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Metamaterial-inspired terahertz (THz) biosensors are devoted to developing high-sensitivity and label-free biosensing strategies. However, most meaningful molecular signals are obscured by the strong THz absorption of solvent water. Most reported THz biosensors require the tested samples to be tediously dried or replaced with a low-absorption medium, which impairs the original bioactivity and the distribution homogeneity of targets. As described in this proposed strategy, a moleculespecific THz biosensor was fabricated from an aptamer hydrogelfunctionalized THz metamaterial. Benefitting from the strong interaction with the localized electric field of the metamaterial, trace thrombin-induced variations in the hydration state of the hydrogel can be sensitively probed, which was investigated experimentally and theoretically. The optimized THz biosensor exhibited remarkable specificity for actual serum sample assays and excellent sensitivity, with a relatively low detection limit of 0.40 pM in the human serum matrix. The proposed strategy could serve as a model system to develop various molecule-specific THz biosensors for aqueous molecule sensing.

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Gastric cancer (GC) is the fifth most common cancer in the world and a serious threat to human health. Due to its high morbidity and mortality, a simple, rapid and accurate early screening method for GC is urgently needed. In this study, the potential of Raman spectroscopy combined with different machine learning methods was explored to distinguish serum samples from GC patients and healthy controls. Serum Raman spectra were collected from 109 patients with GC (including 35 in stage I, 14 in stage II, 35 in stage III, and 25 in stage IV) and 104 healthy volunteers matched for age, presenting for a routine physical examination. We analyzed the difference in serum metabolism between GC patients and healthy people through a comparative study of the average Raman spectra of the two groups. Four machine learning methods, one-dimensional convolutional neural network, random forest, support vector machine, and K-nearest neighbor were used to explore identifying two sets of Raman spectral data. The classification model was established by using 70% of the data as a training set and 30% as a test set. Using unseen data to test the model, the RF model yielded an accuracy of 92.8%, and the sensitivity and specificity were 94.7% and 90.8%. The performance of the RF model was further confirmed by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.9199. This exploratory work shows that serum Raman spectroscopy combined with RF has great potential in the machine-assisted classification of GC, and is expected to provide a non-destructive and convenient technology for the screening of GC patients.