The gut microbiota is crucial in maintaining host health and liver function. Fecal microbiota transplantation (FMT) has shown promising potential in treating chronic liver diseases. To help clinicians quickly master and standardize the clinical application of FMT for chronic liver disease, the Liver Related Digestive Diseases Group of the Chinese Society of Hepatology of the Chinese Medical Association has developed the “Expert Consensus on the Clinical Application of FMT for Chronic Liver Disease.” This consensus addresses the key aspects of FMT, including the indications, contraindications, efficacy, safety, donor selection, transplantation routes, precautions, and the prevention and management of adverse reactions for chronic liver conditions, such as chronic hepatitis, cirrhosis, and liver cancer, thereby offering reference and guidance to clinicians implementing FMT in the treatment of chronic liver disease.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning.

We conducted a cross-sectional study between November 2023 and May 2024 in Fortaleza, Ceará, Brazil. Based on the Framingham Cardiovascular Risk Scale, patients were categorized into three cardiovascular risk groups: Low (22 participants), Moderate (14 participants), and High (17 participants). From ECG signals at two different positions (ECG_Down and ECG_UP), 27 non-linear features were extracted using multi-band analysis. Additionally, 42 medical features provided by physicians were included. From a pool of 19 machine learning classifiers, models were trained and tested within a nested leave-one-out cross-validation procedure using information solely from ECG, solely from medical features, and combining both (multimodal), respectively, to distinguish between Low vs. Moderate, Low vs. High, Moderate vs. High, and All vs. All.

The multimodal model presented the best results for every comparison group, reaching (1) 88.89% Accuracy and 0.8831 area under the curve (AUC) for Low vs. Moderate; (2) 97.44% Accuracy and 0.9706 AUC for Low vs. High; (3) 93.55% Accuracy and an AUC of 0.9412 for Moderate vs. High; (4) 86.79% Accuracy and 0.9346 AUC for All vs. All.

The multimodal model outperformed single-source models in cardiovascular risk classification. ECG-derived non-linear features, especially from ECG_Down, were key drivers, with medical features adding complementary value. The results support its potential use in clinical triage and diagnosis.

Parenteral nutrition (PN)-associated cholestasis (PNAC) is frequently diagnosed in premature infants; however, not all PN-exposed infants develop PNAC. We propose that, in premature infants receiving PN and varying amounts of enteral feeds, differences in the gut microbiome and fecal bile acid content are associated with PNAC development. This study aimed to examine the fecal microbiome and bile acid content of premature infants on PN to determine if there is a relationship with the development of PNAC.

Twenty-two preterm infants had serial bilirubin measurements and fecal samples collected during their neonatal intensive care unit admission. Fecal samples underwent 16S rRNA gene sequencing and bile acid analysis. Binomial regression, adjusting for postmenstrual age with feed amount as a moderator, was used to assess the impact of the fecal microbiome and bile acids on PNAC development.

Cholestatic patients (n = 11) had greater PN and antibiotic exposure (p = 0.020; p = 0.010) and longer neonatal intensive care unit stays (p = 0.0038) than non-cholestatic patients. Microbiome richness was higher in non-cholestatic infants (p < 2E-16), with no difference in β diversity (p = 1.0). Cholestatic infants had a significantly higher abundance of Proteobacteria and Fusobacteriota and a lower abundance of Bacteroidota (p < 2E-16). Akkermansia was abundant in all infants on low feeds; as feed volume increased, Akkermansia abundance significantly increased in non-cholestatic infants (p < 2E-16). Bile acid analysis demonstrated significantly lower deoxycholic acid concentrations in cholestatic infants (p < 2E-16). Metagenomic analysis revealed an increase in Proteobacteria requiring augmented stress responses in non-cholestatic infants.

This is the first study to directly explore the relationship between PNAC susceptibility, the microbiome, and fecal bile acids in preterm infants. The microbiome and bile acid patterns identified here may inform the development of targeted therapeutics for this vulnerable population.

