Dysbiosis in gut microbiota is known to contribute to development of irritable bowel syndrome. We tried to investigate the effect of Bifidobacterium longum on repeated water avoidance stress (WAS) in a Wistar rat model. The three groups (no-stress, WAS, and WAS with B. longum) of rats were allocated to sham or WAS for 1 hour daily for 10 days, and B. longum was administered through gavage for 10 days. Fecal pellet numbers were counted at the end of each 1-hour session of WAS. After 10 days of repeated WAS, the rats were eutanized, and the feces were collected. WAS increased fecal pellet output (FPO) significantly in both sexes (P < 0.001), while the female B. longum group showed significantly decreased FPO (P = 0.005). However, there was no consistent change of myeloperoxidase activity and mRNA expression of interleukin-1ββ and TNF-αα. Mast cell infiltration at colonic submucosa increased in the female WAS group (P = 0.016). In terms of fecal microbiota, the repeated WAS groups in both sexes showed different beta-diversity compared to control and WAS with B. longum groups. WAS-induced mast cell infiltration was reduced by the administration of B. longum in female rats. Moreover, administration of B. longum relieved WAS-caused dysbiosis, especially in female rats. In conclusion, B. longum was beneficial for WAS-induced stress in rats, especially in females.

Background/Aims: We aimed to evaluate the histologic features predictive of prognosis and correlate them with endoscopic findings in patients with ulcerative colitis (UC) having complete or partial mucosal healing (MH). Methods: We prospectively collected and reviewed data from patients with UC who underwent colonoscopy or sigmoidoscopy with biopsy. Complete and partial MH were defined as Mayo endoscopic subscores (MESs) of 0 and 1, respectively. Histologic variables, including the Nancy index (NI), predicting disease progression (defined as the need for medication upgrade or hospitalization/surgery), were evaluated and correlated with endoscopic findings. Results: Overall, 441 biopsy specimens were collected from 194 patients. The average follow-up duration was 14.7 ± 7.4 months. There were 49 (25.3%) and 68 (35.1%) patients with MESs of 0 and 1, respectively. Disease progression occurred only in patients with an MES of 1. NI ≥ 3 was significantly correlated with disease progression during follow-up. Mucosal friability on endoscopy was significantly correlated with NI ≥ 3 (61.1% in NI < 3 vs. 88.0% in NI ≥ 3; p = 0.013). Conclusions Histological activity can help predict the prognosis of patients with UC with mild endoscopic activity. Mucosal friability observed on endoscopy may reflect a more severe histological status, which can be a risk factor for disease progression.

Roseburia faecis, a butyrate-producing, gram-positive anaerobic bacterium, was evaluated for its usefulness against repeated water avoidance stress (WAS)-induced irritable bowel syndrome (IBS) in a rat model, and the underlying mechanism was explored.We divided the subjects into three groups: one without stress exposure, another subjected to daily 1-hour WAS for 10 days, and a third exposed to the same WAS regimen while also receiving two different R. faecis strains (BBH024 or R22-12-24) via oral gavage for the same 10-day duration. Fecal pellet output (FPO), a toluidine blue assay for mast cell infiltration, and fecal microbiota analyses were conducted using 16S rRNA metagenomic sequencing. Predictive functional profiling of microbial communities in metabolism was also conducted. FPO and colonic mucosal mast cell counts were significantly higher in the WAS group than in the control group (male, P = 0.004; female, P = 0.027). The administration of both BBH024 (male, P = 0.015; female, P = 0.022) and R22-12-24 (male, P = 0.003; female, P = 0.040) significantly reduced FPO. Submucosal mast cell infiltration in the colon showed a similar pattern in males. In case of fecal microbiota, the WAS with R. faecis group showed increased abundance of the Roseburia genus compared to WAS alone. Moreover, the expression of a gene encoding a D-methionine transport system substrate-binding protein was significantly elevated in the WAS with R. faecis group compared to that in the WAS (male, P = 0.028; female, P = 0.025) group. These results indicate that R. faecis is a useful probiotic for treating IBS and colonic microinflammation.

