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.

The association of three vaccination doses with reduced gastrointestinal symptoms after severe acute respiratory syndrome coronavirus 2 infections in patients with inflammatory bowel disease.

Background: The protective efficacy of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination against the new-onset gastrointestinal (GI) symptoms following COVID-19 infection is critical among patients with inflammatory bowel disease (IBD); however, the optimal protective vaccine dose remains unknown. Therefore, this study aimed to clarify whether there is a correlation between SARS-CoV-2 vaccinations and GI symptoms following Omicron infection in patients with IBD. Methods: We conducted a multicenter cross-sectional study of IBD patients among three tertiary hospitals in eastern China. Professional physicians collected all data using online questionnaires. The patients were stratified into four groups: patients who were unvaccinated and patients who received one, two, or three vaccination doses. The primary outcome was the presence of any new-onset GI symptoms after SARS-CoV-2 infection before a negative SARS-CoV-2 nucleic acid test or a negative self-testing for antigens. Results: In total, 536 patients with IBD (175 unvaccinated, 31 vaccinated, 166 vaccinated with two doses, and 164 vaccinated with three doses) reported having COVID-19 infection. Compared with the unvaccinated, the three vaccination doses group was associated with reduced GI symptoms after infection (adjusted odds ratio = 0.56, 95% confidence interval 0.34-0.90, P < 0.05). Reduced diarrhea (adjusted odds ratio = 0.54, 95% confidence interval 0.31-0.92, P < 0.05) and nausea or vomiting (adjusted odds ratio = 0.45, 95% confidence interval 0.21-0.92, P < 0.05) were observed in the three vaccination doses group compared with the unvaccinated group. Conclusions: In conclusion, in the 536 patients with IBD who reported COVID-19 infection, we found that the three vaccination doses, but not the one or two doses group, were associated with reduced GI symptoms after infection compared with the unvaccinated group.

Causal relationship between gut microbiota and risk of gastroesophageal reflux disease: a genetic correlation and bidirectional Mendelian randomization study.

Background: Numerous observational studies have identified a linkage between the gut microbiota and gastroesophageal reflux disease (GERD). However, a clear causative association between the gut microbiota and GERD has yet to be definitively ascertained, given the presence of confounding variables. Methods: The genome-wide association study (GWAS) pertaining to the microbiome, conducted by the MiBioGen consortium and comprising 18,340 samples from 24 population-based cohorts, served as the exposure dataset. Summary-level data for GERD were obtained from a recent publicly available genome-wide association involving 78 707 GERD cases and 288 734 controls of European descent. The inverse variance-weighted (IVW) method was performed as a primary analysis, the other four methods were used as supporting analyses. Furthermore, sensitivity analyses encompassing Cochran's Q statistics, MR-Egger intercept, MR-PRESSO global test, and leave-one-out methodology were carried out to identify potential heterogeneity and horizontal pleiotropy. Ultimately, a reverse MR assessment was conducted to investigate the potential for reverse causation. Results: The IVW method's findings suggested protective roles against GERD for the Family Clostridiales Vadin BB60 group (P = 0.027), Genus Lachnospiraceae UCG004 (P = 0.026), Genus Methanobrevibacter (P = 0.026), and Phylum Actinobacteria (P = 0.019). In contrast, Class Mollicutes (P = 0.037), Genus Anaerostipes (P = 0.049), and Phylum Tenericutes (P = 0.024) emerged as potential GERD risk factors. In assessing reverse causation with GERD as the exposure and gut microbiota as the outcome, the findings indicate that GERD leads to dysbiosis in 13 distinct gut microbiota classes. The MR results' reliability was confirmed by thorough assessments of heterogeneity and pleiotropy. Conclusions: For the first time, the MR analysis indicates a genetic link between gut microbiota abundance changes and GERD risk. This not only substantiates the potential of intestinal microecological therapy for GERD, but also establishes a basis for advanced research into the role of intestinal microbiota in the etiology of GERD.

Diagnostic accuracy of magnetically guided capsule endoscopy with a detachable string for detecting oesophagogastric varices in adults with cirrhosis: prospective multicentre study.

