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Recently, it has been reported that Fusobacterium nucleatum, a major pathogen involved in chronic periodontitis, may play an important role in colorectal cancer (CRC) progression. In addition, inflammatory bowel diseases such as ulcerative colitis and Crohn’s disease represent major predisposing conditions for the development of CRC, and this subtype of cancer is called colitis-associated cancer (CAC). Although the importance of F. nucleatum in CRC has attracted attention, its exact role and related mechanism in CAC progression remain unclear. In this study, we investigated the effects of F. nucleatum in experimental colitis induced with dextran sodium sulfate (DSS), which is a well-known colitis-inducing chemical, on the aggressiveness of CAC and its related mechanism in both in vitro and in vivo models. F. nucleatum synergistically increased the aggressiveness and epithelial–mesenchymal transition (EMT) characteristics of CRC cells that were treated with DSS compared to those in non-treated CRC cells. The role of F. nucleatum in CAC progression was further confirmed in mouse models, as F. nucleatum was found to significantly increase the malignancy of azoxymethane (AOM)/DSS-induced colon cancer. This promoting effect of F. nucleatum was based on activation of the EGFR signaling pathways, including protein kinase B (AKT) and extracellular signal-regulated kinase (ERK), and epidermal growth factor receptor (EGFR) inhibition significantly reduced the F. nucleatum-induced EMT alteration. In conclusion, F. nucleatum accelerates the progression of CAC by promoting EMT through the EGFR signaling pathway.

Gut microbiota, which plays a crucial role in inflammatory bowel diseases (IBD), might have therapeutic benefits for ulcerative colitis or Crohn's disease. Targeting gut microbiota represents a new treatment strategy for IBD patients. Rhein is one of the main components of rhubarb and exhibits poor oral bioavailability but still exerts anti-inflammatory effects in some diseases. Therefore, we investigated the effect of rhein on colitis and studied its possible mechanisms. The chronic mouse colitis model was induced by four rounds of 2% dextran sulfate sodium (DSS) treatment. The mice were treated with 50 mg/kg and 100 mg/kg rhein daily, body weight, colon length, histological score, inflammatory cytokines in serum or intestine, and fecal lipocalin 2 concentration were determined. Th17 cell, Th1 cell and Th2 cell infiltration in the mesenteric lymph node were analyzed by flow cytometry. Metabolic profiles were collected by non-targeted metabolomics and key metabolic pathways were identified using MetaboAnalyst 4.0. We also assessed intestinal barrier permeability and performed 16s rDNA sequencing. was cultured, and fecal microbiota transplantation (FMT) was employed to evaluate the contribution of gut microbiota. Rhein could significantly alleviate DSS-induced chronic colitis. Uric acid was identified as a crucial modulator of colitis and rhein treatment led to decreased uric acid levels. We determined that rhein changed purine metabolism indirectly, while the probiotic was involved in the regulation of host metabolism. Uric acid resulted in a worsened intestinal barrier, which could be rescued by rhein. We further confirmed that rhein-treated gut microbiota was sufficient to relieve DSS-induced colitis by FMT. We showed that rhein could modulate gut microbiota, which indirectly changed purine metabolism in the intestine and subsequently alleviated colitis. Our study has identified a new approach to the clinical treatment of colitis.

Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system’s predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.

Aberrant neutrophil extracellular trap (NET) formation and the loss of barrier integrity in inflamed intestinal tissues have long been associated with inflammatory bowel disease (IBD). However, whether NETs alter intestinal epithelium permeability during colitis remains elusive. Here, we demonstrated that NETs promote the breakdown in intestinal barrier function for the pathogenesis of intestinal inflammation in mouse models of colitis. NETs were abundant in the colon of mice with colitis experimentally induced by dextran sulfate sodium (DSS) or 2,4,6-trinitrobenzene sulfonic acid (TNBS). Analysis of the intestinal barrier integrity revealed that NETs impaired gut permeability, enabling the initiation of luminal bacterial translocation and inflammation. Furthermore, NETs induced the apoptosis of epithelial cells and disrupted the integrity of tight junctions and adherens junctions. Intravenous administration of DNase I, an enzyme that dissolves the web-like DNA filaments of NETs, during colitis restored the mucosal barrier integrity which reduced the dissemination of luminal bacteria and attenuated intestinal inflammation in both DSS and TNBS models. We conclude that NETs serve a detrimental factor in the gut epithelial barrier function leading to the pathogenesis of mucosal inflammation during acute colitis.

