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.

Actively identifying the risk factors and predictive indicators associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) can enable early diagnosis and treatment, which is of great significance for prolonging the survival of patients with LC. Hemodynamic disturbances, advanced LC, vascular endothelial injury, and mutations in thrombophilic genetic factors are established risk factors for PVT-LC. Venous dilatation and decreased blood flow velocity contribute to hemodynamic disturbances. The severity of LC can be assessed by the degree of portal hypertension, liver metabolic function biomarkers, and validated liver scoring systems. Iatrogenic interventions, endotoxemia, and metabolic syndrome may induce vascular endothelial injury and hypercoagulability, the latter of which can be quantified via coagulation-anticoagulation-fibrinolysis biomarkers. Mutations in thrombophilic genetic factors, such as Factor V Leiden, MTHFR C667T, and JAK2 V617F, disrupt coagulation-anticoagulation homeostasis and predispose patients to PVT-LC. This review specifically focuses on comprehensively delineating established risk factors and predictive indicators for PVT-LC, thereby providing a theoretical foundation for the construction of clinically applicable PVT predictive models to guide early interventions and improve the prognosis. Future research should further validate the associations between recently proposed risk factors and PVT-LC, while simultaneously establishing cutoff values for indicators with robust predictive value to construct a clinically applicable PVT prediction framework.

This review presents the latest evidence on the link between genetic single nucleotide polymorphisms and dental caries, highlighting key genes and pathways involved, introducing foundational concepts, and discussing essential methodological considerations for future research. Several genes have been identified as significantly associated with caries experience, including those related to tooth mineral tissues, taste perception, salivary composition and flow, and immune response. Epistatic interactions appear to be crucial in explaining genetic influence. Inconsistencies in the literature are attributed to variations in caries classification, age groups, ethnic backgrounds, limited statistical power, and linkage disequilibrium. Population stratification often confounds results, and few studies adequately control for genetic ancestry. Ensuring Hardy-Weinberg equilibrium and accounting for linkage disequilibrium are essential to avoid bias. Bonferroni corrections for multiple comparisons are fundamental but rarely applied, contributing to inconsistent findings. In conclusion, genetic epidemiology studies suggest that dental caries has a genetic component, accounting for significant individual differences in disease risk.

Metabolic syndrome (MetS) is associated with a plethora of different comorbidities. Exploring its key molecular mechanisms, such as advanced glycation end product and its receptor (AGE/RAGE) pathway, holds great potential. Numerous sources agree that targeting the AGE/RAGE pathway is a potential therapeutic strategy for MetS. However, the regulation of AGE/RAGE by microRNAs (miRNAs) in the context of MetS is still poorly understood. This review aimed to provide a systematic picture of the influence of miRNAs on AGE/RAGE in the context of MetS, with a particular focus on its ligands and receptors. This review achieves this in two ways: through an inductive “bottom-up” approach realized by a classical descriptive literature search, and through a deductive/synthetic “top-down” approach based on carefully selected miRNA profiling studies in MetS and its comorbidities. Although the initial inductive approach allowed the identification of some miRNAs of interest, almost all articles on this topic focus on the regulation of processes exclusively involved in atherogenesis. The new deductive approach has broadened the research horizon: It has enabled the discovery of new promising miRNAs and allowed for ranking different comorbid pathologies in MetS according to the degree of miRNA dysregulation of AGE/RAGE. Thus, in addition to atherosclerosis, significant miRNA dysregulation of AGE/RAGE was also described in MetS, particularly in immune cells, as well as in subcutaneous adipose tissue in obesity. This review, along with the novel approaches to systematizing the data contained therein may contribute to the understanding of MetS pathogenesis and the search for targets for the treatment of MetS.

Recent advancements in cancer immunotherapy have highlighted glypican-3 (GPC3) as a prominent target for treating hepatocellular carcinoma (HCC). However, approximately 10% to 30% of HCC patients exhibit low or absent GPC3 expression on the surface of tumor cells, which limits the feasibility of GPC3-targeted therapies. Consequently, it is essential for patients to undergo pre-diagnostic assessments of GPC3 expression in tumor cells to evaluate their suitability for GPC3-directed therapy. Although various methods have been developed to specifically detect GPC3 as a biomarker for treatment and prognosis, the diagnostic approaches currently employed in clinical studies remain relatively limited. Here, we provide a comprehensive overview of the clinical development of GPC3-targeted therapeutics, clinical trials in GPC3-positive HCC, and current methods for detecting GPC3 expression, highlighting their advantages and limitations. Furthermore, we explore the potential of integrating targeted therapy with various GPC3 detection modalities tailored to different pathological stages. This integration not only provides insights into the selection of effective methods for detecting GPC3 expression but also has the potential to significantly improve the clinical outcomes of patients with liver cancer. By simultaneously assessing the advantages and disadvantages of these methods, this review aims to establish a theoretical foundation for the clinical selection of appropriate GPC3 detection strategies for targeted therapy.

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.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with marked phenotypic differences observed among its major histological subtypes, adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung cancer (SCLC), in both clinical presentation and therapeutic response. In recent years, metabolomics has emerged as a powerful tool for studying cancer metabolic reprogramming, providing new insights into the metabolic distinctions among lung cancer subtypes. This review summarizes recent research advances in the metabolomics of ADC, SCC, and SCLC. Studies have revealed that ADC and SCC display distinct metabolic profiles in lipid metabolism, amino acid metabolism, and cell membrane synthesis, while SCLC demonstrates a unique metabolic pattern. Through metabolomic technologies, particularly mass spectrometry and liquid chromatography, it is possible to effectively differentiate lung cancer subtypes and identify potential biomarkers for early diagnosis and personalized treatment. This review also explores the clinical potential of metabolomics in lung cancer, emphasizing its critical role in early diagnosis and subtype stratification. These methodological advances establish a robust foundation for precision oncology paradigms in thoracic malignancies.