Diabetic cardiomyopathy (DCM), a diabetes-specific cardiovascular complication, is pathologically characterized by cardiomyocyte apoptosis, oxidative stress, inflammatory responses, and myocardial fibrosis, distinguishing it from other cardiac disorders, such as hypertension and coronary artery disease. Challenges in early diagnosis, coupled with the limited efficacy and adverse effects of current treatments, have made DCM a significant contributor to heart failure and mortality in patients with diabetes. Natural products, recognized for their diverse sources, structural variety, and multitarget therapeutic potential, have shown promise in preventing and treating DCM. Drawing on advances over the past five years, this review systematically summarizes the pharmacological effects and molecular mechanisms of natural products (e.g., flavonoids, terpenoids, phenylpropanoids, alkaloids, and polysaccharides) in the treatment of DCM, with the aim of providing a theoretical foundation for further research and drug development.

Aging is characterized by a progressive decline in physiological function, an increased risk of chronic diseases, and multiple molecular and cellular alterations, including inflammation, oxidative stress, and mitochondrial dysfunction. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), initially developed for the treatment of type 2 diabetes and obesity, may modulate pathways associated with the hallmarks of aging. This review aims to summarize the mechanistic and therapeutic evidence for GLP-1 RAs in targeting key aging processes and their potential to restore cellular homeostasis and enhance healthspan. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science up to August 2025. Both preclinical and clinical studies were included if they evaluated the effects of GLP-1 RAs on the major biological processes encompassed by the 12 hallmarks of aging, such as mitochondrial dysfunction, insulin resistance, dysbiosis, inflammaging, autophagy, proteostasis, and genomic stability. Data were analyzed narratively to elucidate potential mechanisms and translational relevance. Evidence from animal and human studies demonstrates that GLP-1 RAs improve mitochondrial function, reduce oxidative stress, attenuate chronic inflammation, and enhance autophagic activity. Additionally, they modulate nutrient-sensing pathways and metabolic processes, thereby improving cellular resilience. Preclinical studies indicate neuroprotective, cardioprotective, and hepatoprotective effects, while emerging clinical data support improvements in metabolic and inflammatory profiles in older adults. Taken together, GLP-1 RAs exert pleiotropic effects across all 12 hallmarks of aging. Although long-term safety and efficacy require further evaluation, current evidence positions GLP-1 RAs as promising therapeutic agents in translational geroscience, with the potential to mitigate age-related physiological decline and promote a longer, healthier lifespan.

Mediastinal germ cell tumors (GCTs) are rare malignant neoplasms that occasionally develop somatic-type malignancies (SMs), such as sarcomas, carcinomas, and hematologic malignancies.

We report a unique case of a 16-year-old male patient with a mediastinal GCT that simultaneously developed two different SMs: well-differentiated angiosarcoma and acute megakaryoblastic leukemia (AML). The patient initially presented with left shoulder pain and intermittent shortness of breath. The imaging study demonstrated a 12.5 × 9.0 × 8.5 cm heterogeneous mass in the left anterior mediastinum. The mediastinal mass was resected and showed a cystic mature teratoma with somatic transformation into well-differentiated angiosarcoma and AML. A subsequent bone marrow biopsy confirmed the diagnosis of AML, and next-generation sequencing demonstrated the presence of PTEN and TP53 gene mutations in the AML. Despite aggressive chemotherapy and allogeneic stem cell transplantation, the patient died 10 months after diagnosis.

Our report demonstrates the unique capability of mediastinal GCTs to simultaneously develop two different SMs. The presence of two different SMs in mediastinal GCTs is associated with extremely aggressive behavior and a poor prognosis.

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.

Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference.

We deployed an integrated computational framework combining transcriptomics, network biology, machine learning, and genetic causal inference. Peripheral blood transcriptomes from PSC patients and controls were analyzed to identify disease-associated modules. Candidate genes were refined via protein-protein interaction networks and a multi-algorithm machine learning screen. Causal inference was performed using two-sample Mendelian randomization, integrating plasma protein quantitative trait loci with PSC genome-wide association study summary statistics.

Transcriptomic analysis revealed a PSC-associated module enriched in ribosome biogenesis and protein homeostasis pathways. A machine learning-optimized nine-gene signature (including PTMA, SUMO1, Shwachman-Bodian-Diamond syndrome (SBDS), RPL7, EIF1AX, ANP32A, PCNA, FAM98A, and MPHOSPH6) achieved high diagnostic accuracy (mean AUC = 0.908) and was consistently downregulated in PSC. This signature was linked to a remodeled immune microenvironment characterized by myeloid skewing and specific transcriptional-immune covariation patterns. Mendelian randomization identified SBDS as a putatively causal protective factor, where genetically instrumented higher plasma SBDS protein levels were robustly associated with a lower PSC risk (IVW OR = 0.525, 95% CI: 0.356–0.773, P = 0.001). Sensitivity analyses supported the validity of the Mendelian randomization assumptions.

Our study establishes disrupted ribosome homeostasis as a causal pathway in PSC and nominates plasma SBDS as a high-confidence diagnostic biomarker and therapeutic target. The integrative framework provides a generalizable strategy for discovering causal biomarkers in complex diseases.

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.

Colorectal cancer histopathological grading relies on the accurate segmentation of glandular structures. Current deep learning–based methods depend heavily on large-scale pixel-level annotations that are labor-intensive and not amenable to clinical practice. Weakly supervised semantic segmentation offers a promising alternative; yet, existing class activation map–based weakly supervised semantic segmentation approaches often produce incomplete, low-quality pseudo-masks that overemphasize discriminative regions and fail to provide reliable supervision for unannotated glandular structures, limiting their suitability for dense histopathology segmentation under sparse supervision. We propose a novel weakly supervised teacher–student framework that leverages sparse pathologists’ annotations and an Exponential Moving Average–stabilized teacher network to generate refined pseudo-masks.

Our framework integrates confidence-based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum-guided refinement, enabling the student network to progressively delineate and accurately segment unannotated glandular regions. We validated our framework on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center, consisting of 60 hematoxylin and eosin-stained whole-slide images from independent patients with varying degrees of gland differentiation, as well as on public benchmarks including the Gland Segmentation dataset (derived from stage T3–T4 colorectal adenocarcinomas), TCGA-COAD, TCGA-READ, and SPIDER.

The proposed framework achieved strong performance on the institutional dataset despite limited annotations. On the Gland Segmentation dataset, it demonstrated competitive performance compared to both weakly and fully supervised approaches, achieving a mean Intersection over Union of 80.10% ± 1.52 and a mean Dice coefficient of 89.10% ± 2.10. Moreover, cross-cohort evaluations showed robust generalization on TCGA-COAD and TCGA-READ without requiring additional annotations, while reduced performance on SPIDER reflected pronounced domain shift.

Our framework provides an annotation-efficient and generalizable paradigm for accurate gland segmentation in colorectal histopathology, offering a practical pathway toward significantly reducing annotation burdens while preserving high segmentation fidelity.

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 in detail the pathophysiological regulation 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 TH level assessment in liver failure and to provide novel insights into the prognostic evaluation and comprehensive care of liver failure complicated by thyroid dysfunction.