Methionine-choline deficient diet deteriorates DSS-induced murine colitis through disturbance of gut microbes and infiltration of macrophages.

Ulcerative colitis (UC) is associated with changed dietary habits and mainly linked with the gut microbiota dysbiosis, necroptosis of epithelial cells, and mucosal ulcerations. Liver dysfunction and abnormal level of liver metabolism indices were identified in UC patients, suggesting a close interaction between gut and liver disorders. Methionine-choline deficient diet (MCD) has been shown to induce persistent alterations of gut microbiota and metabolome during hepatitis. In this study we further explored the disease phenotypes in UC patients and investigated whether MCD functioned as a trigger for UC susceptibility. After assessing 88 serum specimens from UC patients, we found significant liver dysfunction and dyslipidemia including abnormal ALT, AST, TG, TC, LDL-c and HDL-c. Liver dysfunction and dyslipidemia were confirmed in DSS-induced colitis mice. We fed mice with MCD for 14 days to cause mild liver damage, and then treated with DSS for 7 days. We found that MCD intake significantly exacerbated the pathogenesis of mucosal inflammation in DSS-induced acute, progressive, and chronic colitis, referring to promotion of mucosal ulcers, colon shortening, diarrhea, inflammatory immune cell infiltration, cytokines release, and abnormal activation of inflammatory macrophages in colon and liver specimens. Intraperitoneal injection of clodronate liposomes to globally delete macrophages dramatically compromised the pathogenesis of MCD-triggering colitis. In addition, MCD intake markedly changed the production pattern of short-chain fatty acids (SCFAs) in murine stools, colons, and livers. We demonstrated that MCD-induced colitis pathogenesis largely depended on the gut microbes and the disease phenotypes could be transmissible through fecal microbiota transplantation (FMT). In conclusion, this study supports the concept that intake of MCD predisposes to experimental colitis and enhances its pathogenesis via modulating gut microbes and macrophages in mice.

The causal relationship between gut microbiota and biliary tract cancer: comprehensive bidirectional Mendelian randomization analysis.

Background: Growing evidence has shown that gut microbiome composition is associated with Biliary tract cancer (BTC), but the causality remains unknown. This study aimed to explore the causal relationship between gut microbiota and BTC, conduct an appraisal of the gut microbiome's utility in facilitating the early diagnosis of BTC. Methods: We acquired the summary data for Genome-wide Association Studies (GWAS) pertaining to BTC (418 cases and 159,201 controls) from the Biobank Japan (BBJ) database. Additionally, the GWAS summary data relevant to gut microbiota (N = 18,340) were sourced from the MiBioGen consortium. The primary methodology employed for the analysis consisted of Inverse Variance Weighting (IVW). Evaluations for sensitivity were carried out through the utilization of multiple statistical techniques, encompassing Cochrane's Q test, the MR-Egger intercept evaluation, the global test of MR-PRESSO, and a leave-one-out methodological analysis. Ultimately, a reverse Mendelian Randomization analysis was conducted to assess the potential for reciprocal causality. Results: The outcomes derived from IVW substantiated that the presence of Family Streptococcaceae (OR = 0.44, P = 0.034), Family Veillonellaceae (OR = 0.46, P = 0.018), and Genus Dorea (OR = 0.29, P = 0.041) exerted a protective influence against BTC. Conversely, Class Lentisphaeria (OR = 2.21, P = 0.017), Genus Lachnospiraceae FCS020 Group (OR = 2.30, P = 0.013), and Order Victivallales (OR = 2.21, P = 0.017) were associated with an adverse impact. To assess any reverse causal effect, we used BTC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between BTC and five different types of gut microbiota. The sensitivity analysis disclosed an absence of empirical indicators for either heterogeneity or pleiotropy. Conclusion: This investigation represents the inaugural identification of indicative data supporting either beneficial or detrimental causal relationships between gut microbiota and the risk of BTC, as determined through the utilization of MR methodologies. These outcomes could hold significance for the formulation of individualized therapeutic strategies aimed at BTC prevention and survival enhancement.

Unsupervised domain adaptation for histopathology image segmentation with incomplete labels.

Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with incompletely labeled source data. This paper propose a Stain-Adaptive Segmentation Network with Incomplete Labels (SASN-IL). Specifically, the algorithm consists of two stages. The first stage is an incomplete label correction stage, involving reliable model selection and label correction to rectify false-negative regions in incomplete labels. The second stage is the unsupervised domain adaptation stage, achieving segmentation on the target domain. In this stage, we introduce an adaptive stain transformation module, which adjusts the degree of transformation based on segmentation performance. We evaluate our method on a gastric cancer dataset, demonstrating significant improvements, with a 10.01% increase in Dice coefficient compared to the baseline and competitive performance relative to existing methods.

CrossU-Net: Dual-modality cross-attention U-Net for segmentation of precancerous lesions in gastric cancer.

Gastric precancerous lesions (GPL) significantly elevate the risk of gastric cancer, and precise diagnosis and timely intervention are critical for patient survival. Due to the elusive pathological features of precancerous lesions, the early detection rate is less than 10%, which hinders lesion localization and diagnosis. In this paper, we provide a GPL pathological dataset and propose a novel method for improving the segmentation accuracy on a limited-scale dataset, namely RGB and Hyperspectral dual-modal pathological image Cross-attention U-Net (CrossU-Net). Specifically, we present a self-supervised pre-training model for hyperspectral images to serve downstream segmentation tasks. Secondly, we design a dual-stream U-Net-based network to extract features from different modal images. To promote information exchange between spatial information in RGB images and spectral information in hyperspectral images, we customize the cross-attention mechanism between the two networks. Furthermore, we use an intermediate agent in this mechanism to improve computational efficiency. Finally, we add a distillation loss to align predicted results for both branches, improving network generalization. Experimental results show that our CrossU-Net achieves accuracy and Dice of 96.53% and 91.62%, respectively, for GPL lesion segmentation, providing a promising spectral research approach for the localization and subsequent quantitative analysis of pathological features in early diagnosis.

Revisiting the Role of Valeric Acid in Manipulating Ulcerative Colitis.

BACKGROUND: Ulcerative colitis (UC) is characterized by a complicated interaction between mucosal inflammation, epithelial dysfunction, abnormal activation of innate immune responses, and gut microbiota dysbiosis. Though valeric acid (VA), one type of short-chain fatty acids (SCFAs), has been identified in other inflammatory disorders and cancer development, the pathological role of VA and underlying mechanism of VA in UC remain under further investigation. METHODS: Studies of human clinical specimens and experimental colitis models were conducted to confirm the pathological manifestations of the level of SCFAs from human fecal samples and murine colonic homogenates. Valeric acid-intervened murine colitis and a macrophage adoptive transfer were applied to identify the underlying mechanisms. RESULTS: In line with gut microbiota dysfunction in UC, alteration of SCFAs from gut microbes were identified in human UC patients and dextran sodium sulfate -induced murine colitis models. Notably, VA was consistently negatively related to the disease severity of UC, the population of monocytes, and the level of interluekin-6. Moreover, VA treatment showed direct suppressive effects on lipopolysaccharides (LPS)-activated human peripheral blood mononuclear cells and murine macrophages in the dependent manner of upregulation of GPR41 and GPR43. Therapeutically, replenishment of VA or adoptive transfer with VA-modulated macrophages showed resistance to dextran sodium sulfate-driven murine colitis though modulating the production of inflammatory cytokine interleukin-6. CONCLUSIONS: In summary, the research uncovered the pathological role of VA in modulating the activation of macrophages in UC and suggested that VA might be a potential effective agent for UC patients.

The study collectively indicated that valeric acid (VA) was consistently negatively related to the disease severity of UC, and hypofunction of macrophage driven by VA impeded the progression of UC.

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges.

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS: HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS: A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS: An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

A combined radiomic model distinguishing GISTs from leiomyomas and schwannomas in the stomach based on endoscopic ultrasonography images.

BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign. PURPOSE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach. METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions. RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively. CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.

Autoimmune pancreatitis: A bibliometric analysis from 2002 to 2022.

