Jingjing Xia

As a noninvasive liquid biopsy approach, extracellular vesicle (EV)-based detection offers significant advantages in reflecting real-time tumor dynamis and overcoming the limitations of conventional tissue biopsy. EVs, nanoscale vesicles secreted by cells, carry diverse biomolecules such as proteins and nucleic acids, playing key roles in tumor progression, metastasis, and immune evasion, and have emerged as promising biomarkers for cancer liquid biopsy. Surface-enhanced Raman spectroscopy (SERS), characterized by high sensitivity, resistance to photobleaching, minimal sample consumption, and multiplexing capability, has shown great potential in EV analysis. This review systematically summarizes current methods for EV isolation, characterization, and storage, with a focus on label-free and label-based SERS detection strategies for early cancer diagnosis, treatment response monitoring, and prognosis evaluation. Furthermore, the integration of SERS with machine learning and deep learning algorithms has substantially improved diagnostic accuracy and cancer subtyping. Despite remaining challenges, such as optimization of SERS substrate performance, intelligent processing of Raman spectral fingerprints, and clinical translation, EV-based SERS technology holds great promise for precision oncology.

Lycopene liquid beverages are functional products with strong antioxidant activity, but their quality control is hindered by the lack of rapid and simultaneous detection methods for multiple indicators. This study investigated a near-infrared spectroscopy (NIRS)—based approach for the simultaneous quantification of pH, moisture, and lycopene content in lycopene liquid beverages. A combined preprocessing strategy integrating Savitzky-Golay smoothing and standard normal variate (SG-SNV) was optimized via grid search to reduce spectral noise and scattering effects. Model interpretability was further enhanced by SHapley Additive exPlanations (SHAP) analysis. Furthermore, four variable selection algorithms (MWPLS, UVE-SPA, ICO, and CARS) were compared to improve model performance and interpretability. The results showed that SG-SNV preprocessing notably improved the prediction performance for pH (R²_CV = 0.9398, R²_PRE = 0.9624) and lycopene (R²_CV = 0.9860, R²_PRE = 0.9576), while moisture prediction remained at a comparable level (R²cv = 0.9617, R²_PRE = 0.9727). SHAP analysis identified spectral regions that may be associated with each component, providing chemically plausible explanations. For variable selection, the optimal algorithm differed across the three quality indicators. For moisture, CARS achieved the highest prediction performance (R²_PRE = 0.9946), while ICO provided comparable accuracy with slightly better model stability. For pH, UVE–SPA produced the strongest generalization ability (R²_PRE = 0.9705) with only 5 selected variables. For lycopene, none of the variable selection methods improved upon the preprocessing-only model (R²_PRE = 0.9576); MWPLS retained a parsimonious 21-variable model but with reduced predictive accuracy (R²_PRE = 0.8888). This study explores a preliminary rapid and non-destructive NIR analytical approach for the simultaneous detection of multiple quality indicators in lycopene liquid beverages, suggesting the feasibility of chemometric modeling for this application. The results provide a preliminary foundation for future development of rapid multi-indicator analytical methods. However, this work was conducted entirely under controlled laboratory conditions using a small, gradient-designed sample set with no production-line or online validation; it should therefore be regarded as a proof-of-concept feasibility study. Substantial further work—including validation with larger, naturally variable sample cohorts—would be needed before the approach could be considered for practical deployment.

