Cirrhotic cardiomyopathy (CCM) is a significant complication of cirrhosis, but its progression and underlying mechanisms remain incompletely understood. This study aimed to investigate dynamic changes in cardiac function, pathology, inflammation, and mitochondrial damage in a mouse model of CCM, and to compare echocardiographic characteristics in patients with cirrhosis.

Bile duct ligation was performed in male C57BL/6J mice to induce cirrhosis. Longitudinal analyses were conducted over eight weeks. Cardiac function was assessed using serum biomarkers, echocardiography, and electrocardiography. Pathology was examined with hematoxylin and eosin, Masson’s trichrome, Sirius Red, and wheat germ agglutinin staining. Western blotting and immunohistochemistry were used to detect markers of inflammation, fibrosis, apoptosis, and mitochondrial function. Cardiac and liver function markers were also evaluated in patients with cirrhosis.

Mice subjected to bile duct ligation developed progressive cardiac dysfunction, including reduced cardiac output and diastolic dysfunction (end-diastolic interventricular septal thickness, left ventricular internal diameters, stroke volume, and left ventricular end-diastolic volume decreased, whereas ejection fraction and fractional shortening increased), as well as cardiac atrophy. Myocardial apoptosis, inflammation (elevated tumor necrosis factor, interleukin-6, and p65), and fibrosis worsened over time. Mitochondrial injury was characterized by reduced carnitine palmitoyltransferase 1A and peroxisome proliferator-activated receptor alpha, with increased hexokinase 2, pyruvate kinase M2, and lactate dehydrogenase A. In patients with cirrhosis, impaired cardiac function and elevated brain natriuretic peptide levels correlated with total bilirubin.

The progression of CCM is closely associated with cirrhosis severity and appears to be driven by myocardial atrophy, apoptosis, inflammation, fibrosis, and mitochondrial dysfunction.

Normalization can standardize and improve machine learning (ML) performance on omics data. However, it is unclear whether normalization is associated with overfitting (i.e., worse cross-dataset performance than intra-dataset performance). Therefore, we aimed to examine associations of normalization and regularization with overfitting of ML on omics data.

Using three paired transcriptomic and clinical datasets (lung adenocarcinoma: the Cancer Genome Atlas (TCGA)/Oncology Singapore; melanoma: TCGA/Dana-Farber Cancer Institute; glioblastoma: TCGA/Clinical Proteomic Tumor Analysis Consortium), we applied ANOVA-based gene selection methods, six normalization methods, and six ML models to classify cancer patients’ deaths. Balanced accuracy (BA) and area under the curve (AUC) in intra- and cross-dataset settings were compared using inferential analyses.

Normalization consistently improved intra-dataset performance (median BA/AUC changes: 0.035–0.214/0.115–0.279) on all data, particularly with Z_Raw, but decreased or slightly increased cross-dataset performance (median BA/AUC changes: −0.029–0.079/0.029–0.064). Least Absolute Shrinkage and Selection Operator (LASSO) model without normalization consistently outperformed most of the ML models in cross-dataset testing across cancer types. ML models on all and molecular-alone data showed similar best performances.

Normalization increases ML’s intra-dataset performance and overfitting in three paired cancer transcriptomic and clinical datasets. Regularized models such as LASSO appear to mitigate overfitting and achieve robust cross-dataset performance. Therefore, cross-dataset evaluation and regularized models are recommended to assess and reduce overfitting, while normalization should be used cautiously. Adding clinical data seems to have little impact on ML models’ performance. However, future work on other diseases and datasets is warranted.

Comparative data on sequential transarterial chemoembolization (TACE) after stereotactic body radiation therapy (SBRT) in recurrent hepatocellular carcinoma (HCC) remain limited. This study aimed to evaluate the efficacy of this combination.

We retrospectively reviewed 152 patients with recurrent HCC who met predefined eligibility criteria; 109 received SBRT alone and 43 received SBRT plus TACE. To minimize selection bias, a 2:1 propensity score matching was performed, resulting in 68 patients in the SBRT-alone group and 36 in the SBRT plus TACE group for the final comparative analysis. Overall survival, progression-free survival, and local control were assessed using the Kaplan-Meier method.