The COVID-19 pandemic profoundly impacted university students, presenting multifaceted challenges including the abrupt transition to virtual learning and significant disruptions to emotional well-being and dietary habits. This study aimed to investigate the dietary and nutritional characteristics associated with academic stress among Mexican university students during the COVID-19 lockdown.

This cross-sectional study was conducted with a sample of 114 university students in Mexico. Participants completed a self-reported questionnaire assessing dietary patterns, nutritional intake, and academic stress levels. Informed consent was obtained from all participants prior to data collection.

Among study participants (n = 114), 57.8% experienced moderate academic stress, while 25.7% reported high academic stress during the COVID-19 lockdown. Notably, 13.5% of students demonstrated food cravings that were significantly associated with increased consumption of red and fatty meats (P = 0.030) and sausages (P = 0.017). A negative virtual education experience was associated with food cravings towards high-calorie and saturated-fat foods (P = 0.014), as well as elevated academic stress levels (P = 0.009). Furthermore, high academic stress levels were positively associated with food cravings (P = 0.020), particularly towards carbohydrate-rich foods (P = 0.037).

The COVID-19 lockdown substantially disrupted the dietary habits and nutritional status of university students, with academic stress serving as a significant mediating factor.

Amoebiasis, or amoebic dysentery, is a gastrointestinal disorder caused by the parasite Entamoeba histolytica. The disease is endemic in parts of Africa, Asia, North and South America, leading to several deaths annually. Reported adverse effects associated with the current first-line treatment for amoebiasis, coupled with the evolution of resistance to it, call for the need to search for plant-based alternatives. This study systematically reviews medicinal plants with activity against Entamoeba histolytica.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to retrieve scholarly literature. The study reviewed 70 articles from 7 popular databases: Google Scholar, PubMed, ScienceDirect, Booksc.org, Emerald, Scopus, and MEDLINE, highlighting several plants with anti-amoebic properties.

The primary parts of the plant used in the treatment of Entamoeba histolytica were the leaves (61%), followed by rhizomes (13%), roots (8%), seeds (8%), stems (4%), and fruits (4%). The families Asteraceae (18%) and Zingiberaceae (18%) contain most plants that are effective against Entamoeba histolytica. These medicinal plants families are rich in phytochemicals such as terpenoids and flavonoids that have anti-entamoeba histolytica activity. Maceration is the most commonly used extraction method.

The results suggest that plants are a promising source of new agents to combat amoebiasis caused by Entamoeba histolytica. The most frequently used plant parts were leaves (61%), and the maceration method was the most common extraction technique due to its simplicity and cost-effectiveness. The majority of studies were limited to in vitro models, with only one plant (Adenophyllum aurantium) tested in vivo. Further research is needed to establish their mechanisms of action, toxicities, and clinical potential.

Epidemiological data on bacterial infections in cirrhosis in China remain limited. Therefore, we aimed to conduct a multicenter study to investigate the characteristics and outcomes of patients with cirrhosis and bacterial infections in China.

We retrospectively enrolled 1,438 hospitalized adult patients with cirrhosis and bacterial or fungal infections from 24 hospitals across China between January 2018 and September 2024. Data on demographics, clinical features, microbiology, treatment, and outcomes were collected.

A total of 1,783 infection episodes were recorded, including 1,668 first infections and 115 second infections. Most infections were community-acquired (86.6%). Pneumonia was the most common infection type (26.7%), followed by spontaneous bacterial peritonitis (19.5%) and spontaneous bacteremia (14.1%). Among 754 pathogens isolated from 620 patients, Klebsiella pneumoniae (20.1%) was nearly as common as Escherichia coli (21.7%). Multidrug-resistant (MDR) organisms accounted for 41.0% of all isolates, with extended-spectrum β-lactamase-producing Escherichia coli being the most prevalent MDR strain (8.9% of patients). Adherence to empirical antibiotic treatment guidelines from the European Association for the Study of the Liver was significantly lower in this cohort compared to the global study (21.5% vs. 61.2%, P < 0.001), accompanied by a lower clinical resolution rate (63.5% vs. 79.8%, P < 0.001).