Cyperus esculentus, a plant rich in unsaturated fatty acids, is characterized by high yield, nutritional value, and drought resistance. Rapid detection of its nutritional components is of great significance for ensuring food security and ecological restoration. In this study, near-infrared (NIR) spectroscopy, in combination with chemometrics, was employed to analyze the characteristics and the contents for crude fat (CF), total sugar (TS), and crude protein (CP) of Cyperus esculentus. To address spectral variability and sample heterogeneity, a hybrid strategy combining Savitzky-Golay smoothing, and standard normal variate was proposed. Grid search was used for parameter optimization, with SHapley Additive Explanations (SHAP) analysis applied to identify the most interpretable variables. The results revealed marked differences in the nutritional profiles of Cyperus esculentus across regions, indicating the potential to develop region-specific products based on these characteristics. For test set, the prediction models achieved high coefficient of determination (R2) of 0.9847 (CF), 0.9096 (TS), 0.9876 (CP). These findings demonstrate that the proposed method enables rapid and accurate prediction of nutritional components in Cyperus esculentus. Notably, the chemical variables screened by SHAP exhibit a high degree of alignment with nutritional profiles, significantly enhancing model interpretability. The proposed method enables rapid and accurate prediction of these components in Cyperus esculentus with high interpretability robustness, thereby establishing an innovative technical framework for quality assessment.

The zinc finger protein with KRAB and SCAN domains 3 (ZKSCAN3) has emerged as a critical regulator of diverse cellular processes, including autophagy, cell cycle progression, and tumorigenesis. Structurally, ZKSCAN3 is characterized by its conserved DNA-binding zinc finger motifs, a SCAN domain mediating protein–protein interaction, and a KRAB repression domain implicated in transcriptional regulation. Post-translational modifications, such as phosphorylation and ubiquitination, dynamically modulate its subcellular localization and activity, enabling context-dependent functional plasticity. Functionally, ZKSCAN3 acts as a master switch in autophagy by repressing the transcription of autophagy-related genes under nutrient-replete conditions, while its nuclear-cytoplasmic shuttling under stress conditions links metabolic reprogramming to cellular survival. Emerging evidence also underscores its paradoxical roles in cancer: it suppresses tumor initiation by maintaining genomic stability yet promotes metastasis through epithelial–mesenchymal transition induction. Furthermore, epigenetic mechanisms, including promoter methylation and non-coding RNA regulation, fine-tune ZKSCAN3 expression, contributing to tissue-specific outcomes. Despite these insights, gaps remain in understanding the structural determinants governing its interaction with chromatin-remodeling complexes and the therapeutic potential of targeting ZKSCAN3 in diseases. Future investigations should prioritize integrating multi-omics approaches to unravel context-specific regulatory networks and explore small-molecule modulators for translational applications. This comprehensive analysis provides a framework for advancing our mechanistic understanding of ZKSCAN3 and its implications in human health and disease. This review synthesizes recent advances in elucidating the regulatory networks and functional complexity of ZKSCAN3, highlighting its dual roles in physiological and pathological contexts.

Fritillaria spp. has an extremely high edible and medicinal value. Different parts of it exhibit significant variations in medicinal efficacy. To rapidly and accurately identify the origin and adulteration of Fritillaria spp., a handheld near-infrared spectrometer was combined with a convolutional neural network (CNN) to establish an efficient and convenient quality assessment method. First, for the origin of Fritillaria spp., the CNN could achieve high accuracy, with 100 ± 0%. The features contributing to the origin of Fritillaria spp. were visualized using gradient-weighted class activation mapping (Grad-CAM). For the adulteration of Fritillaria spp., compared with partial least squares regression (PLSR), the CNN yielded the best performance, with the R² of the test set being 0.9897. Additionally, to improve the interpretability of the adulteration model, a CNN model was established using data whose dimensions had been reduced by PCA (PCA–CNN), which also achieved an R² of 0.9876. The features extracted by PCA focused on 1400–1500 nm, which was consistent with Grad-CAM. The visualization of Grad-CAM and the adulteration detection model achieved mutual validation, showing the effectiveness of both methods in analyzing the samples. The experimental results demonstrated that the integration of a handheld near-infrared spectrometer with a CNN enabled both reliable authentication of Fritillaria spp. geographical origins and quantitative determination of adulteration levels, establishing a novel analytical framework for rapid quality evaluation of Fritillaria spp. materials.