OBJECTIVE: To evaluate the diagnostic accuracy and safety of using magnetically guided capsule endoscopy with a detachable string (ds-MCE) for detecting and grading oesophagogastric varices in adults with cirrhosis. DESIGN: Prospective multicentre diagnostic accuracy study. SETTING: 14 medical centres in China. PARTICIPANTS: 607 adults (>18 years) with cirrhosis recruited between 7 January 2021 and 25 August 2022. Participants underwent ds-MCE (index test), followed by oesophagogastroduodenoscopy (OGD, reference test) within 48 hours. The participants were divided into development and validation cohorts in a ratio of 2:1. MAIN OUTCOME MEASURES: The primary outcomes were the sensitivity and specificity of ds-MCE in detecting oesophagogastric varices compared with OGD. Secondary outcomes included the sensitivity and specificity of ds-MCE for detecting high risk oesophageal varices and the diagnostic accuracy of ds-MCE for detecting high risk oesophagogastric varices, oesophageal varices, and gastric varices. RESULTS: ds-MCE and OGD examinations were completed in 582 (95.9%) of the 607 participants. Using OGD as the reference standard, ds-MCE had a sensitivity of 97.5% (95% confidence interval 95.5% to 98.7%) and specificity of 97.8% (94.4% to 99.1%) for detecting oesophagogastric varices (both P<0.001 compared with a prespecified 85% threshold). When using the optimal 18% threshold for luminal circumference of the oesophagus derived from the development cohort (n=393), the sensitivity and specificity of ds-MCE for detecting high risk oesophageal varices in the validation cohort (n=189) were 95.8% (89.7% to 98.4%) and 94.7% (88.2% to 97.7%), respectively. The diagnostic accuracy of ds-MCE for detecting high risk oesophagogastric varices, oesophageal varices, and gastric varices was 96.3% (92.6% to 98.2%), 96.9% (95.2% to 98.0%), and 96.7% (95.0% to 97.9%), respectively. Two serious adverse events occurred with OGD but none with ds-MCE. CONCLUSION: The findings of this study suggest that ds-MCE is a highly accurate and safe diagnostic tool for detecting and grading oesophagogastric varices and is a promising alternative to OGD for screening and surveillance of oesophagogastric varices in patients with cirrhosis. TRIAL REGISTRATION: ClinicalTrials.gov NCT03748563.

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.

Causal link between gut microbiota and four types of pancreatitis: a genetic association and bidirectional Mendelian randomization study.

Background: A number of recent observational studies have indicated a correlation between the constitution of gut microbiota and the incidence of pancreatitis. Notwithstanding, observational studies are unreliable for inferring causality because of their susceptibility to confounding, bias, and reverse causality, the causal relationship between specific gut microbiota and pancreatitis is still unclear. Therefore, our study aimed to investigate the causal relationship between gut microbiota and four types of pancreatitis. Methods: An investigative undertaking encompassing a genome-wide association study (GWAS) comprising 18,340 participants was undertaken with the aim of discerning genetic instrumental variables that exhibit associations with gut microbiota, The aggregated statistical data pertaining to acute pancreatitis (AP), alcohol-induced AP (AAP), chronic pancreatitis (CP), and alcohol-induced CP (ACP) were acquired from the FinnGen Consortium. The two-sample bidirectional Mendelian randomization (MR) approach was utilized. Utilizing the Inverse-Variance Weighted (IVW) technique as the cornerstone of our primary analysis. The Bonferroni analysis was used to correct for multiple testing, In addition, a number of sensitivity analysis methodologies, comprising the MR-Egger intercept test, the Cochran's Q test, MR polymorphism residual and outlier (MR-PRESSO) test, and the leave-one-out test, were performed to evaluate the robustness of our findings. Results: A total of 28 intestinal microflora were ascertained to exhibit significant associations with diverse outcomes of pancreatitis. Among them, Class Melainabacteria (OR = 1.801, 95% CI: 1.288-2.519, p = 0.008) has a strong causality with ACP after the Bonferroni-corrected test, in order to assess potential reverse causation effects, we used four types of pancreatitis as the exposure variable and scrutinized its impact on gut microbiota as the outcome variable, this analysis revealed associations between pancreatitis and 30 distinct types of gut microflora. The implementation of Cochran's Q test revealed a lack of substantial heterogeneity among the various single nucleotide polymorphisms (SNP). Conclusion: Our first systematic Mendelian randomization analysis provides evidence that multiple gut microbiota taxa may be causally associated with four types of pancreatitis disease. This discovery may contribute significant biomarkers conducive to the preliminary, non-invasive identification of Pancreatitis. Additionally, it could present viable targets for potential therapeutic interventions in the disease's treatment.

Gut microbiota controls the development of chronic pancreatitis: A critical role of short-chain fatty acids-producing Gram-positive bacteria.

