Currently, functional dyspepsia (FD) and irritable bowel syndrome (IBS) are among the most common nosological units in the structure of functional gastrointestinal diseases in adults. An important problem of treatment of these diseases at the current stage of medicine is low efficiency of monotarget drugs, which is determined by multicomponent pathogenesis. Indeed, the currently available methods of drug treatment of FD and IBS have suboptimal efficacy, as illustrated by recent meta-analyses demonstrating high rates of NNT (the average number of patients who need to be treated to achieve a certain favorable outcome). In addition, the frequent “overlap” of these diseases forces clinicians to prescribe several drugs with different pharmacological actions to the patient, which inevitably leads to a decrease in compliance. The optimal strategy for managing patients with FD and IBS is the tactics of multitarget drugs that act on several links in the pathogenesis of these pathologies and have a significant evidence base in the effectiveness and safety of use. STW 5 (Iberogast®), included in the clinical guidelines of the Russian Gastroenterological Association on the diagnosis and treatment of patients with FD, published in 2017, has the above-mentioned characteristics, as well as the clinical guidelines of the Russian Gastroenterological Association in collaboration with the Russian Association of Coloproctologists on the diagnosis and treatment of IBS, published in 2021. The clinical effectiveness of Iberogast in the treatment of FD and IBS has been demonstrated in a number of randomized trials, the results of which showed high efficacy of the drug and its good tolerability.
Due to the aging of the world population and westernization of lifestyles, the prevalence of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is rapidly rising and is expected to put a strong socioeconomic burden on health systems worldwide. Due to the limited success of clinical trials of therapies against neurodegenerative diseases, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome as well as metabolome studies along the gastrointestinal-brain axis have already revealed important insights into disease pathomechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the association between microbiota, metabolites, and neurodegeneration in light of the gastrointestinal-brain axis. In this context, we also illustrate state-of-the art methods of microbiome and metabol
Non-alcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in children and adolescents. Although obesity is the leading cause of NAFLD, the etiologies of NAFLD are multifactorial (e.g., high-fat diet, a lack of exercise, gender, maternal obesity, the antibiotic use), and each of these factors leads to dysbiosis of the gut microbiota community. The gut microbiota is a key player in the development and regulation of the gut mucosal immune system as well as the regulation of both NAFLD and obesity. Dysbiosis of the gut microbiota promotes the development of NAFLD via alteration of gut-liver homeostasis, including disruption of the gut barrier, portal transport of bacterial endotoxin (lipopolysaccharide) to the liver, altered bile acid profiles, and decreased concentrations of short-chain fatty acids. In terms of prevention and treatment, conventional approaches (e.g., dietary and exercise interventions) against obesity and NAFLD have been confirmed to recover the dysbiosis and dysbiosis-mediated altered metabolism. In addition, increased understanding of the importance of gut microbiota-mediated homeostasis in the prevention of NAFLD suggests the potential effectiveness of gut microbiota-targeted preventive and therapeutic strategies (e.g., probiotics and fecal transplantation) against NAFLD in children and adolescents. This review comprehensively summarizes our current knowledge of the gut microbiota, focusing on its interaction with NAFLD and its potential therapeutic role in obese children and adolescents with this disorder.
The three major autoimmune liver diseases are autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC).These conditions are assumed to result from a breakdown in immunological tolerance, which leads to an inflammatory process that causes liver damage.The self-attack is started by T-helper cell-mediated identification of liver autoantigens and B-cell production of autoantibodies,and it is maintained by a reduction in the number and activity of regulatory T-cells.Infections and environmental factors have been explored as triggering factors for these conditions, in addition to a genetic predisposition.Allelic mutations in the HLA locus have been linked to vulnerability, as have relationships with single nucleotide polymorphisms in non-HLA genes.Despite the advances in the management of these diseases, there is no curative treatment for these disorders, and a significant number of patients eventually progress to an end-stage liver disease requiring liver transplantation.In this line, tailored immune-therapeutics have emerged as possible treatments to control the disease.In addition, early diagnosis and treatment are pivotal for reducing the long-lasting effects of these conditions and their burden on quality of life.Herein we present a review of the etiology, clinical presentation, diagnosis, and challenges on ALDs and the feasible solutions for these complex diseases.
Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present a procedure based on temporal network theory to identify possible sentinel locations using two indicators: vulnerability (i.e. the probability of being reached by the disease) and time of infection (i.e. the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study we simulated the introduction of F.A.S.T. (Foot and Mouth Similar Transboundary) diseases in Senegal and used data taken from 2020 Sanitary certificates (LPS, laissez-passer
Liver diseases pose a significant global health burden, impacting many individuals and having substantial economic and social consequences. Rising liver problems are considered a fatal disease in many countries, such as Egypt and Moldova. This study aims to develop a diagnosis and treatment model for liver disease using Basic Formal Ontology (BFO), Patient Clinical Data (PCD) ontology, and detection rules derived from a decision tree algorithm. For the development of the ontology, the National Viral Hepatitis Control Program (NVHCP) guidelines were used, which made the ontology more accurate and reliable. The Apache Jena framework uses batch processing to detect events based on these rules. Based on the event detected, queries can be directly processed using SPARQL. We convert these Decision Tree (DT) and medical guidelines-based rules into Semantic Web Rule Language (SWRL) to operationalize the ontology. Using this SWRL in the ontology to predict different types of liver disease with the help of the Pellet and Drools inference engines in Protege Tools, a total of 615 records were taken from different liver diseases. After inferring the rules, the result can be generated for the pa
We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science and Social Science Citation Indexes (SCI and SSCI) with similar data based on Scopus 2012. First, global maps were developed for the two sets separately; sets of documents can then be compared using overlays to both maps. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the two sets; that is, 96.4% of the 10,936 journals contained in JCR or 51.2% of the 20,554 journals covered by Scopus. Network analysis was then pursued on the set of journals shared between the two databases and the two sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (that is, numbers of citing journals) or total citations is similar in both databases overall (Spearman's \r{ho} > 0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important--they are citing shared journals rather than bein
Non-alcoholic fatty liver disease (NAFLD) is one of the most widespread liver disorders on a global scale, posing a significant threat of progressing to more severe conditions like nonalcoholic steatohepatitis (NASH), liver fibrosis, cirrhosis, and hepatocellular carcinoma. Diagnosing and staging NAFLD presents challenges due to its non-specific symptoms and the invasive nature of liver biopsies. Our research introduces a novel artificial intelligence cascade model employing ensemble learning and feature fusion techniques. We developed a non-invasive, robust, and reliable diagnostic artificial intelligence tool that utilizes anthropometric and laboratory parameters, facilitating early detection and intervention in NAFLD progression. Our novel artificial intelligence achieved an 86% accuracy rate for the NASH steatosis staging task (non-NASH, steatosis grade 1, steatosis grade 2, and steatosis grade 3) and an impressive 96% AUC-ROC for distinguishing between NASH (steatosis grade 1, grade 2, and grade3) and non-NASH cases, outperforming current state-of-the-art models. This notable improvement in diagnostic performance underscores the potential application of artificial intelligence
Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invasive tool, can enable the detection of liver disease by capturing cardiovascular-hepatic interactions. Methods: We trained tree-based machine learning models on ECG features to detect liver diseases using two large datasets: MIMIC-IV-ECG (467,729 patients, 2008-2019) and ECG-View II (775,535 patients, 1994-2013). The task was framed as binary classification, with performance evaluated via the area under the receiver operating characteristic curve (AUROC). To improve interpretability, we applied explainability methods to identify key predictive features. Findings: The models showed strong predictive performance with good generalizability. For example, AUROCs for alcoholic liver disease (K70) were 0.8025 (95% confidence interval (CI), 0.8020-0.8035) internally and 0.7644 (95% CI, 0.7641-0.7649) externally; for hepatic failure (K72), scores were 0.7404 (95% CI, 0.7389-0.7415) and 0.7498 (95% CI, 0.7494-0.7509), respectively. The explainability analysis consistently identified age and prolonged QTc intervals (
This study examines the social media uptake of scientific journals on two different platforms - X and WeChat - by comparing the adoption of X among journals indexed in the Science Citation Index-Expanded (SCIE) with the adoption of WeChat among journals indexed in the Chinese Science Citation Database (CSCD). The findings reveal substantial differences in platform adoption and user engagement, shaped by local contexts. While only 22.7% of SCIE journals maintain an X account, 84.4% of CSCD journals have a WeChat official account. Journals in Life Sciences & Biomedicine lead in uptake on both platforms, whereas those in Technology and Physical Sciences show high WeChat uptake but comparatively lower presence on X. User engagement on both platforms is dominated by low-effort interactions rather than more conversational behaviors. Correlation analyses indicate weak-to-moderate relationships between bibliometric indicators and social media metrics, confirming that online engagement reflects a distinct dimension of journal impact, whether on an international or a local platform. These findings underscore the need for broader social media metric frameworks that incorporate locally dom