Background/Objectives: Autoimmune pancreatitis (AIP) is a distinct form of pancreatic inflammatory disease that responds well to glucocorticoid therapy. Knowledge on AIP has rapidly evolved over the past two decades. Based on bibliometric analysis, this study aimed to assess the research status of AIP over the past two decades and determine the research focus and emerging topics. Methods: AIP-related publications published between January 1, 2002, and June 6, 2022, were retrieved from the Web of Science Core Collection. Bibliometric data were analyzed using HisCite, VOSviewer, CiteSpace, and bibliometrix package. Annual output, leading countries/regions, active institutions and authors, core journals and references, and keywords of AIP were evaluated. Results: Overall, 1,772 publications were retrieved from 501 journals by 6,767 authors from 63 countries/regions. Japan published articles on AIP the most (n=728, 41.1%), followed by the United States (n=336, 19%), Germany (n=147, 8.3%), China (n=127, 7%), and Italy (n=107, 6%). The top three most prolific authors were Terumi Kamisawa from Tokyo Metropolitan Komagome Hospital (n=117), Kazuichi Okazaki from Kansai Medical University (n=103), and Shigeyuki Kawa from Matsumoto Dental University (n=94). Pancreas was the most productive journal regarding AIP research (n=95), followed by the Journal of Gastroenterology (n=67), Internal Medicine (n=66), Pancreatology (n=63), and World Journal of Gastroenterology (n=62). "Diagnosis" was the most mentioned keyword. "Risk," "malignancy," "outcome," "22-gauge needle," and "fine-needle aspiration" were recognized as emerging topics. Conclusion: Japan was the leading country in AIP research. Research papers were mainly published in specialized journals. Diagnosis was the research focus. Long-term outcomes and pancreatic tissue acquisition were recognized as research frontiers for AIP.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.

BACKGROUND: Small intestinal vascular malformations (angiodysplasias) are common causes of small intestinal bleeding. While capsule endoscopy has become the primary diagnostic method for angiodysplasia, manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload, which affects the accuracy of diagnosis. AIM: To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time. METHODS: A convolutional neural network semantic segmentation model with a feature fusion method, which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed. Resnet-50 was used as the skeleton network to design the fusion mechanism, fuse the shallow and depth features, and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia. The training set and test set were constructed and compared with PSPNet, Deeplab3+, and UperNet. RESULTS: The test set constructed in the study achieved satisfactory results, where pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time length to segment and recognize a picture was 0.6 s. CONCLUSION: Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.

A model for predicting degree of malignancy in patients with intraductal papillary mucinous neoplasm.

Background/Objectives: There is no predictive model available to address early stage malignant intraductal papillary mucinous neoplasm (IPMN) including high grade dysplasia (HGD) and pT1a (invasive component≤0.5 cm). The aim of this study was to establish an objective and sufficient model to predict the degree of malignancy in patients with IPMN, which can be easily applied in daily practice and adopted for any type of lesion. Methods: A retrospective cohort study of 309 patients who underwent surgical resection for IPMN was performed. Members of the cohort were randomly allocated to the training or testing set. A detection tree model and random forest model were used for a 3-class classification to distinguish low grade dysplasia (LGD), HGD/pT1a IPMN, and invasive intraductal papillary mucinous cancer (I-IPMC) beyond pT1a. Results: Of the 309 patients, 54 (17.4%) had early stage malignancy (19 HGD, 35 pT1a), 49 (15.9%) had I-IPMC beyond pT1a, and 206 (66.7%) had LGD IPMN. We proposed a 3-class classification model using a random forest algorithm, and the model had an accuracy of 99.5% with the training set, and displayed an accuracy of 96.0% with the testing set. We used SHAP for interpretation of the model and showed the top five factors (mural nodule size, main pancreatic duct diameter, CA19-9 levels, lesion edge and common bile duct dilation) were most likely to influence the 3-class classification results in terms of interpretation of the random forest model. Conclusions: This predictive model will help assess an individual's risk for different stages of IPMN malignancy and may help identify patients with IPMN who require surgery.

Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis.

Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.

Therapeutic strategies following endoscopic submucosal dissection for T1a-MM/T1b-SM1 esophageal squamous cell carcinoma: comparisons of long-term outcomes with propensity score-matched analysis.