The SBRT plus TACE group was associated with numerically higher survival rates, although this difference did not reach statistical significance. The cumulative one-, three-, and five-year overall survival rates were 91.2%, 76.3%, and 61.8% for SBRT alone, compared to 100.0%, 86.1%, and 77.5% for the combination therapy ( p = 0.069). The corresponding progression-free survival rates were 73.1%, 51.1%, and 32.3% versus 88.9%, 58.1%, and 52.3% ( p = 0.091). No acute grade ≥3 toxicities were observed in either group.

In this exploratory analysis of recurrent HCC, the combination of SBRT and TACE demonstrated a favorable trend toward improved survival compared with SBRT alone, without an increase in severe toxicity. While these findings did not reach statistical significance, they establish the safety profile of the combined approach and provide preliminary evidence supporting its potential therapeutic role. This hypothesis-generating study justifies and informs the design of larger, prospective trials to definitively evaluate the efficacy of this regimen.

Insulin resistance is a common extrahepatic manifestation of hepatitis C virus (HCV) infection (HCVi), but its mechanism is poorly understood. While systemic insulin resistance is documented, portal insulin dynamics, a key regulator of hepatic metabolism, remain unexplored. This study aimed to investigate the relationship between insulin, the gut-liver axis, and immunometabolic changes in patients with HCV.

HCV patients were evaluated before (HCVi; n = 29) and after sustained virologic response (SVR) achieved with sofosbuvir/velpatasvir treatment (SVR, n = 23) (NCT02400216). Liver biopsies, portal blood, and peripheral blood were collected at both phases. Statistical analyses were conducted using Wilcoxon rank-sum tests, Mann-Whitney tests, and Pearson’s correlation coefficients to assess differences and associations across insulin, glucose, cytokines, metabolites, immune cells, and hepatic liver transcriptomics to elucidate impaired insulin homeostasis in HCVi.

HCV patients had significantly reduced portal insulin compared to SVR (p = 0.02), while peripheral insulin, portal glucose, and peripheral glucose remained unchanged. Portal insulin correlated positively with proinflammatory cytokines and vascular injury markers and negatively with CD8/CD62L/CD45RA/CD3 cells (naive cytotoxic T-cells) and non-standard nucleotides. Hepatic transcriptomic analysis revealed portal insulin correlated positively with immune and negatively with amino acid pathways, reflecting insulin’s role in the perturbations of immunometabolism during HCVi.

Lower portal insulin during HCVi is associated with changes consistent with altered pancreatic insulin secretion and decreased hepatic insulin extraction. The observed correlations support a potential relationship between the immune response and insulin dynamics, indicating an interplay between the immune system, metabolism, and insulin in HCVi, with clinical implications for the management of dysglycemia.

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignant tumors globally, with a notably low five-year survival rate. Its high mortality is largely attributed to challenges in early detection. Extracellular vesicles (EVs) are naturally occurring nanoparticles secreted by nearly all cell types and carry a diverse array of bioactive molecules, including proteins, nucleic acids (particularly non-coding RNAs), and lipids. EVs play pivotal roles in remodeling the tumor microenvironment and driving cancer progression through intercellular communication. Accumulating evidence has established that EVs are critically involved in the pathogenesis of HCC and are emerging as promising biomarkers for its early detection. With advances in EV isolation technologies, these vesicles have garnered considerable attention in the field of liquid biopsy for HCC. This review provides a comprehensive overview of the diagnostic potential of EV-derived biomarkers in HCC, including DNA, RNA, proteins, and lipids. Additionally, it discusses the advantages of integrating multi-omics approaches for HCC diagnosis. Furthermore, the review highlights the technical challenges in EV isolation and characterization, as well as the crucial role of reference genes in the standardization of EV data. These insights underscore the potential of EVs as novel, minimally invasive liquid biopsy biomarkers for the early diagnosis of HCC.

Pancreatic fibrosis, a major pathological feature of chronic pancreatitis, is primarily driven by the abnormal activation of pancreatic stellate cells (PSCs) and excessive deposition of extracellular matrix. Traditional Chinese medicine (TCM) offers a holistic and synergistic approach to preventing and treating pancreatic fibrosis through multi-target regulation of PSC activation. This review systematically elucidates the mechanisms by which TCM—encompassing both bioactive monomers and compound formulations—modulates key signaling pathways involved in PSC activation, including the mitogen-activated protein kinase, transforming growth factor-β/Smad, platelet-derived growth factor, nuclear factor kappa B, and Wingless/β-catenin pathways. By simultaneously targeting these interconnected signaling networks, TCM strategies effectively inhibit PSC activation, attenuate inflammatory responses, and reduce extracellular matrix deposition. In contrast to single-target pharmacological inhibitors, TCM embodies a “multi-component, multi-pathway” therapeutic paradigm that aligns with the complex pathophysiology of pancreatic fibrosis. This review also draws comparative insights from liver fibrosis, highlighting conserved pathways and organ-specific regulatory contexts. Ultimately, TCM represents a promising integrative avenue for the prevention and treatment of pancreatic fibrosis, supported by growing preclinical evidence and aligned with the principles of holistic intervention.