Liver diseases are a serious health concern in the world, which requires precise and timely diagnosis to enhance the survival chances of patients. The current literature implemented numerous machine learning and deep learning models to classify liver diseases, but most of them had some issues like high misclassification error, poor interpretability, prohibitive computational expense, and lack of good preprocessing strategies. In order to address these drawbacks, we introduced StackLiverNet in this study; an interpretable stacked ensemble model tailored to the liver disease detection task. The framework uses advanced data preprocessing and feature selection technique to increase model robustness and predictive ability. Random undersampling is performed to deal with class imbalance and make the training balanced. StackLiverNet is an ensemble of several hyperparameter-optimized base classifiers, whose complementary advantages are used through a LightGBM meta-model. The provided model demonstrates excellent performance, with the testing accuracy of 99.89%, Cohen Kappa of 0.9974, and AUC of 0.9993, having only 5 misclassifications, and efficient training and inference speeds that are am
Intestinal microecology is established from birth and is constantly changing until homeostasis is reached. Intestinal microecology is involved in the immune inflammatory response of the intestine and regulates the intestinal barrier function. The imbalance of intestinal microecology is closely related to the occurrence and development of digestive system diseases. In some gastrointestinal diseases related to pediatric surgery, intestinal microecology and its metabolites undergo a series of changes, which can provide a certain basis for the diagnosis of diseases. The continuous development of microecological agents and fecal microbiota transplantation technology has provided a new means for its clinical treatment. We review the relationship between pathogenesis, diagnosis and treatment of pediatric surgery-related gastrointestinal diseases and intestinal microecology, in order to provide new ideas and methods for clinical diagnosis, treatment and research.
An exploratory, descriptive analysis is presented of the national orientation of scientific, scholarly journals as reflected in the affiliations of publishing or citing authors. It calculates for journals covered in Scopus an Index of National Orientation (INO), and analyses the distribution of INO values across disciplines and countries, and the correlation between INO values and journal impact factors. The study did not find solid evidence that journal impact factors are good measures of journal internationality in terms of the geographical distribution of publishing or citing authors, as the relationship between a journal's national orientation and its citation impact is found to be inverse U-shaped. In addition, journals publishing in English are not necessarily internationally oriented in terms of the affiliations of publishing or citing authors; in social sciences and humanities also USA has their nationally oriented literatures. The paper examines the extent to which nationally oriented journals entering Scopus in earlier years, have become in recent years more international. It is found that in the study set about 40 per cent of such journals does reveal traces of internati
Non-alcoholic fatty liver disease (NAFLD) and its progressive subtype non-alcoholic steatohepatitis (NASH) are the most prevalent liver diseases, often leading to hepatocellular carcinoma (HCC). This review aims to describe the present knowledge of the risk factors responsible for the development of NAFLD and NASH. I performed a literature review identifying studies focusing on the complex pathogenic pathway and risk factors of NAFLD and steatohepatitis. The relationship between NAFLD and metabolic syndrome is well established and widely recognized. Obesity, dyslipidemia, type 2 diabetes, hypertension, and insulin resistance are the most common risk factors associated with NAFLD. Among the components of metabolic syndrome, current evidence strongly suggests obesity and type 2 diabetes as risk factors of NASH and HCC. However, other elements, namely gender divergences, ethnicity, genetic factors, participation of innate immune system, oxidative stress, apoptotic pathways, and adipocytokines, take a leading role in the onset and promotion of NAFLD. Pathophysiological mechanisms that are responsible for NAFLD development and subsequent progression to NASH are insulin resistance and hyperinsulinemia, oxidative stress, hepatic stellate cell (HSC) activation, cytokine/adipokine signaling pathways, and genetic and environmental factors. Major pathophysiological findings of NAFLD are dysfunction of adipose tissue through the enhanced flow of free fatty acids (FFAs) and release of adipokines, and altered gut microbiome that generate proinflammatory signals and cause NASH progression. Understanding the pathophysiology and risk factors of NAFLD and NASH; this review could provide insight into the development of therapeutic strategies and useful diagnostic tools.
