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.