The prognostic nutritional index (PNI), calculated from serum albumin and lymphocyte count, reflects a patient’s immune-nutritional status and has been proposed as a prognostic marker in hepatocellular carcinoma (HCC). However, its role in advanced HCC patients treated with atezolizumab plus bevacizumab (Ate/Bev) remains unclear. In this study, we aimed to evaluate the prognostic value of PNI in patients receiving first-line Ate/Bev therapy.

We retrospectively analyzed 362 patients with unresectable HCC who received Ate/Bev between November 2020 and June 2023 across two centers. Based on prior literature, a cutoff of 45 was used to classify patients into low-PNI (<45) and high-PNI (≥45) groups. Propensity score matching was performed to balance baseline characteristics.

After propensity score matching, 130 patients (65 per group) were included in the analysis. The high-PNI group showed a significantly lower incidence of grade ≥ 3 treatment-related adverse events (10.8% vs. 24.6%, p = 0.039), a higher objective response rate (38.4% vs. 20.0%, p = 0.037), and significantly longer overall survival (16.7 vs. 7.9 months, p = 0.009). Although progression-free survival was longer in the high-PNI group (4.8 vs. 3.0 months), the difference was not statistically significant (p = 0.597). Multivariate analysis confirmed that PNI was an independent predictor of overall survival (hazard ratio: 0.574, 95% confidence interval: 0.353–0.933, p = 0.025), after adjusting for vascular invasion, alpha-fetoprotein levels, concurrent therapy, and post-treatment interventions.

PNI is an independent prognostic factor for overall survival in advanced HCC patients treated with Ate/Bev in real-world clinical practice. Incorporating PNI into routine assessments may enhance risk stratification and guide therapeutic decision-making.

Microbial resistance and oxidative stress are two significant health issues associated with chronic illnesses and therapy failures. The antioxidant and antibacterial properties of Euphorbia cuneata Vahl. aerial component extracts made with various polarity solvents were assessed in this work.

Disc diffusion and minimum inhibitory concentration (MIC) tests were used to evaluate the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity, total phenolic and flavonoid contents, and antimicrobial activity of n-hexane, toluene, ethanolic, and aqueous extracts against specific ESKAPE pathogens (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans). High-performance liquid chromatography and gas chromatography-mass spectrometry were used to further characterize the most active extract.

Compared to the aqueous (IC₅₀ = 51.61 µg/mL), toluene (IC₅₀ = 30.57 µg/mL), and n-hexane (IC₅₀ = 128.15 µg/mL) extracts, the ethanolic extract demonstrated the greatest 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (97.90 ± 0.8%; IC₅₀ = 28.52 µg/mL). Additionally, it has the highest levels of flavonoids (40.5 ± 1.5 mg luteolin equivalents/g) and phenolic (80.0 ± 0.2 mg gallic acid equivalents/g). While gas chromatography-mass spectrometry found methyl 12-hydroxy-9-octadecenoate (44.39%) as the main volatile molecule, high-performance liquid chromatography analysis identified caffeic acid, pyrogallol, rutin, and 7-hydroxyflavone as important ingredients. The ethanolic extract showed antifungal activity against C. albicans (MIC = 6.25 mg/mL) and moderate antibacterial activity with the lowest MIC values against S. aureus (450 µg/mL) and E. coli (500 µg/mL).

The ethanolic extract of Euphorbia cuneata demonstrated potent in vitro antioxidant activity and moderate antimicrobial effects, primarily attributable to its high phenolic and flavonoid content. These results support its potential as a natural source of bioactive compounds for further development.

Invasive pituitary adenomas with infrasellar extension can present with symptoms such as epistaxis and nasal obstruction, closely mimicking the clinical and radiological characteristics of nasopharyngeal carcinoma, which frequently leads to misdiagnosis. This report discusses the case of a 32-year-old male who was initially misdiagnosed with nasopharyngeal carcinoma for approximately one month and subsequently underwent radiotherapy and chemotherapy. However, a multidisciplinary assessment at our institution, incorporating magnetic resonance imaging findings of an invasive sellar mass, serum prolactin levels exceeding 2,000 ng/mL, and positive immunohistochemistry for PIT-1 and prolactin, established the diagnosis of an invasive prolactinoma. Treatment with bromocriptine led to significant tumor reduction. However, this was complicated by cerebrospinal fluid leakage, which subsequently resulted in an intracranial infection. The patient underwent surgical resection of the tumor and repair of the cerebrospinal fluid leak, with postoperative pathology confirming a PIT1-lineage, densely granulated, prolactin-secreting adenoma. The patient experienced a favorable recovery, with prolactin levels normalizing under continued bromocriptine therapy. This case highlights the critical importance of routine hormonal screening, thorough evaluation of nasopharyngeal mucosal integrity, and multidisciplinary collaboration in the diagnostic process.

