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.

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.

Adipocyte dysfunction promotes lung inflammation and aberrant repair: a potential target of COPD.

Background: Obesity and chronic obstructive pulmonary disease (COPD) are prevailing worldwide, bringing a heavy medical burden. Clinical and pathophysiological relationship between obesity and COPD is paradoxical and elusive. We aim to explore their inherent associations from clinical, genetic, and animal levels. Methods: We performed literature review and cohort analysis of patients with COPD to compare lung function, symptom, and prognosis among different weight groups. After retrieving datasets of obesity and COPD in Gene Expression Omnibus (GEO) database, we carried out differentially expressed gene analysis, functional enrichment, protein-protein interactions network, and weighted gene co-expression network analysis. Then, we acquired paraffin-embedded lung tissues of fatty acid-binding protein 4-Cre-BMPR2fl/fl conditional knockout (CKO) mice that were characterized by adipocyte-specific knockout of bone morphogenetic protein receptor 2 (BMPR2) for staining and analysis. Results: Our cohort study reports the effect of obesity on COPD is inconsistent with previous clinical studies. Lung function of overweight group was statistically superior to that of other groups. We also found that the inflammatory factors were significantly increased hub genes, and cytokine-associated pathways were enriched in white adipose tissue of patients with obesity. Similarly, injury repair-associated genes and pathways were further enhanced in the small airways of patients with COPD. CKO mice spontaneously developed lung injury, emphysema, and pulmonary vascular remodeling, along with increased infiltration of macrophages. BMPR2-defiecient adipocytes had dysregulated expression of adipocytokines. Conclusion: Inflammation and abnormal repair might be potential mechanisms of the pathological association between obesity and COPD. BMPR2-associated adipocyte dysfunction promoted lung inflammation and aberrant repair, in which adipocytokines might play a role and thus could be a promising therapeutic target.

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.

22q11.2 deletion syndrome and schizophrenia.

22q11.2 deletion is a common microdeletion that causes an array of developmental defects including 22q11.2 deletion syndrome (22q11DS) or DiGeorge syndrome and velocardiofacial syndrome. About 30% of patients with 22q11.2 deletion develop schizophrenia. Mice with deletion of the ortholog region in mouse chromosome 16qA13 exhibit schizophrenia-like abnormal behaviors. It is suggested that the genes deleted in 22q11DS are involved in the pathogenesis of schizophrenia. Among these genes, COMT, ZDHHC8, DGCR8, and PRODH have been identified as schizophrenia susceptibility genes. And DGCR2 is also found to be associated with schizophrenia. In this review, we focused on these five genes and reviewed their functions in the brain and the potential pathophysiological mechanisms in schizophrenia, which will give us a deeper understanding of the pathology of schizophrenia.

Bioinformatics Analysis Identifies the Estrogen Receptor 1 (ESR1) Gene and hsa-miR-26a-5p as Potential Prognostic Biomarkers in Patients with Intrahepatic Cholangiocarcinoma.

BACKGROUND Intrahepatic cholangiocarcinoma arises from the epithelial cells of the bile ducts and is associated with poor prognosis. This study aimed to use bioinformatics analysis to identify molecular biomarkers of intrahepatic cholangiocarcinoma and their potential mechanisms. MATERIAL AND METHODS MicroRNA (miRNA) and mRNA microarrays from GSE53870 and GSE32879 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) associated with prognosis were identified using limma software and Kaplan-Meier survival analysis. Predictive target genes of the DEMs were identified using miRWalk, miRTarBase, miRDB, and TargetScan databases of miRNA-binding sites and targets. Target genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Hub genes were analyzed by constructing the protein-protein interaction (PPI) network using Cytoscape. DEMs validated the hub genes, followed by construction of the miRNA-gene regulatory network. RESULTS Twenty-five DEMs were identified. Fifteen DEMs were upregulated, and ten were down-regulated. Kaplan-Meier survival analysis identified seven upregulated DEMs and nine down-regulated DEMs that were associated with the overall survival (OS), and 130 target genes were selected. GO analysis showed that target genes were mainly enriched for metabolism and development processes. KEGG analysis showed that target genes were mainly enriched for cancer processes and some signaling pathways. Fourteen hub genes identified from the PPI network were associated with the regulation of cell proliferation. The overlap between hub genes and DEMs identified the estrogen receptor 1 (ESR1) gene and hsa-miR-26a-5p. CONCLUSIONS Bioinformatics analysis identified ESR1 and hsa-miR-26a-5p as potential prognostic biomarkers for intrahepatic cholangiocarcinoma.