Chronic pancreatitis (CP) is a progressive and irreversible fibroinflammatory disorder, accompanied by pancreatic exocrine insufficiency and dysregulated gut microbiota. Recently, accumulating evidence has supported a correlation between gut dysbiosis and CP development. However, whether gut microbiota dysbiosis contributes to CP pathogenesis remains unclear. Herein, an experimental CP was induced by repeated high-dose caerulein injections. The broad-spectrum antibiotics (ABX) and ABX targeting Gram-positive (G+) or Gram-negative bacteria (G-) were applied to explore the specific roles of these bacteria. Gut dysbiosis was observed in both mice and in CP patients, which was accompanied by a sharply reduced abundance for short-chain fatty acids (SCFAs)-producers, especially G+ bacteria. Broad-spectrum ABX exacerbated the severity of CP, as evidenced by aggravated pancreatic fibrosis and gut dysbiosis, especially the depletion of SCFAs-producing G+ bacteria. Additionally, depletion of SCFAs-producing G+ bacteria rather than G- bacteria intensified CP progression independent of TLR4, which was attenuated by supplementation with exogenous SCFAs. Finally, SCFAs modulated pancreatic fibrosis through inhibition of macrophage infiltration and M2 phenotype switching. The study supports a critical role for SCFAs-producing G+ bacteria in CP. Therefore, modulation of dietary-derived SCFAs or G+ SCFAs-producing bacteria may be considered a novel interventive approach for the management of CP.

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.

Simultaneous profiling of host expression and microbial abundance by spatial metatranscriptome sequencing.

We developed an analysis pipeline that can extract microbial sequences from spatial transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline spatial metatranscriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from these novel data that showed host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively showed the capture efficiency and recall of our methods. This proof-of-concept work shows the feasibility of SMT analysis and paves the way for further experimental optimization and application.

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.

Exploring the potential targets of Biling Weitong Granules on visceral hypersensitivity through integration of network pharmacology and in vivo analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Biling Weitong Granules (BLWTG) are a newly developed traditional Chinese medicine prescription based on the ancient prescription Jinlingzi San and Zuojin Wan. It is used for the treatment of functional gastrointestinal disorders (FGIDs) featured as visceral hypersensitivity (VH). However, its active ingredients and protein targets involved still remain unknown. AIM OF THE STUDY: To explore the potential targets of BLWTG for the treatment of visceral hypersensitivity. MATERIALS AND METHODS: Active components and their protein targets of BLWTG were screened from TCMSP database and the component-target network were constructed with Cytoscape software. Irritable bowel syndrome (IBS) was the representative disease in this study and information on its linked pathways was obtained from NCBI, Drugbank and Genecard. Target pathways of BLWTG were analyzed through KEGG to verify the correlation with IBS related pathways.Then, the VH mouse models was induced by maternal separation (MS), randomly divided into normal saline (NS),BLWTG1 (low-dosage) and BLWTG2 (high-dosage) group. After intervention, threshold intensity of colorectal distension (CRD) and body weight were measured to evaluate relief of IBS symptoms. Elisa was performed to evaluate 5-HT concentration changes of colon tissues. Flow cytometry was performed to assess changes of colon eosinophils and mast cells proportion. Transcriptome sequencing was employed to analyze changes of pathways and differential genes. RESULTS: 199 protein targets and 132 active components of BLWTG were identified. KEGG analysis revealed the overlap between BLWTG target pathways and IBS related pathways such as neuroactive ligand-receptor interaction, tryptophan metabolism and inflammatory reaction. 34 genes were not only BLWTG target proteins but also recognized targets for treating IBS. After maternal separation (MS), the mice showed a significant decrease in threshold intensity of CRD, a progressive decrease in body weight and an increase of 5-HT concentration of colon tissue. The proportion of mast cells and eosinophils in the colon increased. Differential genes including Hp,Ido1 and Aqp7 were significantly increased in MS mice group and IBS-related pathways were upregulated. After treatment of BLWTG, threshold intensity of CRD and body weight were significantly improved and IBS related pathways were downregulated. In addition, among BLWTG protein targets, Il1b,Tnf,Adrb1 and Nos2 were found upregulated in MS+NS mice and downregulated after BLWTG intervention through combination of transcriptome sequencing. CONCLUSIONS: In maternal separation-induced mouse models, BLWTG could alleviate visceral hypersensitivity, possibly through downregulation of 5-HT concentration and eosinophils and mast cells proportion in colon and critical pathways such as neuroactive ligand-receptor pathway. Potential targets of BLWTG including Il1b,Tnf,Adrb1 and Nos2 were found through integration of network pharmacology database and transcriptome sequencing, providing evidence for further study on mechanisms underlying visceral hypersensitivity.

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.

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.

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.

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.