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.

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.

Vulvovaginal candidiasis, an infection caused by an abnormal proliferation of Candida species in the vagina and vulva, is particularly relevant, affecting up to 75% of women of reproductive age. Because of antifungal drug resistance, a significant number of plants are used to treat vaginal candidoses in Cameroon. Thus, the scientific validation of the use of these plants in treating candidiasis is valuable. This study sought to identify medicinal plants used to treat vaginal infections in the Dschang district and evaluate the antifungal activity of the most promising plants on five Candida species.

The ethnobotanical survey was conducted in Dschang (Menoua Division, West Cameroon) through individual interviews using a semi-structured questionnaire. Extracts from seventeen plants were obtained by maceration using water or a water–ethanol solution (3:7; v/v). Antifungal activity was evaluated using the microdilution method.

Forty-eight plants belonging to 33 families were identified as treating vaginal infections. Decoction and formulation of ovules were the prevalent modes of plant preparation, with leaves and bark being the predominant plant organs used. Out of thirty-four extracts tested, two (CSEHAlc and MIEHAlc) showed antifungal activity, with minimum inhibitory concentrations ranging from 0.315 to 2.5 mg/mL. The determination of the minimum fungicidal concentrations revealed the fungicidal orientation of these bioactive extracts.

This study identifies medicinal plants used to treat vaginal infections in Dschang and their modes of preparation. The in vitro antifungal screening of selected plants indicated Mangifera indica and Canarium schweinfurthii as the anti-Candida plants that can be further exploited for antifungal drug discovery.

Early and accurate prognostic assessment is crucial to avoid serious disease progression in patients with liver failure. Thyroid hormone is an important metabolic regulator involved in hepatic function. This review examines the pathophysiological regulation in detail of the hypothalamic-pituitary-thyroid axis in patients with liver failure and emphasizes the importance of thyroid profiling (thyroid-stimulating hormone, T3, and T4) in prognostic assessment and risk stratification. T3 can enhance liver regeneration. The clinical application of thyroid hormone replacement therapy in patients with acute-on-chronic liver failure complicated by non-thyroidal illness syndrome is controversial. This review aims to inform clinical practice regarding the relevance of thyroid hormone level assessment in liver failure and to provide novel insights into the prognostic evaluation and comprehensive care of liver failure complicated by thyroid dysfunction.

Fast inverse planning in radiosurgery planning is limited by an excessive number of isocenters, which is clinically hypothesized to be driven by the morphological irregularity of the target volume. This retrospective cross-sectional study aimed to empirically evaluate this hypothesis in vestibular schwannoma cases.

Consecutive patients diagnosed with vestibular schwannoma and receiving Gamma Knife radiosurgery in 2023 were included, and their treatment plans designed using the GammaPlan planning system were collected. Morphological irregularity–related parameters, including standard sphericity (SS), volume ratio sphericity (VRS), and the coefficient of variance of diameters (DCV), were calculated based on parameters provided by the system. Basic demographic and clinical data were collected to evaluate their impact on sphericity. The effects of different sphericity assessment methods on common treatment plan parameters were analyzed.

Treatment plans of 280 patients with vestibular schwannoma were collected. The SS, VRS, and DCV of the tumors were 0.85 (0.77–0.91), 0.46 ± 0.16, and 0.22 (0.14–0.34), respectively. Multivariate analysis showed that lesion volume, acoustic neuroma consensus on systems for reporting results grade, and age were significant factors influencing sphericity. All other planning parameters, except prescription dose and homogeneity index, were significantly correlated with sphericity. DCV was more closely correlated with SS than with VRS.

DCV may serve as a simple quantitative metric of target morphological irregularity, showing strong consistency with SS. Incorporating morphological irregularity into Gamma Knife treatment plan evaluation may help improve future planning strategies and support optimization of isocenter utilization.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major global health concern and encompasses a spectrum ranging from hepatic steatosis and metabolic dysfunction-associated steatohepatitis to liver fibrosis, cirrhosis, and ultimately hepatocellular carcinoma. Insulin resistance, the pathogenic cornerstone of MASLD, drives enhanced peripheral lipolysis and increased hepatic de novo lipogenesis, thereby overloading the liver with lipids and inducing steatosis. Subsequent lipotoxicity, inflammation, and gut microbiota dysbiosis further exacerbate disease progression. The gut microbiota and their metabolites communicate with the liver via the gut-liver axis, forming a complex signaling network that directly or indirectly modulates hepatic metabolism, systemic immune responses, oxidative stress, and intestinal barrier integrity. In this review, we synthesize evidence for the beneficial and detrimental effects of the major human gut microbial communities and their metabolites during the course of MASLD. We delineate how these gut-derived factors regulate hepatic function through an integrated tripartite “gut-liver axis–oxidative stress–metabolic reprogramming” mechanism. These insights may inform microbiome-based precision interventions and accelerate the development of therapeutic strategies targeting MASLD.

Chronic hepatitis B (CHB) remains a major global public health challenge. Current therapies based on nucleos(t)ide analogues and interferon mainly achieve long-term viral suppression, whereas only a small proportion of patients attain a functional cure, defined as sustained hepatitis B surface antigen loss with hepatitis B virus (HBV) DNA below the limit of quantification for at least 24 weeks after treatment discontinuation, with or without anti-HBs seroconversion. Emerging evidence from the gut–liver axis indicates that gut microbiota–derived metabolites, particularly short-chain fatty acids (SCFAs) and bile acids (BAs), modulate the HBV life cycle and immune regulation in CHB, thereby offering therapeutic targets to overcome immune tolerance. This review summarizes the biological characteristics of SCFAs and BAs and their mechanistic roles across different stages of HBV infection, with emphasis on translational relevance. In vitro and animal studies suggest that butyrate and related SCFAs suppress HBV gene expression by inhibiting histone deacetylases and remodeling covalently closed circular DNA minichromatin. SCFAs may also enhance antiviral immunity, although they may reinforce immune tolerance in certain contexts. For BAs, the farnesoid X receptor, Takeda G protein–coupled receptor 5, and the HBV entry receptor sodium taurocholate cotransporting polypeptide form a key signaling hub with dual effects on viral replication and host responses. Early-phase studies suggest that farnesoid X receptor agonists, pegylated interferon-α, or nucleos(t)ide analogues are associated with hepatitis B surface antigen reductions, though larger trials are needed. This review proposes biomarker-guided stratification and multi-target combination strategies to improve functional cure rates in CHB.