BACKGROUND: For esophageal squamous cell carcinoma (ESCC) invading the muscularis mucosa (T1a-MM) or upper submucosa (T1b-SM1, up to 200 µm), the curative effectiveness of endoscopic submucosal dissection (ESD) and additional therapeutic strategies remain controversial. The present study aims to investigate the effectiveness of ESD followed by different therapeutic strategies in treating such patients. METHODS: A total of 242 patients with T1a-MM/T1b-SM1 ESCCs were involved. Data on therapeutic outcomes and long-term survivals were collected for analysis. Propensity score-matched analysis was performed to compensate for selection bias between patients with no additional therapy (NAT group) and those with additional therapy (AT group). RESULTS: R0 resection rate was 83.1% and curative resection rate was 78.5%. After a mean follow-up period of 57.8 ± 27.3 months, the cumulative recurrence rate was 7.9%. The 5-year overall survival (OS) and cause-specific survival (CSS) rate was 91.1% and 94.0%. In a matched cohort of 24 pairs, the 5-year OS and CSS rates showed no significant difference between NAT group (82.0% and 87.1%) and AT group (86.0% and 89.9%) (P > 0.05). In the subgroup of patients with noncurative ESD (n = 52), the 5-year OS and CSS rates were significantly higher in surgery group (90.2% and 95.2%) than that in NAT group (50.1% and 59.5%) and chemoradiotherapy group (51.4% and 60.0%) (P < 0.05). CONCLUSIONS: ESD with no additional therapy could achieve favorable long-term outcomes in treating T1a-MM/T1b-SM1 ESCCs. For patients with noncurative ESD, surgery ranks a prime recommendation over CRT.

A computer-aided system improves the performance of endoscopists in detecting colorectal polyps: a multi-center, randomized controlled trial.

Background: Up to 45.9% of polyps are missed during colonoscopy, which is the major cause of post-colonoscopy colorectal cancer (CRC). Computer-aided detection (CADe) techniques based on deep learning might improve endoscopists' performance in detecting polyps. We aimed to evaluate the effectiveness of the CADe system in assisting endoscopists in a real-world clinical setting. Methods: The CADe system was trained to detect colorectal polyps, recognize the ileocecal region, and monitor the speed of withdrawal during colonoscopy in real-time. Between 17 January 2021 and 16 July 2021. We recruited consecutive patients aged 18-75 years from three centers in China. We randomized patients in 1:1 groups to either colonoscopy with the CADe system or unassisted (control). The primary outcomes were the sensitivity and specificity of the endoscopists. We used subgroup analysis to examine the polyp detection rate (PDR) and the miss detection rate of endoscopists. Results: A total of 1293 patients were included. The sensitivity of the endoscopists in the experimental group was significantly higher than that of the control group (84.97 vs. 72.07%, p < 0.001), and the specificity of the endoscopists in these two groups was comparable (100.00 vs. 100.00%). In a subgroup analysis, the CADe system improved the PDR of the 6-9 mm polyps (18.04 vs. 13.85%, p < 0.05) and reduced the miss detection rate, especially at 10:00-12:00 am (12.5 vs. 39.81%, p < 0.001). Conclusion: The CADe system can potentially improve the sensitivity of endoscopists in detecting polyps, reduce the missed detection of polyps in colonoscopy, and reduce the risk of CRC. Registration: This clinical trial was registered with the Chinese Clinical Trial Registry (Trial Registration Number: ChiCTR2100041988). Clinical trial registration: website www.chictr.org.cn, identifier ChiCTR2100041988.

Pseudoileus caused by primary visceral myopathy in a Han Chinese patient with a rare MYH11 mutation: A case report.

