Clostridioides difficile infection: history, epidemiology, risk factors, prevention, clinical manifestations, treatment, and future options.

SUMMARYClostridioides difficile infection (CDI) is one of the major issues in nosocomial infections. This bacterium is constantly evolving and poses complex challenges for clinicians, often encountered in real-life scenarios. In the face of CDI, we are increasingly equipped with new therapeutic strategies, such as monoclonal antibodies and live biotherapeutic products, which need to be thoroughly understood to fully harness their benefits. Moreover, interesting options are currently under study for the future, including bacteriophages, vaccines, and antibiotic inhibitors. Surveillance and prevention strategies continue to play a pivotal role in limiting the spread of the infection. In this review, we aim to provide the reader with a comprehensive overview of epidemiological aspects, predisposing factors, clinical manifestations, diagnostic tools, and current and future prophylactic and therapeutic options for C. difficile infection.

Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes.

Large Language Models (LLMs) are transformer-based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts and generate context-based responses. The interest in LLMs has not spared digestive disease academics, who have mainly investigated foundational LLM accuracy, which ranges from 25% to 90% and is influenced by the lack of standardized rules to report methodologies and results for LLM-oriented research. In addition, a critical issue is the absence of a universally accepted definition of accuracy, varying from binary to scalar interpretations, often tied to grader expertise without reference to clinical guidelines. We address strategies and challenges to increase accuracy. In particular, LLMs can be infused with domain knowledge using Retrieval Augmented Generation (RAG) or Supervised Fine-Tuning (SFT) with reinforcement learning from human feedback (RLHF). RAG faces challenges with in-context window limits and accurate information retrieval from the provided context. SFT, a deeper adaptation method, is computationally demanding and requires specialized knowledge. LLMs may increase patient quality of care across the field of digestive diseases, where physicians are often engaged in screening, treatment and surveillance for a broad range of pathologies for which in-context learning or SFT with RLHF could improve clinical decision-making and patient outcomes. However, despite their potential, the safe deployment of LLMs in healthcare still needs to overcome hurdles in accuracy, suggesting a need for strategies that integrate human feedback with advanced model training.

The Metabolic Impact of Nonalcoholic Fatty Liver Disease on Cognitive Dysfunction: A Comprehensive Clinical and Pathophysiological Review.

Nonalcoholic fatty liver disease (NAFLD) exponentially affects the global healthcare burden, and it is currently gaining increasing interest in relation to its potential impact on central nervous system (CNS) diseases, especially concerning cognitive deterioration and dementias. Overall, scientific research nowadays extends to different levels, exploring NAFLD's putative proinflammatory mechanism of such dysmetabolic conditions, spreading out from the liver to a multisystemic involvement. The aim of this review is to analyze the most recent scientific literature on cognitive involvement in NAFLD, as well as understand its underlying potential background processes, i.e., neuroinflammation, the role of microbiota in the brain-liver-gut axis, hyperammonemia neurotoxicity, insulin resistance, free fatty acids, and vitamins.

The Gut-Liver-Brain Axis: From the Head to the Feet.

The gut-liver-brain axis, a multifaceted network of communication, intricately connects the enteric, hepatic, and central nervous systems [...].

Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease.

The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe-disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.

Celiac Disease and Neurological Manifestations: From Gluten to Neuroinflammation.

Celiac disease (CD) is a complex multi-organ disease with a high prevalence of extra-intestinal involvement, including neurological and psychiatric manifestations, such as cerebellar ataxia, peripheral neuropathy, epilepsy, headache, cognitive impairment, and depression. However, the mechanisms behind the neurological involvement in CD remain controversial. Recent evidence shows these can be related to gluten-mediated pathogenesis, including antibody cross-reaction, deposition of immune-complex, direct neurotoxicity, and in severe cases, vitamins or nutrients deficiency. Here, we have summarized new evidence related to gut microbiota and the so-called "gut-liver-brain axis" involved in CD-related neurological manifestations. Additionally, there has yet to be an agreement on whether serological or neurophysiological findings can effectively early diagnose and properly monitor CD-associated neurological involvement; notably, most of them can revert to normal with a rigorous gluten-free diet. Moving from a molecular level to a symptom-based approach, clinical, serological, and neurophysiology data might help to disentangle the many-faceted interactions between the gut and brain in CD. Eventually, the identification of multimodal biomarkers might help diagnose, monitor, and improve the quality of life of patients with "neuroCD".

