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.

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.

Immunothrombosis, the interplay between immune activation and coagulation, contributes to disease progression in inflammatory disorders. Its role in hepatitis B virus–related acute-on-chronic liver failure (HBV-ACLF) and the involvement of neutrophil extracellular traps (NETs) remain unclear. This study aimed to elucidate NETs-mediated immunothrombosis in HBV-ACLF.

Liver single-cell RNA sequencing data from HBV-ACLF patients and healthy controls were analyzed to define immune and endothelial transcriptional profiles. A cohort of 46 HBV-ACLF patients, 20 chronic hepatitis B patients, and 20 healthy controls was assessed for circulating NETs, endothelial injury markers, and coagulation parameters. Histopathology and in vitro assays examined NETs distribution and endothelial interactions.

NETs were markedly elevated in HBV-ACLF and correlated with endothelial injury markers (syndecan-1, von Willebrand factor, soluble thrombomodulin), coagulopathy, and prognostic scores. Histology revealed NETs colocalization with endothelial cells and platelets within hepatic microthrombi. NETs from patient neutrophils impaired endothelial integrity and enhanced procoagulant activity in vitro. Mechanistically, toll-like receptor 2 (TLR2) and complement component 5a receptor 1 (C5aR1) signaling were involved in NETs formation, and their pharmacological inhibition reduced NETs generation.

NETs are associated with endothelial injury and immunothrombosis in HBV-ACLF. Mechanistic analyses suggest a role for TLR2 and C5aR1 pathways in NETs formation, indicating potential targets for future therapeutic investigation.

Synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy bodies, and multiple system atrophy (MSA), are a group of neurodegenerative diseases characterized by the oligomerization of α-synuclein protein in neurons or glial cells. Various splicing isoforms of α-synuclein have been described, each with different aggregation properties. The α-synuclein gene (SNCA) has been identified as a highly significant genetic risk locus associated with various synucleinopathies across populations. This study aimed to assess the association of SNCA genetic variants with MSA and the levels of SNCA transcripts in peripheral blood mononuclear cells (PBMCs) from MSA and PD patients.

In this retrospective case–control study, 96 MSA patients, 1086 PD patients, and 485 healthy volunteers were included. PCR followed by restriction endonuclease analysis was used to detect four SNCA single-nucleotide polymorphisms (rs356219, rs3756063, rs11931074, and rs356168) in these individuals. In addition, RT-qPCR was performed to detect the levels of α-synuclein transcripts (SNCA mRNA isoforms -140, -126, and -112) in PBMCs of 24 MSA patients (including parkinsonian (MSA-P) and cerebellar (MSA-C) variants), 31 PD patients, and 32 healthy volunteers.

The frequency of the ‘T’ allele (of rs11931074) was significantly higher in MSA patients than in the healthy controls. The level of SNCA-140 mRNA was significantly decreased in MSA and PD patients compared with the controls, while the level of SNCA-112 mRNA was significantly increased in MSA-P patients than in PD patients and the controls. SNCA-112 mRNA/SNCA-140 mRNA and SNCA-112 mRNA/SNCA-126 mRNA ratios were significantly increased in MSA patients than in the controls.

The SNCA rs11931074 polymorphism is associated with MSA. There is a pronounced alteration in the expression of SNCA transcripts in PBMCs of MSA and PD patients.

Early risk stratification of severe acute liver injury (SLI) that may progress to acute liver failure (ALF), is vital for timely intervention, but no universal prognostic assessment tool covers both conditions. This study aimed to develop a simplified prognostic model for early risk assessment in SLI/ALF patients.

A retrospective cohort study consecutively enrolled SLI patients (including those progressing to ALF) from July 1, 2020 to May 31, 2025. Baseline clinical and laboratory data on admission were collected, with 90-day transplant-free survival as the primary outcome. Independent prognostic factors were screened via Cox regression to build a simplified scoring model, whose performance was compared with the Model for End-Stage Liver Disease (MELD), King’s College Criteria (KCC), and the Acute Liver Failure Study Group Prognostic Index (ALFSG-PI).