BACKGROUND: Chronic intestinal pseudo-obstruction (CIPO) is a syndrome of intestinal motor dysfunction caused by intestinal nerve, muscle, and/or Cajal stromal cell lesions. CIPO is a serious category of gastrointestinal dynamic dysfunction, which can eventually lead to the death of patients with intestinal failure. Due to considerable phenotypic heterogeneity, the estimated incidence of CIPO is 1/476190 and 1/416666 in men and women, respectively. According to the etiology, CIPO can be divided into idiopathic and secondary, of which the latter is the most common, often secondary to tumor, virus infection, connective tissue disease, neurological diseases, and endocrine diseases. Idiopathic CIPO in the intestinal tract is divided into visceral myopathy, neuropathy, and stromal cell lesions according to the location. Surgery is usually not recommended for CIPO, because it often does not benefit patients with CIPO, and postoperative intestinal obstruction is likely to occur, which may even worsen the condition. CASE SUMMARY: Here, we describe the case of a 43-year-old male Han Chinese patient with a 15-year history of recurrent abdominal distention with no clear cause. The results of physical, biochemical, and other relevant examinations showed no clear abnormalities. Contrast-enhanced computed tomography (CT) indicated a large duodenum, clear expansion of the intestinal lumen, and CIPO. Whole exome sequencing (WES) of the patient and his mother confirmed the diagnosis of primary familial visceral myopathy type 2 chronic pseudoileus with a rare heterozygous gene mutation in MYH11. This is the second reported case of CIPO with a heterozygous MYH11 [NM_001040113.1: c.5819delC (p.Pro1940Hisfs*91)] mutation. CONCLUSION: This case report indicates that physicians can perform routine clinical examinations, CT, and WES to achieve a diagnosis and treatment of CIPO in early disease stages.

Butyrate-producing Eubacterium rectale suppresses lymphomagenesis by alleviating the TNF-induced TLR4/MyD88/NF-κB axis.

Microbiota-induced tumorigenesis is well established in solid tumors of the gastrointestinal tract but rarely explored in hematologic malignancies. To determine the role of gut microbiota in lymphoma progression, we performed metagenomic sequencing on human primary gastrointestinal B cell lymphomas. We identified a distinct microbiota profile of intestinal lymphoma, with significantly decreased symbiotic microbes, particularly the genus Eubacterium and notably butyrate-producing Eubacterium rectale. Transfer of E. rectale-deficit microbiota of intestinal lymphoma patients to mice caused inflammation and tumor necrosis factor (TNF) production. Conversely, E. rectale treatment reduced TNF levels and the incidence of lymphoma in sensitized Eµ-Myc mice. Moreover, lipopolysaccharide from the resident microbiota of lymphoma patients and mice synergizes with TNF signaling and reinforces the NF-κB pathway via the MyD88-dependent TLR4 signaling, amalgamating in enhanced intestinal B cell survival and proliferation. These findings reveal a mechanism of inflammation-associated lymphomagenesis and a potential clinical rationale for therapeutic targeting of gut microbiota.

Changhai advanced endoscopy courses for ERCP (CHANCE) training program: A short-term training model in China.

BACKGROUND: There is huge shortage of ERCP practitioners (ERCPists) in China, and ERCP training is urgently needed. ChangHai Advanced eNdoscopy Courses for ERCP (CHANCE) is a 4-month program for ERCP training since 2004. This study evaluated the efficiency of this short-term training model, and reported on the ERCP careers of the trainees following completion of the CHANCE program. METHODS: This study was a retrospective investigation included all the CHANCE trainees from Jan 2004 to Dec 2014. Questionnaires were sent to all trainees. The career competence percentage, ERCP careers and predictive factors of career competence were investigated and analyzed. RESULTS: A total of 413 trainees participated in the CHANCE program over 11 years covered by the survey and 258 questionnaires were valid for the study. The mean (SD) age of the trainees was 35.36 (4.17), and the male to female ratio was 4.4:1. The average follow-up time was 7.77 (3.44) years. A total of 173 (67.1%) trainees had achieved career competence. In terms of ERCP careers, the mean annual ERCP volume was 120.60 (96.67), with a complication percentage of 8.2%. Hospital qualification, compliance with follow-up learning guidance, participating academic activity, and practitioner type were identified predictive factors of career competence. CONCLUSIONS: As a short-term training program, the CHANCE achieved an acceptable career competence percentage, providing endoscopists more chances to learn ERCP and giving them appropriate training guidance for career competence. This training mode is worth promoting in developing countries with shortage of ERCPists.