Repeated double cross-validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients.

Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra.

Homocysteine in Neurology: A Possible Contributing Factor to Small Vessel Disease.

Homocysteine (Hcy) is a sulfur-containing amino acid generated during methionine metabolism, accumulation of which may be caused by genetic defects or the deficit of vitamin B12 and folate. A serum level greater than 15 micro-mols/L is defined as hyperhomocysteinemia (HHcy). Hcy has many roles, the most important being the active participation in the transmethylation reactions, fundamental for the brain. Many studies focused on the role of homocysteine accumulation in vascular or degenerative neurological diseases, but the results are still undefined. More is known in cardiovascular disease. HHcy is a determinant for the development and progression of inflammation, atherosclerotic plaque formation, endothelium, arteriolar damage, smooth muscle cell proliferation, and altered-oxidative stress response. Conversely, few studies focused on the relationship between HHcy and small vessel disease (SVD), despite the evidence that mice with HHcy showed a significant end-feet disruption of astrocytes with a diffuse SVD. A severe reduction of vascular aquaporin-4-water channels, lower levels of high-functioning potassium channels, and higher metalloproteinases are also observed. HHcy modulates the N-homocysteinylation process, promoting a pro-coagulative state and damage of the cellular protein integrity. This altered process could be directly involved in the altered endothelium activation, typical of SVD and protein quality, inhibiting the ubiquitin-proteasome system control. HHcy also promotes a constant enhancement of microglia activation, inducing the sustained pro-inflammatory status observed in SVD. This review article addresses the possible role of HHcy in small-vessel disease and understands its pathogenic impact.

A story of liver and gut microbes: how does the intestinal flora affect liver disease? A review of the literature.

Each individual is endowed with a unique gut microbiota (GM) footprint that mediates numerous host-related physiological functions, such as nutrient metabolism, maintenance of the structural integrity of the gut mucosal barrier, immunomodulation, and protection against microbial pathogens. Because of increased scientific interest in the GM, its central role in the pathophysiology of many intestinal and extraintestinal conditions has been recognized. Given the close relationship between the gastrointestinal tract and the liver, many pathological processes have been investigated in the light of a microbial-centered hypothesis of hepatic damage. In this review we introduce to neophytes the vast world of gut microbes, including prevalent bacterial distribution in healthy individuals, how the microbiota is commonly analyzed, and the current knowledge of the role of GM in liver disease pathophysiology. Also, we highlight the potentials and downsides of GM-based therapy.

Spleen stiffness can be employed to assess the efficacy of spontaneous portosystemic shunts in relieving portal hypertension.

INTRODUCTION: Spleen stiffness (SS) has been found to mirror dynamic changes in portal pressure after transjugular intrahepatic portosystemic shunt (TIPS) placement. However, there is no data available regarding SS in patients with spontaneous portosystemic shunting (SPSS), especially in regards to prediction of hepatic decompensation. METHODS: We retrospectively selected patients with confirmed SPSS and esophageal varices (EVs) at endoscopic examination, and recorded any decompensating event (i.e., variceal hemorrhage, overt hepatic encephalopathy, refractory ascites, spontaneous bacterial peritonitis, hepatorenal syndrome) in the first twelve months following liver and spleen elastography. RESULTS: The patients who presented decompensating events showed lower platelet count (94.5 vs. 121.5 g/L, p < 0.001), higher SS (44 vs. 30 kPa, p < 0.001), higher probability of EVs according to SS (77 vs. 2 %, p < 0.001), and higher spleen diameter (14 vs. 12 cm, p = 0.043). They also showed a higher prevalence of splenorenal shunts (66.7 vs. 31.2%), and a significantly wider SPSS major diameter (14.5 vs. 8 mm, p < 0.001). CONCLUSION: SS could predict SPSS efficacy in relieving portal pressure, and could predict decompensating events in patients with SPSS.