Liver fibrosis, that is, excessive accumulation of extracellular matrix protein, occurs and is the wound‐healing response and common final pathway of various chronic liver diseases. Advanced hepatic fibrosis caused by chronic liver inflammation eventually progresses to cirrhosis, and prognosis and management of chronic liver diseases depend on the fibrotic severities. Therefore, the early and precise evaluation of severity and status of liver fibrosis provides useful information for diagnosis as well as treatment planning and treatment efficacy and prognosis. Although invasive liver biopsy is the gold standard to assess the nature and severity of hepatic fibrosis, it has several recognized limitations including sampling error and inter‐observer variability in interpretation and staging. Furthermore, the dynamic process of fibrosis resulting from progression and regression is difficult to capture with biopsy alone. Therefore, alternative, simple, reliable, and noninvasive direct and indirect serum markers able to predict the presence of significant fibrosis or cirrhosis in patients with chronic liver disease with considerable accuracy were needed. The hepatology experts are actively researching noninvasive methods of fibrosis quantification. The aims of this chapter were to review the nature and limitations of the several noninvasive methods for the assessment of presence and severity of liver fibrosis in patients with chronic liver disease.
Prion diseases are invariably fatal and highly infectious neurodegenerative diseases affecting humans and animals. By now there have not been some effective therapeutic approaches to treat all these prion diseases. In 2008, canine mammals including dogs (canis familials) were the first time academically reported to be resistant to prion diseases (Vaccine 26: 2601--2614 (2008)). Rabbits are the mammalian species known to be resistant to infection from prion diseases from other species (Journal of Virology 77: 2003--2009 (2003)). Horses were reported to be resistant to prion diseases too (Proceedings of the National Academy of Sciences USA 107: 19808--19813 (2010)). By now all the NMR structures of dog, rabbit and horse prion proteins had been released into protein data bank respectively in 2005, 2007 and 2010 (Proceedings of the National Academy of Sciences USA 102: 640--645 (2005), Journal of Biomolecular NMR 38:181 (2007), Journal of Molecular Biology 400: 121--128 (2010)). Thus, at this moment it is very worth studying the NMR molecular structures of horse, dog and rabbit prion proteins to obtain insights into their immunity prion diseases. This article reports the findings of th
Liver cirrhosis is a major global health problem causing millions of deaths annually, and timely detection with aggressive treatment can significantly improve patients' quality of life. Modelling complex diseases from biomedical data is computationally challenging due to high dimensionality, strong feature correlations, noise, and limited labelled samples. Conventional Machine Learning (ML) pipelines often struggle with robustness, interpretability, and generalisation under such conditions. In this study, we propose an ML-driven multi-stage decision framework for complex disease modelling and therapeutic exploration. The framework integrates single-cell transcriptomic profiling, high-dimensional network-based feature stabilisation, multi-model learning, deep representation construction, and post-hoc decision support. Specifically, single-cell sequencing data were analysed to identify key cellular subpopulations, followed by high-dimensional weighted gene co-expression network analysis (hdWGCNA) to stabilise gene modules under sparsity and noise. To enhance non-linear feature interaction modelling, tabular molecular features were restructured into two-dimensional disease maps and an
