Anti-HBV drug entecavir ameliorates DSS-induced colitis through PD-L1 induction

Oral Spore-Based Probiotic Supplementation Alters Post-Prandial Expression of mRNA Associated with Gastrointestinal Health

Background/Objectives: Unregulated post-prandial dietary endotoxemia may accumulate over time and underlie the development of chronic disease (e.g., leaky gut, inflammatory bowel disease, etc.), for which oral probiotic supplementation may be a prophylactic. The purpose of this study was to determine if 45 d of oral spore-based probiotic supplementation altered gastrointestinal-associated mRNA expression following a high-fat meal. Methods: A subset of apparently healthy individuals from a larger study who had dietary endotoxemia at baseline completed 45 d of supplementation with either a placebo (rice flour; n = 10) or spore-based probiotic (Megasporebiotic™; Novonesis, Kongens Lyngby, Denmark; Bacillus indicus (HU36™), Bacillus subtilis (HU58™), Bacillus coagulans (SC208™), and Bacillus licheniformis (SL-307), and Bacillus clausii (SC109™); n = 10). Venous blood was collected in Paxgene RNA tubes prior to (PRE), 3 h, and 5 h after consumption of a high-fat meal (85% of the daily fat RDA and 65% of the daily calorie needs). Total RNA was analyzed for 579 mRNAs of interest (Nanostring nCounter Sprint; Seattle, WA, USA). After normalization to housekeeping controls and calculation of differential expression relative to PRE and controlled for FDR, 15 mRNAs were determined to be significantly changed at either 3 h and/or 5 h post-prandial in the probiotic group but not in the placebo group. Results: Significant mRNA expressions were associated with gastrointestinal tract barrier function (four mRNAs: BATF3, CCR6, CXCR6, and PDCD2), gastrointestinal immunity (four mRNAs: CLEC5A, IL7, CARD9, and FCER1G), or future IBD risk (seven mRNAs: PD-L1, CSF1R, FAS, BID, FADD, GATA3, and KIR3DL). Conclusions: Collectively, the present findings may support the notion that post-prandial immune response to eating is enhanced following 45 d of probiotic supplementation.

Artificial intelligence and classification of mature lymphoid neoplasms

Hematologists, geneticists, and clinicians came to a multidisciplinary agreement on the classification of lymphoid neoplasms that combines clinical features, histological characteristics, immunophenotype, and molecular pathology analyses. The current classification includes the World Health Organization (WHO) Classification of tumours of haematopoietic and lymphoid tissues revised 4th edition, the International Consensus Classification (ICC) of mature lymphoid neoplasms (report from the Clinical Advisory Committee 2022), and the 5th edition of the proposed WHO Classification of haematolymphoid tumours (lymphoid neoplasms, WHO-HAEM5). This article revises the recent advances in the classification of mature lymphoid neoplasms. Artificial intelligence (AI) has advanced rapidly recently, and its role in medicine is becoming more important as AI integrates computer science and datasets to make predictions or classifications based on complex input data. Summarizing previous research, it is described how several machine learning and neural networks can predict the prognosis of the patients, and classified mature B-cell neoplasms. In addition, new analysis predicted lymphoma subtypes using cell-of-origin markers that hematopathologists use in the clinical routine, including CD3, CD5, CD19, CD79A, MS4A1 (CD20), MME (CD10), BCL6, IRF4 (MUM-1), BCL2, SOX11, MNDA, and FCRL4 (IRTA1). In conclusion, although most categories are similar in both classifications, there are also conceptual differences and differences in the diagnostic criteria for some diseases. It is expected that AI will be incorporated into the lymphoma classification as another bioinformatics tool.

