Cytolethal distending toxin B inoculation leads to distinct gut microtypes and IBS-D-like microRNA-mediated gene expression changes in a rodent model.

Diarrhea-predominant irritable bowel syndrome (IBS-D), associated with increased intestinal permeability, inflammation, and small intestinal bacterial overgrowth, can be triggered by acute gastroenteritis. Cytolethal distending toxin B (CdtB) is produced by gastroenteritis-causing pathogens and may underlie IBS-D development, through molecular mimicry with vinculin. Here, we examine the effects of exposure to CdtB alone on gut microbiome composition, host intestinal gene expression, and IBS-D-like phenotypes in a rat model. CdtB-inoculated rats exhibited increased anti-CdtB levels, which correlated with increased stool wet weights, pro-inflammatory cytokines (TNFα, IL2) and predicted microbial metabolic pathways including inflammatory responses, TNF responses, and diarrhea. Three distinct ileal microbiome profiles (microtypes) were identified in CdtB-inoculated rats. The first microtype (most like controls) had altered relative abundance (RA) of genera Bifidobacterium, Lactococcus, and Rothia. The second had lower microbial diversity, higher Escherichia-Shigella RA, higher absolute E. coli abundance, and altered host ileal tissue expression of immune-response and TNF-response genes compared to controls. The third microtype had higher microbial diversity, higher RA of hydrogen sulfide (H2S)-producer Desulfovibrio, and increased expression of H2S-associated pain/serotonin response genes. All CdtB-inoculated rats exhibited decreased ileal expression of cell junction component mRNAs, including vinculin-associated proteins. Significantly, cluster-specific microRNA-mRNA interactions controlling intestinal permeability, visceral hypersensitivity/pain, and gastrointestinal motility genes, including several previously associated with IBS were seen. These findings demonstrate that exposure to CdtB toxin alone results in IBS-like phenotypes including inflammation and diarrhea-like stool, decreased expression of intestinal barrier components, and altered ileal microtypes that influenced changes in microRNA-modulated gene expression and predicted metabolic pathways consistent with specific IBS-D symptoms.

Coronavirus Disease 2019 (COVID-19) Pandemic Lifestyle Changes May Have Influenced Small Bowel Microbial Composition and Microbial Resistance.

BACKGROUND: The coronavirus disease 2019 (COVID-19) global pandemic necessitated many severe lifestyle changes, including lockdowns, social distancing, altered food consumption and exercise patterns, and extensive hygiene practices. These extensive changes may have affected the human gut microbiome, which is highly influenced by lifestyle. AIMS: To examine the potential effects of pandemic-related lifestyle changes on the metabolically relevant small bowel microbiome. METHODS: Adult subjects presenting for upper endoscopy without colonoscopy were identified and divided into two matched groups: pre-pandemic (February 2019-March 2020) and intra-pandemic (April 2021-September 2021, all COVID-19 negative). Duodenal aspirates and blood samples were collected. Duodenal microbiomes were analyzed by 16S rRNA sequencing. Serum cytokine levels were analyzed by Luminex FlexMap3D. RESULTS: Fifty-six pre-pandemic and 38 COVID-negative intra-pandemic subjects were included. There were no significant changes in duodenal microbial alpha diversity in the intra-pandemic vs. pre-pandemic group, but beta diversity was significantly different. The relative abundance (RA) of phylum Deinococcus-Thermus and family Thermaceae, which are resistant extremophiles, was significantly higher in the intra-pandemic vs. pre-pandemic group. The RA of several Gram-negative taxa including Bacteroidaceae (phylum Bacteroidetes) and the Proteobacteria families Enterobacteriaceae and Pseudomonadaceae, and the RA of potential disruptor genera Escherichia-Shigella and Rothia, were significantly lower in the intra-pandemic vs. pre-pandemic group. Circulating levels of interleukin-18 were also lower in the intra-pandemic group. CONCLUSIONS: These findings suggest the small bowel microbiome underwent significant changes during the pandemic, in COVID-19-negative individuals. Given the key roles of the small bowel microbiota in host physiology, this may have implications for human health.

Methanogens and Hydrogen Sulfide Producing Bacteria Guide Distinct Gut Microbe Profiles and Irritable Bowel Syndrome Subtypes.

INTRODUCTION: Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome sequencing to identify potential microbial drivers of IBS subtypes. METHODS: IBS-C and IBS-D subjects from 2 randomized controlled trials (NCT03763175 and NCT04557215) were included. Baseline breath carbon dioxide, hydrogen (H 2 ), methane (CH 4 ), and hydrogen sulfide (H 2 S) levels were measured by gas chromatography, and baseline stool microbiome composition was analyzed by 16S rRNA sequencing. Microbial metabolic pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes collection databases. RESULTS: IBS-C subjects had higher breath CH 4 that correlated with higher gut microbial diversity and higher relative abundance (RA) of stool methanogens, predominantly Methanobrevibacter , as well as higher absolute abundance of Methanobrevibacter smithii in stool. IBS-D subjects had higher breath H 2 that correlated with lower microbial diversity and higher breath H 2 S that correlated with higher RA of H 2 S-producing bacteria, including Fusobacterium and Desulfovibrio spp. The predominant H 2 producers were different in these distinct microtypes, with higher RA of Ruminococcaceae and Christensenellaceae in IBS-C/CH 4 + (which correlated with Methanobacteriaceae RA) and higher Enterobacteriaceae RA in IBS-D. Finally, microbial metabolic pathway analysis revealed enrichment of Kyoto Encyclopedia of Genes and Genomes modules associated with methanogenesis and biosynthesis of methanogenesis cofactor F420 in IBS-C/CH 4 + subjects, whereas modules associated with H 2 S production, including sulfate reduction pathways, were enriched in IBS-D. DISCUSSION: Our findings identify distinct gut microtypes linked to breath gas patterns in IBS-C and IBS-D subjects, driven by methanogens such as M. smithii and H 2 S producers such as Fusobacterium and Desulfovibrio spp, respectively.

A New Framework for Precise Identification of Prostatic Adenocarcinoma.

Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system's performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey.

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

Gene expression cancer classification using modified K-Nearest Neighbors technique.

Gene expression microarray classification is a crucial research field as it has been employed in cancer prediction and diagnosis systems. Gene expression data are composed of dozens of samples characterized by thousands of genes. Hence, an accurate and effective classification of such samples is a challenge. Machine learning techniques have been broadly utilized to build substantial and precise classification models. This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN). MKNN is applied in two scenarios namely; smallest modified KNN (SMKNN) and largest modified KNN (LMKNN). Both implementations are undertaken to enhance the performance of KNN. The key idea is to employ robust neighbors from training data by using a new weighting strategy. Several experiments have been performed on six different gene expression datasets. Experiments have shown that MKNN in its both scenarios outperforms traditional as well as recent ones. MKNN has been compared against (i) KNN, (ii) weighted KNN, (iii) support vector machine (SVM), (iv) fuzzy support vector machine, (v) brain emotional learning (BEL) in terms of classification accuracy, precision, and recall. On the other hand, results show that MKNN introduces smaller testing time than both KNN and weighted KNN.