Following the obesity epidemics, nonalcoholic fatty liver disease (NAFLD) has grown in prevalence and become a main cause of morbidity and death, intimately linked to cardiovascular disease, cancer, and cirrhosis. The key factor in the evolution of NAFLD is thought to be oxidative stress. Because most patients cannot change their lifestyle or dietary habits, a pharmaceutical strategy is now required to treat NAFLD. Nonalcoholic fatty liver disease (NAFLD), including nonalcoholic steatohepatitis, is treated with vitamin E. (NASH). Vitamin E is also a powerful antioxidant that has been demonstrated to lower oxidative stress in people with NAFLD. Thymol is a monoterpene phenol with a variety of pharmacological effects, however its anti-fatty liver properties have yet to be investigated. Despite the fact that oxidative stress is thought to have a role in the etiology of nonalcoholic steatohepatitis, antioxidant therapies have not been well studied in the treatment of nonalcoholic steatohepatitis. The goal was to learn more about vitamin E and thymol's biological activities, with a particular emphasis on their therapeutic effectiveness in NAFLD. Four groups of thirty-two adult male rats were formed (healthy control, thymol, Vit E, and fatty liver). For 28 days, rats were given either oral vitamin E (200 mg/kg) or thymol (50 mg/kg) randomly. The levels of ALT, AST, TNF- α, Ferritin, CK-MB enzymes, and MAPK gene expression were then determined in the serum. Based on a random effect model analysis, at the end of 28 days of therapy, ALT (41.43 U/L), AST (47.91 U/L), Ferritin (1.13 pg/dl), CK-MB (251.22 IU/L), TNF-α (95.39 pg/mL) (p≤0.001), and MAPK gene expression levels (p≤0.05) significantly reduced in both experimental groups compared with the fatty liver group. Vitamin E and thymol therapy is a safe, affordable, and effective therapeutic option in the fatty liver group. Patients with fatty liver disease should be encouraged to take vitamin E and Thymol supplements, which are both safe and affordable, because more effective new therapeutic options are lacking.
Endoscopy provides a major contribution to the diagnosis of the Gastrointestinal Tract (GIT) diseases. With Colon Endoscopy having its certain limitations, Wireless Capsule Endoscopy is gradually taking over it in the terms of ease and efficiency. WCE is performed with a miniature optical endoscope which is swallowed by the patient and transmits colour images wirelessly during its journey through the GIT, inside the body of the patient. These images are used to implement an effective and computationally efficient approach which aims to detect the abnormal and normal tissues in the GIT automatically, and thus helps in reducing the manual work of the reviewers. The algorithm further aims to classify the diseased tissues into various GIT diseases that are commonly known to be affecting the tract. In this manuscript, the descriptor used for the detection of the interest points is Speeded Up Robust Features (SURF), which uses the colour information contained in the images which is converted to CIELAB space colours for better identification. The features extracted at the interest points are then used to train and test a Support Vector Machine (SVM), so that it automatically classifies th
Background: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and fibrosis/portal hypertension. Methods: We assessed retrospectively healthy controls, non-advanced and advanced chronic liver disease (ACLD) patients using a 3D U-Net model for hepatic vessel segmentation on portal venous phase gadoxetic acid-enhanced 3-T MRI. Total (TVVR), hepatic (HVVR), and intrahepatic portal vein-to-volume ratios (PVVR) were compared between groups and correlated with: albumin-bilirubin (ALBI) and model for end-stage liver disease-sodium (MELD-Na) score, and fibrosis/portal hypertension (Fibrosis-4 [FIB-4] score, liver stiffness measurement [LSM], hepatic venous pressure gradient [HVPG], platelet count [PLT], and spleen volume). Results: We included 197 subjects, aged 54.9 $\pm$ 13.8 years (mean $\pm$ standard deviation), 111 males (56.3\%): 35 healthy controls, 44 non-ACLD, and 118 ACLD patients. TVVR and HVVR were highest in controls (3.9; 2.1), intermediate in non-ACLD (2.8; 1.7), and lowest in ACLD patients (2.3