and Eubacterium, alongside an enrichment of opportunistic taxa including Klebsiella, Enterococcus lactis, and Streptococcus spp. (FDR < 0.05). Functional predictions using PICRUSt2 indicated an enrichment of carbohydrate metabolism and reductions in amino acid metabolism, B-vitamin pathways, and the biosynthesis of endogenous antibiotics (FDR < 0.05). These findings suggest that the PG harbors a dysbiotic gut microbiota characterized by reduced diversity, depletion of key commensal taxa, expansion of opportunistic bacteria, and potentially adverse shifts in metabolic functions.