Spleen Stiffness Probability Index (SSPI): A simple and accurate method to detect esophageal varices in patients with compensated liver cirrhosis.

INTRODUCTION AND OBJECTIVES: Recent findings pointed out that even low-risk esophageal varices (EVs) are markers of severe prognosis. Accordingly, we analyzed spleen stiffness (SS) as a non-invasive method to predict EVs of any grade in a cohort of patients with compensated liver cirrhosis. METHOD: We measured SS and liver stiffness (LS) using point-Shear-Wave Elastography (pSWE) with Philips Affiniti 70 system in 210 cirrhotic patients who had undergone endoscopic screening for EVs. We compared SS and LS predictive capability for EVs of any grade. RESULTS: SS was higher in cirrhotic patients with EVs if compared to patients without EVs (p<0.001). The cut-off analysis detected 31kPa (100% sensitivity and negative predictive value) as the value to rule-out EVs and 69kPa (100% specificity and positive predictive value) to rule-in EVs. Besides, we developed the Spleen Stiffness Probability Index (SSPI), that can provide a probability of presence/absence of EVs. SSPI was the best model according to all discriminative and calibration metrics (AIC=120, BIC=127, AUROC=0.95, Pseudo-R2=0.74). SS demonstrated higher correlation with spleen bipolar diameter and spleen surface (r=0.52/0.55) if compared to LS (r=0.30/0.25) - and with platelet count as well (r=0.67 vs r=0.4). CONCLUSION: SS showed significantly higher performance than other parameters, proving to be the best non-invasive test in the screening of EVs: by directly applying SS cut-off of 31kPa, our department could have safely avoided endoscopy in 36% of patients. Despite cut-off analyses, it was possible to create a probability model that could further stratify low-risk from high-risk patients (for any grade of EVs).

Is there an association between commonly employed biomarkers of liver fibrosis and liver stiffness in the general population?

INTRODUCTION AND OBJECTIVES: Surrogate biomarkers of liver fibrosis developed in tertiary care are increasingly used in general populations. We evaluated the association between liver stiffness (LS) and five continuous (AST/ALT, APRI, Forns Index, FIB-4, GGT) and two discrete biomarkers (BARD, BAAT) in a general population. PATIENTS AND METHODS: 636 (29%) of the 2159 citizens of the Bagnacavallo Study had LS measured by transient elastography. Using linear regression with univariate multiple imputation, we evaluated the association of LS with the above biomarkers in the total sample of 2159 citizens. RESULTS: The mean change of LS between the 5th and 95th internal percentile of any continuous biomarker was ≤1kPa. The mean change of LS between scores 0 and 3 of BARD and scores 0 and ≥3 of BAAT was >1kPa but of doubtful clinical relevance. CONCLUSION: We found a modest association between LS and seven biomarkers of liver fibrosis in a general population.

Evaluation of spleen stiffness in healthy volunteers using point shear wave elastography.