Given the lack of efficient biomarkers for hepatocellular carcinoma (HCC) diagnosis, this study aimed to develop an HCC diagnostic strategy based on serum protein glycosylation signatures. We characterized differential N-glycosylation patterns of serum IgG to differentiate HCC from healthy controls and liver cirrhosis, and elucidated the molecular mechanisms driving aberrant Neu5Gc elevation in HCC to provide a theoretical basis for clinical application and differential diagnosis of HCC.

LIP-ELISA was applied to quantify serum Neu5Gc in 6,768 healthy individuals for baseline establishment. IgG was purified and subsequently analyzed by RPLC-MS/MS for glycosylation profiling in HCC and healthy samples. Bioinformatic analysis of CMAH and related gene clusters modulating Neu5Gc synthesis was conducted.

In a cohort of 1,114 participants, the LIP-ELISA platform achieved 80.21% sensitivity, 96.01% specificity, and 92.46% accuracy for primary HCC diagnosis. Serum IgG from HCC patients displayed multi-branched N-glycans modified with core fucose and Neu5Gc. Key molecules involved in glycan modification were identified, enabling the development of multiplexed gene detection for HCC, LC, and chronic hepatitis B. In vitro assays confirmed hypoxia-induced sialic acid accumulation in HCC cells. Meanwhile, CMAH-knockout mouse experiments verified that an exogenous high-sialic-acid diet compensates for endogenous Neu5Gc synthesis deficiency, revealing a dietary-mediated compensatory mechanism for Neu5Gc elevation.

This study established an LIP-ELISA-based clinical diagnostic platform combining AFP and Neu5Gc, defined sialic acid–modified glycan structures, and preliminarily identified regulators of Neu5Gc biosynthesis, providing novel insights for HCC diagnosis and mechanism research.

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.

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.

Metabolic dysfunction–associated steatotic liver disease (MASLD), defined by the American Association for the Study of Liver Diseases as hepatic steatosis with at least one cardiometabolic risk factor, affects approximately 30–40% of adults worldwide. This condition may progress to fibrosis, cirrhosis, and hepatocellular carcinoma. The rising prevalence, alongside obesity and type 2 diabetes, underscores the need for early risk stratification and integrated therapeutic strategies. Circadian homeostasis, orchestrated by the suprachiasmatic nucleus and core clock gene feedback loops, synchronizes hepatic metabolic pathways with environmental light–dark cycles. The objective of this review is to evaluate the role of circadian disruption and metabolic dysfunction in the development of hepatic steatosis, as well as to assess current and potential treatment modalities for both disorders. Circadian disruption through shift work, artificial light at night, sleep restriction, and chrono-nutritional misalignment destabilizes hepatic clocks, promoting insulin resistance, dyslipidemia, inflammation, and steatosis. Experimental models demonstrate that clock gene dysfunction alone can induce steatohepatitis, while progressive MASLD further impairs central circadian regulation, establishing a self-reinforcing chrono-metabolic cycle. Pharmacologic therapies, including glucagon-like peptide-1 receptor agonists and thyroid hormone receptor-β agonists, improve histologic endpoints and fibrosis regression, although heterogeneity among clinical trials precludes direct comparison. Recent evidence characterizing MASLD as a predominantly nocturnal metabolic disorder further highlights persistent nighttime insulin dysregulation despite weight loss, emphasizing the potential role of circadian-targeted interventions such as melatonin. In conclusion, the peripheral circadian clock is intricately linked with MASLD pathogenesis, and metabolic dysfunction, in turn, disrupts circadian pathways. Several pharmacologic therapies offer potential for the treatment of MASLD and circadian dysfunction.

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.