RESUMEN
Antibiotic residues in breast milk can have an impact on the intestinal flora and health of babies. Amoxicillin, as one of the most used antibiotics, affects the abundance of some intestinal bacteria. In this study, we developed a convenient and rapid process that used a combination of colorimetric methods and artificial intelligence image preprocessing, and back propagation-artificial neural network (BP-ANN) analysis to detect amoxicillin in breast milk. The colorimetric method derived from the reaction of gold nanoparticles (AuNPs) was coupled to aptamers (ssDNA) with different concentrations of amoxicillin to produce different color results. The color image was captured by a portable image acquisition device, and image preprocessing was implemented in three steps: segmentation, filtering, and cropping. We decided on a range of detection from 0 µM to 3.9 µM based on the physiological concentration of amoxicillin in breast milk and the detection effect. The segmentation and filtering steps were conducted by Hough circle detection and Gaussian filtering, respectively. The segmented results were analyzed by linear regression and BP-ANN, and good linear correlations between the colorimetric image value and concentration of target amoxicillin were obtained. The R2 and MSE of the training set were 0.9551 and 0.0696, respectively, and those of the test set were 0.9276 and 0.1142, respectively. In prepared breast milk sample detection, the recoveries were 111.00%, 98.00%, and 100.20%, and RSDs were 6.42%, 4.27%, and 1.11%. The result suggests that the colorimetric process combined with artificial intelligence image preprocessing and BP-ANN provides an accurate, rapid, and convenient way to achieve the detection of amoxicillin in breast milk.
RESUMEN
This study aimed to compare the gene expression variation of clinical primary osteosarcoma (OS) and metastatic OS, identify expression profiles and signal pathways related to disease classification, and systematically evaluate the potential anticancer effect and molecular mechanism of ginsenoside Rh2 on OS. A raw dataset (GSE14359), which excluded GSM359137 and GSM359138, was downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) and principal component analysis (PCA) were obtained with limma. Pathways enrichment analysis was understood by GSEA app. Rh2-associated targets were harvested and mapped through PharmMapper and Cytoscape 3.4.0. The toxicity of Rh2 was determined using crystal staining and MTT assay on 143B and MG63 cell lines. The relative protein expression was confirmed through Western blot analysis. The mitochondrial membrane potential (â³Ψm) was evaluated by JC-1 fluorescence staining. The cell mobility was measured via wound healing and transwell assays. A total of 752 genes were upregulated, while 161 genes were downregulated. GSEA and PCA displayed significant function enrichment and classification. Through PharmMapper and Cytoscape 3.4.0, Rh2 was found to target the mitogen activated protein kinase (MAPK) and PI3K signaling pathways, which are the key pathways in the metastasis of OS. Furthermore, Rh2 induced a concentration-dependent decrease in cell viability and early apoptosis associated with ΔΨm decline, while a non-lethal dose of Rh2 weakened the metastatic capability. Moreover, systematic evaluation showed that promoting the MAPK signaling pathway and inhibiting PI3K/Akt/mTOR were correlated with the anticancer effects of Rh2 on metastatic OS. In conclusion, transcriptome-derived approaches may be beneficial in diagnosing early metastases, and Rh2, a multi-targeting agent, shows promising application potential in suppressing metastatic OS in an MAPK- and PI3K/Akt/mTOR-dependent manner.