Development of mixed histiocytosis (Langerhans cell histiocytosis (LCH))/Erdheim–Chester disease (ECD)) after treatment in patients with an initial skull LCH lesion has not been well recognized. An elderly woman initially developed LCH at the left temporal bone, preceded by polyuria and polydipsia five years earlier; the lesion was surgically removed. Two years thereafter, she experienced her first LCH relapse with a right parietal skull lesion, in which a BRAF V600E mutation was confirmed, and chemotherapy was initiated. After a second LCH relapse involving the left parietal bone, the patient presented with a third relapse at the L2 vertebra. This lesion was pathologically diagnosed as mixed histiocytosis (LCH/ECD), resulting in refractoriness to conventional chemotherapy, and was successfully treated with targeted therapy using BRAF and MEK inhibitors. Spinal mixed histiocytosis (LCH/ECD) may develop following relapses of skull LCH after chemotherapy, for which targeted therapy could be effective.

The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) family regulates fundamental processes in both innate and adaptive immunity. Aberrant NF-κB activation, whether through canonical or non-canonical signaling pathways, contributes to chronic inflammation, autoimmunity, allergy, and primary immunodeficiency/autoinflammatory syndromes, while also influencing host defense and tissue repair mechanisms. The present review aims to synthesize molecular architecture, upstream triggers, ubiquitin-centered relay systems, and the dynamic regulation of NF-κB activity. The major findings on the NF-κB signaling pathway encompass its dual molecular mechanisms (canonical and non-canonical), its central roles in immune and inflammatory responses, cell survival, and development, as well as its complex regulatory networks. We interpret NF-κB as a master integrator of diverse signals, essential for both acute and long-term physiological processes. Dysregulation of NF-κB underlies many diseases, and while it is a promising therapeutic target, its ubiquitous functions demand precise modulation to avoid adverse effects. In conclusion, the proper function of the NF-κB signaling pathway is essential for maintaining cellular homeostasis and immune defense; its dysregulation is linked to chronic inflammatory diseases, autoimmune disorders, and cancer, which underscores the pathway’s significance as a therapeutic target. Although it elucidates molecular processes and treatment options, experimental validation of emerging therapeutic concepts such as ubiquitin code editing and spatial immunology remains limited.

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.

The optimal management strategy for adults with immune-tolerant (IT) chronic hepatitis B infection remains undefined. This study aimed to investigate the efficacy and predictive factors of a pegylated interferon (Peg-IFN)-based treatment strategy in IT patients with chronic HBV infection.

In this pilot, open-label, prospective study, 286 patients aged 18 to 60 years with IT characteristics were enrolled and allocated to one of three groups. The combination group received Peg-IFN for 48–96 weeks, with tenofovir disoproxil fumarate (TDF) initiated at week 12 and continued through week 96 (n = 103). The monotherapy group received TDF monotherapy alone (n = 125), and the control group was monitored without therapeutic intervention (n = 58).

No patients in the control group met any predefined efficacy endpoints. Intention-to-treat analysis showed that patients in the combination group achieved significantly higher virological response rates (71.8% vs. 53.6%, p = 0.005), hepatitis B e antigen seroconversion rates (15.5% vs. 1.6%, p < 0.001), and hepatitis B surface antigen (HBsAg) loss rates (10.7% vs. 0%, p < 0.001) compared with those in the monotherapy group at week 96. In the combination group, the cumulative rate of HBsAg loss was 5.4% at week 48 and increased to 11.8% by week 96. Independent predictors of achieving either hepatitis B e antigen seroconversion or HBsAg loss were baseline age under 30 years (odds ratio = 0.217, 95% confidence interval: 0.048–0.976, p = 0.046) and a decline in HBsAg level greater than 1 log10 IU/mL by week 24 (odds ratio = 13.976, 95% confidence interval: 2.506–77.932, p = 0.003).

A Peg-IFN-based treatment strategy significantly increases response rates compared with TDF monotherapy or observation in patients with IT characteristics.

N6-methyladenosine (m6A), the most prevalent internal RNA modification in eukaryotic cells, is a dynamic regulator of RNA metabolism and cancer biology. In colorectal cancer (CRC), dysregulated m6A reshapes transcriptomic programs that control tumor growth, metastasis, immune evasion, and therapeutic resistance. However, the context-dependent functions of individual m6A regulators remain incompletely defined, the integration of m6A with canonical oncogenic signaling remains incomplete, and its role in metabolic reprogramming lacks a systematic overview. This review aims to integrate current evidence on m6A regulatory machinery in CRC, clarify its coordination with oncogenic signaling and metabolic pathways, and highlight emerging translational implications. The key players regulating m6A in CRC progression are m6A “writers”, including methyltransferase-like 3 and methyltransferase-like 14; m6A “erasers”, including fat mass and obesity-associated protein and AlkB homolog 5; and m6A “readers”, including the YTH m6A RNA-binding protein family and the insulin-like growth factor 2 mRNA-binding protein family. m6A modification coordinates key oncogenic pathways, including Wnt/β-catenin, PI3K/Akt, MAPK, and p53 signaling. Moreover, m6A-dependent regulation of metabolic enzymes such as hexokinase 2, pyruvate kinase M2, and fatty acid synthase promotes the reprogramming of glucose, amino acid, and lipid metabolism, linking epitranscriptomic control to bioenergetic adaptation. We also discuss context-dependent and paradoxical functions of m6A regulators and advances in m6A-targeted therapies. In conclusion, m6A modification functions as a central regulatory hub in CRC by integrating signaling networks and metabolic pathways. Deeper mechanistic insights into spatiotemporal m6A regulation may accelerate the development of biomarkers and targeted therapies for precision CRC management.

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.

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.