A fluorescent sensor array based on antibiotic-stabilized metal nanoclusters for the multiplex detection of bacteria.

To address the need for facile, rapid detection of pathogens in water supplies, a fluorescent sensing array platform based on antibiotic-stabilized metal nanoclusters was developed for the multiplex detection of pathogens. Using five common antibiotics, eight different nanoclusters (NCs) were synthesized including ampicillin stabilized copper NCs, cefepime stabilized gold and copper NCs, kanamycin stabilized gold and copper NCs, lysozyme stabilized gold NCs, and vancomycin stabilized gold/silver and copper NCs. Based on the different interaction of each NC with the bacteria strains, unique patterns were generated. Various machine learning algorithms were employed for pattern discernment, among which the artificial neural networks proved to have the highest performance, with an accuracy of 100%. The developed prediction model performed well on an independent test dataset and on real samples gathered from drinking water, tap water and the Anzali Lagoon water, with prediction accuracy of 96.88% and 95.14%, respectively. This work demonstrates how generic antibiotics can be implemented for NC synthesis and used as recognition elements for pathogen detection. Furthermore, it displays how merging machine learning techniques can elevate sensitivity of analytical devices.

Smartphone image analysis-based fluorescence detection of tetracycline using machine learning.

Tetracycline (TC) is vastly used as a veterinary drug, making its detection highly important. We have aimed to develop a rapid detection method for TC. For this, BSA-protected Au/Ag bimetallic nanoclusters (BSA-BMNCs) were synthesized for the detection of TC in water and milk. The interaction of TC with BSA shifted the emission of the BMNCs from red to yellow as concentrations of TC increased. Images of the sensing platform were captured with various smartphones and the color and texture information was extracted to generate training datasets for water and milk samples. The datasets were used to train machine learning (ML) algorithms. A model using bagging and artificial neural networks (R2 = 0.994, NRMSE = 0.078) for water samples and one using bagging and decision trees (R2 = 0.999, NRMSE = 0.027) for milk samples were developed. This study shows the ability of ML algorithms for the development of rapid sensors for the detection of food analytes.

Untargeted Metabolomics Analysis of Crocus cancellatus subsp. damascenus (Herb.) B. Mathew Stigmas and Their Anticarcinogenic Effect on Breast Cancer Cells.

Safranal, crocin, crocetin, and picrocrocin are major known compounds in the stigma extract of Crocus sativus with various medicinal properties. Crocus cancellatus is another Crocus species that grows extensively in Iran's various regions, such as the Kurdistan province. The predominant metabolites and biological properties of C. cancellatus have not yet been investigated. The ingredients of the stigma ethanol extract of C. cancellatus were investigated using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography with tandem mass spectrometry (LC-MS). The ROIMCR approach was performed to analyze the LC-MS full scan data sets. This method searches the MS regions of interest (ROI) data in the m/z domain and analyses the results using the multivariate curve-resolution alternating least squares (MCR-ALS) chemometrics technique for simultaneous resolution of two extracts. Also, the antiproliferative properties of C. cancellatus against MDA-MB-231 and MCF-7 cancer cells were examined by MTT, dual acridine orange/ethidium bromide test, Annexin V-FITC/PI, and zymography. The GC-MS and LC-MS untargeted metabolomics data analysis of the extract indicated the presence of cytotoxic agents including safranal, crocin, picrocrocin, and crocetin in the stigma ethanol extract of C. cancellatus. Biological tests showed that the viability of MDA-MB-231 and MCF-7 cancer cells is decreased following C. cancellatus treatment in a time- and dose-dependent way in both monolayer and 3D cell cultures. The MCF-7 cell spheroids had greater resistance to the cytotoxic activity of the extract in 3D cell culture than the MDA-MB-231 cell spheroids. The morphological changes of the cells treated with C. cancellatus stigmas extract were indicative of apoptosis. Zymography analysis revealed a similar trend of matrix metallopeptidase-2 (MMP-2) and matrix metallopeptidase-9 (MMP-9) activity in the treated cells with C. cancellatus extract in comparison with doxorubicin treatment as a positive control. The findings of this research indicate that the ethanolic extract of C. cancellatus stigmas was a good source of bioactive metabolites with anticancer activity.

Multiplex Detection of Antibiotic Residues in Milk: Application of MCR-ALS on Excitation-Emission Matrix Fluorescence (EEMF) Data Sets.

The presence of antibiotics and their metabolites in milk and dairy products is a serious concern because of their harmful effects on human health. In the current study, a novel synergistic bimetallic nanocluster with gold and silver as an emission fluorescence probe was investigated for the simultaneous determination of tetracycline (TC), ampicillin (AMP), and sulfacetamide (SAC) antibiotics in the milk samples using excitation-emission matrix fluorescence (EEMF) spectroscopy. The multivariate curve resolution-alternating least squares (MCR-ALS) method was implemented to analyze augmented EEMF data sets to quantify the multicomponent systems in the presence of interferences with considerable spectral overlap. A pseudo-univariate calibration curve of the resolved emission spectra intensity against the concentration of the mentioned antibiotics was linear in the range of 5-5000 ng mL-1 for AMP and 50-5000 ng mL-1 for TC and SAC. The calculated values of the limit of detection ranged between 1.4 and 14.6 ng mL-1 with a relative standard deviation (RSD) of less than 4.9%. The obtained results show that the EEMF/MCR-ALS methodology using an emission fluorescence probe is a powerful tool for the simultaneous quantification of TC, AMP, and SAC in complex matrices with highly overlapped spectra.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs.

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.