INTRODUCTION AND OBJECTIVES: This study aims to measure the values of spleen stiffness (SS) in healthy subjects, the inter-operator agreement in SS measurement, and to detect statistically significant correlations between SS and age, sex, weight, BMI, portal vein dynamics and splenic dimensions. MATERIALS AND METHODS: The study included 100 healthy volunteers who had no substantial alcohol intake (<30g/daily for man, <20g/daily women), were negative on hepatitis B, hepatitis C, HIV blood serology, and had any history of lymphoproliferative disorders. Abdominal ultrasound, liver and spleen elastography were performed on each patient to search for focal splenic lesions, bile tract or portal vein dilatation, moderate/severe liver steatosis, and to measure liver and spleen stiffness. RESULTS: The mean value was 18.14 (±3.08) kPa. In the group of men (n=49), the mean was 17.73 (±2.91) kPa, whereas in the group of women (n=51) it was 16.72 (±3.32) kPa. Statistical analyses showed no correlation between spleen stiffness and sex, age, weight, and BMI. Regarding their splenoportal axis, statistically significant differences in SS were found in the means of the two subgroups of subjects stratified by their portal flow velocity (p=0.003) and spleen area (p<0.001). Spearman's rank showed a weak association between SS and portal flow velocty (r=0.271) and splenic area (r=-0.237). ICC showed excellent (0.96) inter-operator agreement and Bland-Altman plot demonstrated no systematic over/under-estimation of spleen stiffness values. CONCLUSIONS: Our results may serve as a reference point in the evaluation of SS especially in patients affected by advanced liver disease.

Systematic review: The use of large language models as medical chatbots in digestive diseases.

BACKGROUND: Interest in large language models (LLMs), such as OpenAI's ChatGPT, across multiple specialties has grown as a source of patient-facing medical advice and provider-facing clinical decision support. The accuracy of LLM responses for gastroenterology and hepatology-related questions is unknown. AIMS: To evaluate the accuracy and potential safety implications for LLMs for the diagnosis, management and treatment of questions related to gastroenterology and hepatology. METHODS: We conducted a systematic literature search including Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus and the Web of Science Core Collection to identify relevant articles published from inception until January 28, 2024, using a combination of keywords and controlled vocabulary for LLMs and gastroenterology or hepatology. Accuracy was defined as the percentage of entirely correct answers. RESULTS: Among the 1671 reports screened, we identified 33 full-text articles on using LLMs in gastroenterology and hepatology and included 18 in the final analysis. The accuracy of question-responding varied across different model versions. For example, accuracy ranged from 6.4% to 45.5% with ChatGPT-3.5 and was between 40% and 91.4% with ChatGPT-4. In addition, the absence of standardised methodology and reporting metrics for studies involving LLMs places all the studies at a high risk of bias and does not allow for the generalisation of single-study results. CONCLUSIONS: Current general-purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks, which may lead to adverse patient safety events through incorrect information or triage recommendations, which might overburden healthcare systems or delay necessary care.

Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework.

Large language models (LLMs) can potentially transform healthcare, particularly in providing the right information to the right provider at the right time in the hospital workflow. This study investigates the integration of LLMs into healthcare, specifically focusing on improving clinical decision support systems (CDSSs) through accurate interpretation of medical guidelines for chronic Hepatitis C Virus infection management. Utilizing OpenAI's GPT-4 Turbo model, we developed a customized LLM framework that incorporates retrieval augmented generation (RAG) and prompt engineering. Our framework involved guideline conversion into the best-structured format that can be efficiently processed by LLMs to provide the most accurate output. An ablation study was conducted to evaluate the impact of different formatting and learning strategies on the LLM's answer generation accuracy. The baseline GPT-4 Turbo model's performance was compared against five experimental setups with increasing levels of complexity: inclusion of in-context guidelines, guideline reformatting, and implementation of few-shot learning. Our primary outcome was the qualitative assessment of accuracy based on expert review, while secondary outcomes included the quantitative measurement of similarity of LLM-generated responses to expert-provided answers using text-similarity scores. The results showed a significant improvement in accuracy from 43 to 99% (p < 0.001), when guidelines were provided as context in a coherent corpus of text and non-text sources were converted into text. In addition, few-shot learning did not seem to improve overall accuracy. The study highlights that structured guideline reformatting and advanced prompt engineering (data quality vs. data quantity) can enhance the efficacy of LLM integrations to CDSSs for guideline delivery.