ETV1 Positively Correlated With Immune Infiltration and Poor Clinical Prognosis in Colorectal Cancer.

Objective: Numerous studies recently suggested that the immune microenvironment could influence the development of colorectal cancer (CRC). These findings implied that the infiltration of immune cells could be a promising prognostic biomarker for CRC. Methods: Furthermore, the Oncomine database and R2 platform analysis were applied in our research to validate CRC clinical prognosis via expression levels of polyoma enhancer activator 3 (PEA3) members. We explored the correlation of ETV1, ETV4, and ETV5 with tumor-infiltrating immune cells (TIICs) in CRC tumor microenvironments via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). Immunohistochemistry (IHC) was used to validate our CRC clinical data. Results: Our findings indicated that the upregulation of PEA3 members including ETV1 and ETV5 was positively associated with poor prognosis in CRC patients. Meanwhile, ETV1 and ETV5 may play significant roles in the development progress of CRC. Furthermore, ETV1 tends to be associated with immune infiltration of CRC, especially with cancer-associated fibroblasts and M2 macrophages. Conclusion: These findings revealed that ETV1 and ETV5 played significant roles in the development of CRC. Moreover, ETV1 was significantly associated with the infiltration of cancer-associated fibroblasts and M2 macrophages in CRC. Targeting ETV1 can be a potential auspicious approach for CRC treatment.

Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction.

OBJECTIVE: An international meeting was organised to develop consensus on (1) the landmarks to define the gastro-oesophageal junction (GOJ), (2) the occurrence and pathophysiological significance of the cardiac gland, (3) the definition of the gastro-oesophageal junctional zone (GOJZ) and (4) the causes of inflammation, metaplasia and neoplasia occurring in the GOJZ. DESIGN: Clinical questions relevant to the afore-mentioned major issues were drafted for which expert panels formulated relevant statements and textural explanations.A Delphi method using an anonymous system was employed to develop the consensus, the level of which was predefined as ≥80% of agreement. Two rounds of voting and amendments were completed before the meeting at which clinical questions and consensus were finalised. RESULTS: Twenty eight clinical questions and statements were finalised after extensive amendments. Critical consensus was achieved: (1) definition for the GOJ, (2) definition of the GOJZ spanning 1 cm proximal and distal to the GOJ as defined by the end of palisade vessels was accepted based on the anatomical distribution of cardiac type gland, (3) chemical and bacterial (Helicobacter pylori) factors as the primary causes of inflammation, metaplasia and neoplasia occurring in the GOJZ, (4) a new definition of Barrett's oesophagus (BO). CONCLUSIONS: This international consensus on the new definitions of BO, GOJ and the GOJZ will be instrumental in future studies aiming to resolve many issues on this important anatomic area and hopefully will lead to better classification and management of the diseases surrounding the GOJ.

Synthetic and Biodegradable Molybdenum(IV) Diselenide Triggers the Cascade Photo- and Immunotherapy of Tumor.

In this study, a polyvinylpyrrolidone (PVP)-decorated MoSe2 (MoSe2 -PVP) nanoparticle with excellent photothermal transforming ability and chlorin E6 (Ce6) loading capacity is designed for combined tumor photothermal therapy (PTT), tumor photodynamic therapy (PDT), and immunotherapy. The light-to-heat conversion efficiency under irradiation with an 808 nm near-infrared laser is as high as 59.28%. The MoSe2 -PVP NPs could function as an artificial catalase and catalyze the decomposition of H2 O2 . Their catalytic activity and thermal durability are higher than the native catalase, which relieve the tumor hypoxia status and sensitize the tumor PDT. The data show that the synthetic MoSe2 -PVP is biodegradable, owing to the oxidation of the Mo4+ to Mo6+ . Moreover, its degradation products could increase the proportion of mature dendritic cells and CD8+ thymus (T) cells and promote the infiltration of active CD8+ T cells in tumors. The immune checkpoint inhibitor, programmed cell death protein 1 monoclonal antibody is combined with MoSe2 -PVP and it is found that its degradation product could efficiently change the immune microenvironment of the tumor.