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Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).
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INTRODUCTION: Alginate/gelatin hydrogel (Alg-Gel) scaffold has been applied in tissue engineering, but the research on its application in dental tissues regeneration is still lacking. We investigated the effect of this scaffold on human dental pulp stem cells (hDPSCs). METHODS: hDPSCs were cultured in both Alg-Gel and 3D-printed Alg-Gel scaffolds. Cell growth and adhesion were compared using fluorescein isothiocyanate-phalloidin staining and scanning electron microscopic micrographs. Changes in the proliferation in hDPSCs cultured in the complete culture medium containing aqueous extracts of the Alg-Gel or 3D-printed Alg-Gel scaffolds were examined using Cell Counting Kit-8 assay and flow cytometry analysis. Cells were cultured in the mineralization medium containing aqueous extracts of the Alg-Gel or 3D-printed Alg-Gel scaffolds for 7 or 14 days, and the differentiation of cells was shown by alizarin red S staining and alkaline phosphatase staining. The messenger RNA and protein expression of mineralization-related genes were detected with real-time polymerase chain reaction and Western blotting. Elemental analysis was used to test the material extract composition. RESULTS: More cells were grown and adhered to the 3D-printed Alg-Gel scaffolds than the Alg-Gel scaffolds. The aqueous extracts of 3D-printed scaffolds can promote cell proliferation, and compared with Alg-Gel scaffolds, the extracts of 3D-printed scaffolds were more effective. Compared with the negative control group, 3D-printed Alg-Gel scaffold and Alg-Gel scaffold aqueous extracts promoted osteogenic/odontoblastic differentiation of hDPSCs with the enhanced formation of bone-like nodules and the alkaline phosphatase staining. The expression of mineralization-related genes was also up-regulated. 3D-printed scaffold aqueous extract contained more calcium and phosphorus ions than the Alg-Gel scaffold. CONCLUSIONS: These findings suggest that compared with the Alg-Gel scaffold, 3D-printed Alg-Gel is more suitable for the growth of hDPSCs, and the scaffold extracts can better promote cell proliferation and differentiation.