Electrospinning Equipment for Research| In Vivo Spatiotemporal Acquisition ofMetabolic Vibrational Signatures forUnraveling Gastric Ulcer Genesis

 Prof. Chen Zhuo (Hunan University) in *Biomaterials*: Development of Magnetoplasmonic System via Coaxial Electrospinning for Gastric Ulcer Diagnosis

Abnormal gastric fluid metabolism typically directly reflects pathological changes in gastric mucosa. Accurate in situ acquisition of gastric metabolic dynamics is crucial yet challenging for understanding the onset and progression of gastric diseases. 

Recently, Prof. Chen Zhuo's team from the Molecular Science and Biomedicine Laboratory (MBL) at Hunan University published their latest research titled "In Vivo Spatiotemporal Acquisition of Metabolic Vibrational Signatures for Unraveling Gastric Ulcer Genesis" in *Biomaterials*.The study developed an integrated magnetoplasmonic system (MPS) for long-term in vivo spatiotemporal metabolic information analysis and gastric ulcer assessment. The MPS was fabricated using coaxial electrospinning technology, consisting of a porous calcium alginate-silver plasmonic hydrogel shell and an iron-cobalt graphene nanocapsule (FeCo@Graphene) magnetic core. The MPS demonstrated synergistic effects in capturing free metabolites through enrichment, filtration screening, and magnet-driven gastric fluid pumping, achieving a 9.76-fold improvement in efficiency. Multiple metabolic vibrational fingerprints were simultaneously obtained in both harsh simulated gastric fluid (SGF) environments and ex vivo gastric models.

The research acquired in situ metabolic information from rat stomachs. Edge histograms derived from time-resolved surface-enhanced Raman spectroscopy (SERS) showed positive correlations between metabolite levels at different stages. Furthermore, ulcer identification was achieved with high accuracy using spectral dimensionality reduction and random forest classifiers. Metabolite correlation analysis revealed strong positive correlations between Raman signals at 1602 cm-1 and 2112 cm-1 after ulcer onset. This study represents the first analysis of in situ metabolic information within the stomach and explores their correlations during disease progression, demonstrating its potential for auxiliary clinical diagnosis. The first authors of the paper are PhD student Cheng Yuqi and master's student Zhao Lingjin from the College of Chemistry and Chemical Engineering at Hunan University, with corresponding authors Prof. Chen Zhuo, Dr. Cai Xinqi, and Dr. Dong Qian.

Figure 1: Spatiotemporal acquisition of in vivo gastric fluid metabolic information and ulcer diagnosis

Figure 2: Preparation and characterization of MPS

The study utilized electrostatic spraying technology for large-scale preparation of the biocompatible MPS. Silver nanoparticles (AgNPs) with diameters of 38.8±4.0 nm were confined within the three-dimensional pores of the hydrogel. Three-dimensional finite-difference time-domain (FDTD) simulations demonstrated that the strongest localized electromagnetic field intensity generated by the encapsulated AgNPs was 24.4 times that of free AgNPs. The incorporation of FeCo@Graphene magnetic cores with high saturation magnetization (181.9 emu•g-1) enabled the MPS to perform accelerated and circular motions at speeds of 5.6 mm/s and 541 μm/s under applied static and dynamic magnetic fields, respectively. Rheological and cyclic compression curve characterizations confirmed the flexibility of the MPS.

Figure 3: Ex vivo gastric model analysis and in vivo time-resolved SERS study

To validate the application potential of MPS, the authors examined its feasibility in ex vivo mouse stomach models. A series of metabolite mixtures with known concentrations were added to the ex vivo mouse stomach models. The minimum detectable concentration changes for adenine, Tyr, and Phe were 0.0054, 0.018, and 0.33 g/L, respectively. To further demonstrate MPS's capability for in vivo metabolic information acquisition, the authors orally administered three metabolites (10 mM) to rats to simulate metabolic processes in gastric fluid. Time-resolved SERS studies were conducted on adenine, Tyr, and Phe over different time periods. Edge histograms were used to track correlations between changes in the levels of these three metabolites in gastric fluid and different stages, indicating certain positive correlations among them. Thus, the MPS demonstrated the ability to analyze gastric fluid metabolism in living animals in real time.

Figure 4: Gastric ulcer metabolic analysis and precise classification

The authors induced gastric ulcers in rats using 10 mL/kg absolute alcohol for 2 hours and then studied the metabolic kinetics using MPS. t-distributed stochastic neighbor embedding (t-SNE) was employed for dimensionality reduction and visualization of molecular metabolic changes in high-dimensional spectra, revealing clear differentiation between normal and ulcerated rats. Simultaneously, the authors achieved this differentiation using a random forest classifier with 100% prediction accuracy. The receiver operating characteristic (ROC) curve was used to evaluate the classification accuracy of this model, yielding an area under the curve (AUC) value of 1, indicating outstanding classification performance. Therefore, machine learning-assisted SERS spectroscopy achieved classification between normal and ulcerated rats, providing possibilities for distinguishing gastric diseases.

Figure 5: Metabolite correlation analysis

Subsequently, changes in metabolomics during ulcer development were discussed through metabolite-metabolite correlation analysis. The authors investigated interactions among multiple variables in the dataset. Scatter plot matrix diagrams clearly showed correlations between different metabolites in normal and ulcerated rats. Strong correlations between amino acids and amines suggested their potential as markers for ulcer onset. This method represents a promising strategy for studying metabolic product interactions and demonstrates potential for gastric disease diagnosis.

In summary, the MPS established in this study possesses powerful capabilities for acquiring and measuring gastric fluid metabolomics information. This holds promise for revealing the development of gastric-related diseases and provides new insights for molecular diagnosis of gastric diseases in the era of precision medicine.

Paper link:https://doi.org/10.1016/j.biomaterials.2025.123383