Of 302 patients, 190 (62.9%) achieved 90-day transplant-free survival. Multivariate Cox regression identified international normalized ratio (hazard ratio [HR]: 1.118, 95% confidence interval [CI]: 1.050–1.191), platelet count (HR: 0.995, 95% CI: 0.993–0.998), and hepatic encephalopathy grade ≥ 2 (HR: 5.187, 95% CI: 3.403–7.907) as independent predictors, forming the HIP (derived from the above-mentioned three predictors) model. It showed good discrimination (area under the receiver operating characteristic curve [AUC]: 0.82), outperforming MELD (AUC: 0.76, P = 0.019) and KCC (AUC: 0.72, P = 0.002), and performing comparably to ALFSG-PI (AUC: 0.80, P = 0.429). The model also performed robustly in ALF subgroups defined by the American College of Gastroenterology and the 2024 Chinese Medical Association guidelines (AUCs: 0.80 and 0.76, respectively) and achieved an AUC of 0.85 in the validation set.

The HIP model is a simple and effective tool for prognostic risk stratification in SLI/ALF patients, suitable for emergency and primary care to facilitate timely intervention.

Hepatocellular carcinoma (HCC) remains one of the most fatal cancers, primarily due to late diagnosis and the lack of effective early biomarkers. Recent advances in multi-omics and liquid biopsy technologies hold promise for improving early detection, prognostication, and monitoring of HCC. Understanding the immune landscape of HCC through genetic and epigenetic signatures is essential for identifying therapeutic targets and improving immunotherapy outcomes. This review aims to present current findings on immune-related biomarkers, multi-omics strategies, and biomarker validation in HCC. It also aims to evaluate the role of liquid biopsy and gene signatures in predicting treatment responses, with a specific focus on their applications in immunotherapy. The goal is to provide a comprehensive framework for integrating these emerging tools into clinical practice. The integration of multi-omics approaches has led to the identification of robust gene signatures that predict HCC prognosis and response to immune checkpoint inhibitors. Liquid biopsy technologies, including circulating tumor DNA, provide non-invasive alternatives for monitoring tumor evolution and therapeutic responses. Despite promising results, challenges remain in clinical validation, particularly in cross-platform reproducibility and the interpretation of complex multi-omics data. While genetic biomarkers are rapidly advancing, their clinical application in personalized medicine remains hindered by technical and ethical challenges, such as data privacy, informed consent, and method standardization. The integration of multi-omics data and liquid biopsies offers a promising path toward real-time, personalized treatment and the development of universal prognostic signatures for HCC. However, successful clinical adoption depends on cross-disciplinary collaboration to standardize data protocols and overcome challenges regarding accuracy, reproducibility, and patient privacy.

The immunosuppressive tumor microenvironment (TME) limits immunotherapy efficacy in intrahepatic cholangiocarcinoma (ICC). Understanding the molecular drivers of this TME is essential for developing new therapies. This study aimed to identify novel oncogenes that modulate the immune landscape of ICC using a multi-omics approach.

We integrated transcriptomic and proteomic data from our ICC cohorts with public datasets (TCGA-CHOL, GSE107943, OEP002768) to identify genes co-upregulated with PD-L1 (CD274). Single-cell RNA sequencing (scRNA-seq) was used to analyze cell-type-specific expression and intercellular communication. Clinical significance was validated through tissue microarrays and multiplex immunofluorescence in an independent ICC cohort.

Multi-omics screening identified TACC3 as a key candidate in ICC. Elevated TACC3 expression in ICC tissues correlated with poor prognosis and promoted tumor cell proliferation and migration. TACC3 activated the STAT3 pathway, increasing PD-L1 transcription. scRNA-seq showed TACC3/PD-L1 interaction in malignant epithelial cells, with PD-L1 co-expressed with FOXP3 in regulatory T cells (Tregs). Cell–cell communication analysis predicted strong interactions between malignant cells and Tregs. TACC3 knockdown reduced PD-L1 expression and inhibited STAT3 and AKT phosphorylation. Clinical validation confirmed co-expression of TACC3, PD-L1, and FOXP3, with high TACC3 levels linked to worse clinicopathological features and shorter progression-free survival.

Our study defines a TACC3-STAT3-PD-L1 axis driving immunosuppression in ICC. TACC3 fosters an immunosuppressive TME by upregulating PD-L1 and is associated with a Treg-rich contexture, suggesting that TACC3 may serve as a potential therapeutic target to overcome ICC immunosuppression.