The clinical and microbiological characteristics of bacterial infections in patients with cirrhosis in China differ substantially from those reported in other regions. These findings highlight the need for region-specific management and prevention strategies, particularly in light of the changing microbiological landscape, high MDR prevalence, and suboptimal antibiotic practices.

Radiotherapy remains one of the essential treatment modalities for brain gliomas, brain metastases, pediatric neuroblastomas, and primary central nervous system lymphomas. With continuous advancements in modern radiotherapy techniques, patients have achieved significantly improved local control rates and prolonged survival. However, the long-term complications associated with radiotherapy have become increasingly evident. Radiation-induced brain injury (RIBI) is a clinical syndrome characterized primarily by neurological dysfunction following focal or whole-brain radiotherapy. It negatively impacts patients’ quality of life and imposes a considerable burden on families and society. With the rapid development of medical imaging and artificial intelligence technologies, multimodal imaging techniques, including structural magnetic resonance imaging, diffusion-weighted imaging, functional magnetic resonance imaging, perfusion imaging, positron emission tomography-computed tomography metabolic imaging, and radiomics, have demonstrated significant potential for early detection, dynamic monitoring, and quantitative evaluation of RIBI. Meanwhile, treatment strategies for RIBI are shifting from traditional symptomatic and supportive care toward multidimensional interventions aimed at protecting the blood-brain barrier, modulating neuroinflammation, and implementing precise targeted therapies. Additionally, emerging studies have explored neuromodulation techniques and gut-brain axis regulation, offering new directions for the prevention and treatment of RIBI. Although conventional imaging methods remain valuable for diagnosing RIBI, they exhibit notable limitations in the early stages of the disease and in differentiating RIBI from tumor recurrence. This review focuses on the current state of technological development, key findings, and existing limitations, with the aim of providing a theoretical foundation and technical support for the early identification and precise intervention of RIBI.

Prognostic scores are valuable tools for predicting survival in patients with chronic liver disease. Recently, the albumin-bilirubin (ALBI) score has emerged as a potential prognostic indicator in liver-related conditions. This study aimed to compare the prognostic efficacy of the ALBI score with the Model for End-stage Liver Disease (MELD), MELD-Na+, and Child-Turcotte-Pugh (CTP) scores in predicting survival among patients with alcohol-associated liver disease (ALD).

This study included consecutive ALD patients admitted to the Medicine and Gastroenterology wards of MKCG Medical College and Hospital, Berhampur, Odisha, India, between November 2019 and November 2022. Upon hospitalization, baseline characteristics, clinical and laboratory parameters, ALBI, MELD, MELD-Na+, and CTP scores were recorded. The accuracy of these scores in predicting survival up to three years was compared.

A total of 490 ALD patients were included. Higher ALBI scores were observed in patients who died during hospitalization (p < 0.001), at 28 days (p < 0.001), 90 days (p < 0.001), six months (p < 0.001), one year (p < 0.001), two years (p < 0.001), and three years (p < 0.001), compared to those who survived. However, the area under the receiver operating characteristic (AUROC) curves showed that the ALBI score was inferior to MELD, MELD-Na+, and CTP scores in predicting survival at admission [AUROC: ALBI (0.719), MELD-Na+ (0.823), MELD (0.817), CTP (0.770)] and at three years [AUROC: ALBI (0.755), MELD-Na+ (0.787), MELD (0.758), CTP (0.784)]. Furthermore, Cox regression analysis revealed that components used in the MELD, MELD-Na+, and CTP scores—such as serum creatinine, serum sodium, and hepatic encephalopathy—were independent predictors of mortality, whereas the components of the ALBI score (serum albumin and serum bilirubin) were not.

All hospitalized ALD patients had a grade 3 ALBI score, with significantly higher scores observed among non-survivors compared to survivors. However, MELD, MELD-Na+, and CTP scores were superior to the ALBI score in predicting survival both during hospitalization and over a three-year follow-up period.