The Role of Endoscopic Ultrasonography (EUS) in Metastatic Tumors in the Pancreas: 10 Years of Experience from a Single High-Volume Center.

BACKGROUND: Metastatic pancreatic lesions (MPLs) are relatively uncommon, constituting 2 to 5% of all pancreatic tumors. They often manifest as solitary lesions without distinct clinical symptoms, usually identified incidentally during radiologic imaging for the surveillance of prior malignancies. Differentiating these lesions from primary pancreatic tumors presents a significant challenge due to their nonspecific presentation. METHODS: We aimed to prospectively assess the effectiveness of endoscopic ultrasound (EUS) and EUS-guided fine needle aspiration/biopsy (EUS-FNA/B) in diagnosing MPLs in a carefully selected cohort of patients presenting with pancreatic masses. Additionally, we sought to examine the relevance of specific EUS findings in supporting the initial diagnosis of MPLs and their agreement with the definitive cytological diagnosis. This study retrospectively analyzed data from 41 patients diagnosed with MPLs between 2013 and 2023, focusing on their clinical and pathological characteristics, the echogenic features of the pancreatic lesions, and the techniques used for tissue acquisition. RESULTS: The incidence of MPLs in our cohort was 3.53%, with the most frequent primary tumors originating in the kidney (43.90%), colorectum (9.76%), lung (9.76%), lymphoma (9.76%), and breast (4.88%). MPLs typically presented as hypoechoic, oval-shaped lesions with well-defined borders and were predominantly hypervascular. Interestingly, 68.29% of the cases were discovered incidentally during follow-up of the primary tumors, while the involvement of the common bile duct was uncommon (19.51%). CONCLUSIONS: EUS and EUS-FNA/B have been validated as valuable diagnostic tools for identifying MPLs. While our findings are promising, further multicenter studies are necessary to corroborate these results and elucidate the predictive value of specific EUS characteristics in determining the metastatic origin of pancreatic lesions.

Predicting response to non-selective beta-blockers with liver-spleen stiffness and heart rate in patients with liver cirrhosis and high-risk varices.

INTRODUCTION: Non-selective beta-blockers (NSBB) are used for primary prophylaxis in patients with liver cirrhosis and high-risk varices (HRVs). Assessing therapeutic response is challenging due to the invasive nature of hepatic venous pressure gradient (HVPG) measurement. This study aims to define a noninvasive machine-learning based approach to determine response to NSBB in patients with liver cirrhosis and HRVs. METHODS: We conducted a prospective study on a cohort of cirrhotic patients with documented HRVs receiving NSBB treatment. Patients were followed-up with clinical and elastography appointments at 3, 6, and 12 months after NSBB treatment initiation. NSBB response was defined as stationary or downstaging variceal grading at the 12-month esophagogastroduodenoscopy (EGD). In contrast, non-response was defined as upstaging variceal grading at the 12-month EGD or at least one variceal hemorrhage episode during the 12-month follow-up. We chose cut-off values for univariate and multivariate model with 100% specificity. RESULTS: According to least absolute shrinkage and selection operator (LASSO) regression, spleen stiffness (SS) and liver stiffness (LS) percentual decrease, along with changes in heart rate (HR) at 3 months were the most significant predictors of NSBB response. A decrease > 11.5% in SS, > 16.8% in LS, and > 25.3% in HR was associated with better prediction of clinical response to NSBB. SS percentual decrease showed the highest accuracy (86.4%) with high sensitivity (78.8%) when compared to LS and HR. The multivariate model incorporating SS, LS, and HR showed the highest discrimination and calibration metrics (AUROC = 0.96), with the optimal cut-off of 0.90 (sensitivity 94.2%, specificity 100%, PPV 95.7%, NPV 100%, accuracy 97.5%).