Disintegration and Machine-Learning-Assisted Identification of Bacteria on Antimicrobial and Plasmonic Ag-CuxO Nanostructures.

Bacteria cause many common infections and are the culprit of many outbreaks throughout history that have led to the loss of millions of lives. Contamination of inanimate surfaces in clinics, the food chain, and the environment poses a significant threat to humanity, with the increase in antimicrobial resistance exacerbating the issue. Two key strategies to address this issue are antibacterial coatings and effective detection of bacterial contamination. In this study, we present the formation of antimicrobial and plasmonic surfaces based on Ag-CuxO nanostructures using green synthesis methods and low-cost paper substrates. The fabricated nanostructured surfaces exhibit excellent bactericidal efficiency and high surface-enhanced Raman scattering (SERS) activity. The CuxO ensures outstanding and rapid antibacterial activity within 30 min, with a rate of >99.99% against typical Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria. The plasmonic Ag nanoparticles facilitate the electromagnetic enhancement of Raman scattering and enables rapid, label-free, and sensitive identification of bacteria at a concentration as low as 103 cfu/mL. The detection of different strains at this low concentration is attributed to the leaching of the intracellular components of the bacteria caused by the nanostructures. Additionally, SERS is coupled with machine learning algorithms for the automated identification of bacteria with an accuracy that exceeds 96%. The proposed strategy achieves effective prevention of bacterial contamination and accurate identification of the bacteria on the same material platform by using sustainable and low-cost materials.

Indomethacin prevents TGF-ß-induced epithelial-to-mesenchymal transition in pancreatic cancer cells; evidence by Raman spectroscopy.

Pancreatic ductal adenocarcinoma (PDAC) has a very low survival rate due to the late detection and poor response to chemotherapy. Epithelial-to-mesenchymal transition (EMT) is considered an important step in tumor progression with regard to invasion and metastasis, and Transforming Growth Factor-beta (TGF-ß) signaling has been shown to play an important role in EMT. Therefore, we aimed to investigate whether indomethacin, an anti-inflammatory and analgesic drug, has any effect on TGF-ß-induced EMT in pancreatic cancer cell line and analyze the changes in their molecular structures by Raman spectroscopy and other molecular techniques. Indomethacin treated Panc-1 cells were analyzed with Raman spectroscopy, quantitative polymerase chain reaction and immunofluorescence techniques after the induction of EMT with TGF-ß. The exposure of Panc-1 cells to TGF-ß resulted in characteristic morphological alterations of EMT, and indomethacin inhibits TGF-ß-induced EMT through up-regulation of E-cadherin and down-regulation of N-cadherin and Snail expressions. Raman spectroscopy supported by principal component analysis (PCA) confirmed the effects of both TGF-ß and indomethacin. Raman spectra were further analyzed using the PCA-assisted vector machine algorithm and it was seen that the data could be classified with 97.6% accuracy. Our results suggest that indomethacin may have a significant effect on PDAC metastasis, and Raman spectroscopy was able to probe EMT-related changes and the efficacy of indomethacin in a short time and without the need for specific reagents compared to other molecular techniques.