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.

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.

Enhanced Neutrophil Immune Homeostasis Due to Deletion of PHLPP.

Neutrophils are known to adopt dynamic and distinct functional phenotypes involved in the modulation of inflammation and immune homeostasis. However, inter-cellular signaling mechanisms that govern neutrophil polarization dynamics are not well understood. Employing a novel model of PHLPP deficient mice, we examined how neutrophils deficient in PHLPP may uniquely modulate immune defense and the host response during acute colitis. We found that PHLPP-/- mice were protected from dextran sodium sulfate (DSS)-induced septic colitis characterized by minimal body weight-loss, alleviated colon tissue destruction and reduced clinical symptoms. PHLPP-/- neutrophils have enhanced immune homeostasis as compared to WT neutrophils, reflected in enhanced migratory capacity toward chemoattractants, and reduced expression of inflammatory mediators due to elevated phosphorylation of AKT, STAT1, and ERK. Further, adoptive transfer of PHLPP deficient neutrophils to WT mice is sufficient to potently alleviate the severity of DSS-induced colitis. Our data reveal that PHLPP deficient neutrophils can be uniquely reprogrammed to a state conducive to host inflammation resolution. As a consequence, PHLPP-/- neutrophils can effectively transfer immune homeostasis in mice subjected to acute colitis. Our findings hold significant and novel insights into the mechanisms by which neutrophils can be effectively reprogrammed into a homeostatic state conducive for treating acute injuries such as septic colitis.

Neutrophil programming dynamics and its disease relevance.

Neutrophils are traditionally considered as first responders to infection and provide antimicrobial host defense. However, recent advances indicate that neutrophils are also critically involved in the modulation of host immune environments by dynamically adopting distinct functional states. Functionally diverse neutrophil subsets are increasingly recognized as critical components mediating host pathophysiology. Despite its emerging significance, molecular mechanisms as well as functional relevance of dynamically programmed neutrophils remain to be better defined. The increasing complexity of neutrophil functions may require integrative studies that address programming dynamics of neutrophils and their pathophysiological relevance. This review aims to provide an update on the emerging topics of neutrophil programming dynamics as well as their functional relevance in diseases.

Toll-interacting protein deficiency promotes neurodegeneration via impeding autophagy completion in high-fat diet-fed ApoE-/- mouse model.

The excessive accumulation of specific cellular proteins or autophagic vacuoles (AVs) within neurons is a pathologic hallmark of neurodegenerative diseases. Constitutive autophagy in neurons prevents abnormal intracellular protein aggregation and is critical for maintaining cell survival. Since our previous study showed that Toll-interacting protein (Tollip)-deficient macrophages had constitutive disruption of endosome-lysosome fusion, we hypothesize that Tollip deficiency may also promote neuron death via blockage of autophagy completion. Indeed, we observed significantly higher levels of neuron death in the brain regions of cerebral cortex, hippocampus, and cerebellum from ApoE-/-/Tollip-/- mice as compared to ApoE-/- mice fed with high fat diet (HFD). We further documented diminished density of neurons and increased ratios of TUNEL positive cells in the hippocampus of ApoE-/-/Tollip-/- mice. The ultrastructural electron microscopy analyses revealed neuron cell shrinkage as well as loss of intracellular structure in brain tissues from ApoE-/-/Tollip-/- mice. There was dramatic accumulation of autophagosomes in the cytoplasm, elevated accumulation of ß-amyloid and α-synuclein, and increased levels of p62 and Parkin in the brain tissues from ApoE-/-/Tollip-/- mice as compared to ApoE-/- mice. Our data suggest that Tollip may play a crucial role in sustaining neuron health by facilitating the completion of autophagy, and that Tollip-deficiency may accelerate neuron death related to neurological disease such as Alzheimer's disease.