Alpinia calcarata (A. calcarata) Roscoe (Family: Zingiberaceae) is a rhizomatous perennial herb used in traditional medicine to treat inflammatory conditions. This study aimed to develop a topical emulgel dosage form by incorporating the essential oil of A. calcarata rhizome and to investigate it’s in vitro anti-inflammatory activity. A thin-layer chromatographic fingerprint of the essential oil of A. calcarata rhizome was developed. Then, an emulsion base containing plant oil was formulated and incorporated within a Carbopol gel base. The physical characteristics of this formulation were evaluated subsequently. The anti-inflammatory mechanism of the emulgel was determined by in vitro blood cell membrane stabilization assay and thrombolytic activity assay. The results were statistically analyzed by one-way analysis of variance. The thin-layer chromatographic fingerprint of the test oil demonstrated several bands with unique retention factor values. The formulated herbal emulgel was white, viscous, and homogeneous in appearance. The spreadability was 118 g·cm/M, and the pH of the emulgel was 6.30 at 25°C. The A. calcarata emulgel significantly (p < 0.05) inhibited heat-induced in vitro hemolysis, with the highest activity at a 50 µg/mL dose (87.68 ± 0.35%) compared to the placebo. Furthermore, this activity was found to be dependent on the essential oil concentration (r2 = 0.99) of the emulgel. Therefore, it was concluded that the essential oil of A. calcarata rhizome is an effective active ingredient to be used in a topical emulgel formulation, whereas the diverse phytochemicals present in the essential oil would be the underlying source of its anti-inflammatory activity.

Breast cancer (BCA) is one of the most common cancers worldwide, with a high rate of mortality and morbidity in women. This review focuses on the applications of nanotechnology, nanomaterials, and nanoparticles (NPs) in BCA, encompassing diagnosis and therapy. Nanotechnologies, nanocarriers, and nano-encapsulations versus their corresponding counterparts for BCA diagnosis and therapy have been discussed. Various drug formulations into different nanocarriers (lipid NPs, nanoemulsions, polymeric NPs, and metal-based NPs) enhanced their bioavailability and therapeutic efficacy, overcoming the limitations of conventional formulations. Additionally, clinical specialists have achieved improved outcomes in the detection and monitoring of BCA at various stages using nanotechnology, ultimately leading to an improved quality of life for patients.

The rate of functional cure (HBsAg loss) remains unsatisfactory following pegylated interferon (PEG-IFN) treatment in chronic hepatitis B. To optimize PEG-IFN administration, this study aimed to evaluate virological markers to predict functional cure and/or hepatitis B e antigen (HBeAg) loss.

Relevant studies assessing virologic markers for predicting functional cure and HBeAg loss after PEG-IFN therapy were systematically retrieved from PubMed, Embase, the Cochrane Library, and Web of Science up to November 2023. Predictive effectiveness was evaluated via the summary receiver operating characteristic curve.

We analyzed 38 studies (6,179 patients). HBsAg decline at week 24 had the greatest discriminative ability according to the area under the receiver operating characteristic curve (AUROC) (0.89) and sensitivity (0.88) for predicting functional cure, whereas baseline HBsAg had a comparable AUROC (0.86) and highest specificity (0.79), with both being significantly better than baseline hepatitis B core-related antigen and hepatitis B virus (HBV) RNA (all P < 0.001). For HBeAg loss or seroconversion, HBV RNA, HBV DNA, HBeAg, and HBeAg decline at week 12, as well as HBV DNA and HBeAg decline at week 24, all exhibited comparable predictive values (AUROC = 0.75–0.78). HBV RNA and HBeAg levels at week 24 showed optimal sensitivity (0.87), and HBeAg decline at week 12 had the highest specificity (0.83).

HBsAg decline at week 24 and baseline HBsAg levels are better predictors of functional cure than novel virologic markers, while on-treatment HBV RNA and HBeAg levels and dynamic changes are the most reliable indicators for HBeAg loss.