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.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.

Hepatic ischemia–reperfusion (HIR) injury impairs outcomes post–liver transplantation. Therefore, we aimed to investigate the role and mechanism of miR-381-3p in HIR.

The study enrolled 150 healthy controls, 82 non-HIR-injured patients, and 68 patients with HIR injury following liver transplantation. Clinical data were analyzed. Multivariate analysis identified HIR risk factors; the predictive value of miR-381-3p was assessed via receiver operating characteristic analysis. An in vitro hypoxia/reoxygenation (H/R) model was established and employed. The cellular effects of miR-381-3p and JAK2 were evaluated using CCK-8, flow cytometry, ELISA, luciferase, RIP, and bioinformatics.

Serum miR-381-3p was significantly elevated in HIR compared with the other groups. miR-381-3p was the strongest independent HIR risk factor, which was confirmed by receiver operating characteristic analysis. H/R upregulated miR-381-3p. Inhibiting miR-381-3p counteracted H/R-induced decreased viability and increased apoptosis, inflammation, and oxidative stress. miR-381-3p directly bound to and suppressed JAK2 via its 3′ untranslated region (validated by luciferase and RIP). Transfection of si-JAK2 abolished the protective effects of miR-381-3p inhibition.

miR-381-3p exacerbates post-transplant HIR by directly targeting JAK2, amplifying inflammation and oxidative stress. Thus, our findings nominate serum miR-381-3p as a promising non-invasive biomarker and suggest its potential as a therapeutic target for mitigating HIR injury.

Chronic stress-induced hypercortisolism causes diminished ovarian reserve (DOR), contributing to infertility and miscarriage. Androgen supplementation is an emerging therapeutic approach for DOR. The traditional Chinese herbal decoction modified Gengnianchun formula (MGNC) has shown clinical efficacy in treating DOR. This study aimed to compare the effectiveness of MGNC with that of androgens in a stress-induced DOR mouse model.

Sexually mature female C57 mice aged six weeks were randomly assigned to six groups (n = 10 per group, with 3 independent replicates per group), including the control, model, low-dose testosterone (LT), medium-dose testosterone (MT), high-dose testosterone (HT), and MGNC groups. This sample size and study design were determined based on preliminary experimental data. Chronic stress was induced in mice, except for the control group, by daily glucocorticoid injection, and the mice in the LT, MT, HT, and MGNC groups were treated at the same time with testosterone (low, medium, or high dose) or MGNC for six weeks. Body weight, estrous cycles, ovarian follicle counts, hormone profiles, including follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, and testosterone, and in vitro preantral follicle growth rates (via MGNC-enriched or androgen-treated serum) were assessed.

All groups presented stable body weights. MGNC ameliorated estrous cycle irregularities caused by stress, while testosterone exacerbated the abnormality. Moreover, MGNC outperformed LT in improving primordial/primary/antral follicle counts and corpus luteum formation, while MT and HT did not improve ovarian follicle reserve. LT was associated with the highest serum estradiol level, but none of the testosterone doses reduced FSH levels or the FSH/LH ratio, whereas MGNC lowered FSH and the FSH/LH ratio. Additionally, MGNC-enriched serum significantly enhanced the in vitro follicular growth rate in corticosterone-supplemented culture medium, and this effect was superior to that observed with testosterone-pretreated serum.

MGNC demonstrates superior efficacy over androgen therapy in treating chronic stress-induced DOR in mice, supporting further investigations into its clinical potential and mechanisms.

Breast cancer (BCA) is one of the most common cancers worldwide, with a high rate of mortality and morbidity in women. This review focuses on the applications of nanotechnology, nanomaterials, and nanoparticles (NPs) in BCA, encompassing diagnosis and therapy. Nanotechnologies, nanocarriers, and nano-encapsulations versus their corresponding counterparts for BCA diagnosis and therapy have been discussed. Various drug formulations into different nanocarriers (lipid NPs, nanoemulsions, polymeric NPs, and metal-based NPs) enhanced their bioavailability and therapeutic efficacy, overcoming the limitations of conventional formulations. Additionally, clinical specialists have achieved improved outcomes in the detection and monitoring of BCA at various stages using nanotechnology, ultimately leading to an improved quality of life for patients.

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.