Effect of Extracelluar Vesicles Derived from Akkermansia muciniphila on Intestinal Barrier in Colitis Mice

Inflammatory bowel disease (IBD) is a chronic and recurrent disease. It has been observed that the incidence and prevalence of IBD are increasing, which consequently raises the risk of developing colon cancer. Recently, the regulation of the intestinal barrier by probiotics has become an effective treatment for colitis. Akkermansia muciniphila-derived extracellular vesicles (Akk EVs) are nano-vesicles that contain multiple bioactive macromolecules with the potential to modulate the intestinal barrier. In this study, we used ultrafiltration in conjunction with high-speed centrifugation to extract Akk EVs. A lipopolysaccharide (LPS)-induced RAW264.7 cell model was established to assess the anti-inflammatory effects of Akk EVs. It was found that Akk EVs were able to be absorbed by RAW264.7 cells and significantly reduce the expression of nitric oxide (NO), TNF-α, and IL-1β (p < 0.05). We explored the preventative effects on colitis and the regulating effects on the intestinal barrier using a mouse colitis model caused by dextran sulfate sodium (DSS). The findings demonstrated that Akk EVs effectively prevented colitis symptoms and reduced colonic tissue injury. Additionally, Akk EVs significantly enhanced the effectiveness of the intestinal barrier by elevating the expression of MUC2 (0.53 ± 0.07), improving mucus integrity, and reducing intestinal permeability (p < 0.05). Moreover, Akk EVs increased the proportion of the beneficial bacteria Firmicutes (33.01 ± 0.09%) and downregulated the proportion of the harmful bacteria Proteobacteria (0.32 ± 0.27%). These findings suggest that Akk EVs possess the ability to regulate immune responses, protect intestinal barriers, and modulate the gut microbiota. The research presents a potential intervention approach for Akk EVs to prevent colitis.

The pathobiology of follicular lymphoma

Follicular lymphoma is one of the most frequent lymphomas. Histologically, it is characterized by a follicular (nodular) growth pattern of centrocytes and centroblasts; mixed with variable immune microenvironment cells. Clinically, it is characterized by diffuse lymphadenopathy, bone marrow involvement, and splenomegaly. It is biologically and clinically heterogeneous. In most patients it is indolent, but others have a more aggressive evolution with relapses; and transformation to diffuse large B-cell lymphoma. Tumorigenesis includes an asymptomatic preclinical phase in which premalignant B-lymphocytes with the t(14;18) chromosomal translocation acquire additional genetic alterations in the germinal centers, and clonal evolution occurs, although not all the cells progress to the tumor stage. This manuscript reviews the pathobiology and clinicopathological characteristics of follicular lymphoma. It includes a description of the physiology of the germinal center, the genetic alterations of BCL2 and BCL6, the mutational profile, the immune checkpoint, precision medicine, and highlights in the lymphoma classification. In addition, a comment and review on artificial intelligence and machine (deep) learning are made.

A budding concept with certain microbiota, anti-proliferative family proteins, and engram theory for the innovative treatment of colon cancer

Inflammatory bowel disease (IBD) is a multifactorial chronic disease. Patients with IBD have an increased risk of developing colorectal cancer which has become a major health concern. IBD might exert a role of engrams for making the condition of specific inflammation in the gut. Dysregulation of immune cells induced by the command of engrams might be crucial in the pathogenesis of damages in gut epithelium. The anti-proliferative (APRO) family of anti-proliferative proteins characterized by immediate early responsive gene-products that might be involved in the machinery of the carcinogenesis in IBD. Herein, it is suggested that some probiotics with specific bacteria could prevent the development and/or progression of the IBD related tumors. In addition, consideration regarding the application of studying APRO family proteins for the comprehension of IBD related tumors has been presented. It is hypothesized that overexpression of Tob1, a member of APRO family proteins, in the epithelium of IBD could suppress the function of adjacent cytotoxic immune cells possibly via the paracrine signaling.

Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel

Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95–100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (PD-L1, CD274, PDCD1LG1, B7-H1) was validated in a series from the Tokai University Hospital by immunohistochemistry. In conclusion, artificial intelligence analysis of transcriptomic data of ulcerative colitis is a feasible analytical strategy.