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18 F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion Radiomic models using MRI were able to differentiate JME from HCs.

Epidermal cell adhesion molecule (EpCAM) is a tumor-associated antigen (TAA), which has been considered as a cancer vaccine candidate. The EpCAM protein fused to the fragment crystallizable region of immunoglobulin G (IgG) tagged with KDEL endoplasmic reticulum (ER) retention signal (EpCAM-FcK) has been successfully expressed in transgenic tobacco (Nicotiana tabacum cv. Xanthi) and purified from the plant leaf. In this study, we investigated the ability of the plant-derived EpCAM-FcK (EpCAM-FcKP ) to elicit an immune response in vivo. The animal group injected with the EpCAM-FcKP showed a higher differentiated germinal center (GC) B cell population (~9%) compared with the animal group injected with the recombinant rhEpCAM-Fc chimera (EpCAM-FcM ). The animal group injected with EpCAM-FcKP (~42%) had more differentiated T follicular helper cells (Tfh) than the animal group injected with EpCAM-FcM (~7%). This study demonstrated that the plant-derived EpCAM-FcK fusion antigenic protein induced a humoral immune response in mice.

Background/Aims: We aim to evaluate the differences in the microbiome of responders and non-responders, as well as predict the response to probiotic therapy, based on fecal microbiome data in patients with diarrhea-predominant irritable bowel syndrome (IBS-D). Methods: A multi-strain probiotics that contains Lactobacillus acidophilus (KCTC 11906BP), Lactobacillus plantarum (KCTC11867BP), Lactobacillus rhamnosus (KCTC 11868BP), Bifidobacterium breve (KCTC 11858BP), Bifidobacterium lactis (KCTC 11903BP), Bifidobacterium longum (KCTC 11860BP), and Streptococcus thermophilus (KCTC 11870BP) were used. Patients were categorized into probiotic and placebo groups, and fecal samples were collected from all patients before and at the end of 8 weeks of treatment. The probiotic group was further divided into responders and non-responders. Responders were defined as patients who experienced adequate relief of overall irritable bowel syndrome symptoms after probiotic therapy. Fecal microbiota were investigated using Illumina MiSeq and analyzed using the EzBioCloud 16S database and microbiome pipeline (https://www.EZbiocloud.net). Results: There was no significant difference in the alpha and beta diversity between the responder and non-responder groups. The abundances of the phylum Proteobacteria and genus Bacteroides significantly decreased after probiotic treatment. Bifidobacterium bifidum, Pediococcus acidilactici, and Enterococcus faecium showed a significantly higher abundance in the probiotic group after treatment compared to the placebo group. Enterococcus faecalis and Lactococcus lactis were identified as biomarkers of non-response to probiotics. The abundance of Fusicatenibacter saccharivorans significantly increased in the responders after treatment. Conclusions Probiotic treatment changes some composition of fecal bacteria in patients with IBS-D. E. faecalis and L. lactis may be prediction biomarkers for non-response to probiotics. Increased abundance of F. sccharivorans is correlated to symptom improvement by probiotics in patients with IBS-D.

Objective: The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources. The aim of this study is to predict the risk of in-hospital mortality associated with AP using an artificial neural network (ANN) model. Methods: In this multicenter retrospective study, ANN and logistic regression models were constructed using the clinical and laboratory data of 1,304 patients seeking emergency treatment for AP. The ANN model was first trained on 912/1,304 (70%) randomly selected patients and then tested on the remaining 392/1,304 (30%). Receiver operating characteristic curve analysis was used to evaluate the mortality prediction of the two models. Results: Age, endotracheal intubation status, and intensive care unit admission were significant predictors of mortality in patients with AP in the multivariate logistic regression model. The ANN model indicated age, Glasgow Coma Scale, intensive care unit admission, and endotracheal intubation status were critical factors among the 12 independent variables related to in-hospital mortality. The area under the receiver operating characteristic curve for mortality prediction was significantly higher in the ANN model compared to the logistic regression model. Conclusion This study establishes that the ANN model could be a valuable tool for predicting the risk of death following AP. Thus, it may facilitate effective patient triage and improve the outcomes.