Chronic stress is known to promote inflammatory bowel disease (IBD), but the underlying mechanism remains largely unresolved. Here, we found chronic stress to sensitize mice to dextran sulfate sodium (DSS)-induced colitis; to increase the infiltration of B cells, neutrophils, and proinflammatory ly6Chi macrophages in colonic lamina propria; and to present with decreased thymus and mesenteric lymph node (MLN) coefficients. Circulating total white blood cells were significantly increased after stress, and the proportion of MLN-associated immune cells were largely changed. Results showed a marked activation of IL-6/STAT3 signaling by stress. The detrimental action of stress was not terminated in IL-6−/− mice. Interestingly, the composition of gut microbiota was dramatically changed after stress, with expansion of inflammation-promoting bacteria. Furthermore, results showed stress-induced deficient expression of mucin-2 and lysozyme, which may contribute to the disorder of gut microbiota. Of note is that, in the case of cohousing, the stress-induced immune reaction and decreased body weight were abrogated, and transferred gut microbiota from stressed mice to control mice was sufficient to facilitate DSS-induced colitis. The important role of gut microbiota was further reinforced by broad-spectrum antibiotic treatment. Taken together, our results reveal that chronic stress disturbs gut microbiota, triggering immune system response and facilitating DSS-induced colitis.

Goblet cells (GCs) are specialized cells of the intestinal epithelium contributing critically to mucosal homeostasis. One of the functions of GCs is to produce and secrete MUC2, the mucin that forms the scaffold of the intestinal mucus layer coating the epithelium and separates the luminal pathogens and commensal microbiota from the host tissues. Although a variety of ion channels and transporters are thought to impact on MUC2 secretion, the specific cellular mechanisms that regulate GC function remain incompletely understood. Previously, we demonstrated that leucine-rich repeat-containing protein 26 (LRRC26), a known regulatory subunit of the Ca2+-and voltage-activated K⁺ channel (BK channel), localizes specifically to secretory cells within the intestinal tract. Here, utilizing a mouse model in which MUC2 is fluorescently tagged, thereby allowing visualization of single GCs in intact colonic crypts, we show that murine colonic GCs have functional LRRC26-associated BK channels. In the absence of LRRC26, BK channels are present in GCs, but are not activated at physiological conditions. In contrast, all tested MUC2⁻ cells completely lacked BK channels. Moreover, LRRC26-associated BK channels underlie the BK channel contribution to the resting transepithelial current across mouse distal colonic mucosa. Genetic ablation of either LRRC26 or BK pore-forming α-subunit in mice results in a dramatically enhanced susceptibility to colitis induced by dextran sodium sulfate. These results demonstrate that normal potassium flux through LRRC26-associated BK channels in GCs has protective effects against colitis in mice.

There are significant sex differences in colorectal cancer (CRC), including in incidence, onset, and molecular characteristics. Further, while inflammatory bowel disease (IBD) is a risk factor for CRC in both sexes, men with IBD have a 60% higher risk of developing CRC compared to women. In this study, we investigated sex differences during colitis-associated CRC (CAC) using a chemically induced CAC mouse model. The mice were treated with azoxymethane (AOM) and dextran sodium sulfate (DSS) and followed for 9 and 15 weeks. We performed RNA-sequencing of colon samples from males (n = 15) and females (n = 15) to study different stages of inflammation and identify corresponding transcriptomic sex differences in non-tumor colon tissue. We found a significant transcriptome response to AOM/DSS treatment in both sexes, including in pathways related to inflammation and cell proliferation. Notably, we found a stronger response in males and that male-specific differentially expressed genes were involved in NFκB signaling and circadian rhythm. Further, an overrepresented proportion of male-specific gene regulations were predicted to be targets of Stat3, whereas for females, targets of the glucocorticoid receptor (Gr/Nr3c1) were overrepresented. At 15 weeks, the most apparent sex difference involved genes with functions in T cell proliferation, followed by the regulation of demethylases. The majority of sex differences were thus related to inflammation and the immune system. Our novel data, profiling the transcriptomic response to chemically induced colitis and CAC, indicate clear sex differences in CRC initiation and progression.

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

Ulcerative colitis (UC) is a gastrointestinal disease. The link between gut microbiota and the inflammatory response in the gut has been recently established. Restoration of gut microbiota suppresses inflammatory signaling. Kuijieling (KJL) decoction, an experimental Chinese medicine formula could ameliorate the symptom of colitis. However, the involvement of gut microbiota in its curative effect remains known. Here, we would like to assess the therapeutic effect of KJL in DSS-induced UC model. Mouse feces were collected, followed by 16S rRNA sequencing. Kuijieling decoction improved gut microbial homeostasis and suppressed inflammation in the UC model. A 5-fold cross-validation and random forest analysis identified seven signature bacterial taxa representing the DSS-mediated pathogenic condition and recovery stage upon KJL decoction treatment. Overall, the findings support the notion of KJL decoction-mediated restoration of gut microbiota as a critical step of inducing remission and alleviating UC symptoms. In the present investigation, we aimed to address the question of whether KJL decoction alleviates the UC symptoms by manipulating the gut microbial structure and function.

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data. •Preprocessing is essential for EEG and MEG data analysis.•Robust methods for data preprocessing are not affected by glitches and artifacts.•Methods include robust detrending, rereferencing, inpainting and step removal.•These methods are effective and complementary with standard techniques such as ICA.