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.

Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food.

We have developed a rapid, facile liquid crystal (LC)-based aptasensor for E. coli detection in water and juice samples. A textile grid-anchored LC platform was used with specific aptamers adsorbed via a cationic surfactant, cetyltrimethylammonium bromide (CTAB), on the LC surface. The presence of E. coli dissociates the aptamers from CTAB and restores the dark signal induced by the surfactant. Using polarized microscopy, the images of the LCs in the presence of various concentrations of E. coli were captured and analyzed using image analysis and machine learning (ML). The artificial neural networks (ANN) and extreme gradient boosting (XGBoost) rendered the best results for water samples (R2 = 0.986 and RMSE = 0.209) and juice samples (R2 = 0.976 and RMSE = 0.262), respectively. The platform was able to detect E. coli with a detection limit (LOD) of 6 CFU mL-1.

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.

Sensitive detection of S. Aureus using aptamer- and vancomycin -copper nanoclusters as dual recognition strategy.

The proposed aptamer- and antibiotic-based dual detection sensor, combines copper nanoclusters (CuNCs) as an effective approach for the recognition and quantification of Staphylococcus aureus (S. aureus) as a pathogenic bacteria. A facile method for CuNCs based on vancomycin as the template using a fluorescence platform was proposed for the recognition of the S. aureus whole cells via antibiotic and aptamer. Using dual receptor functionalized CuNCs linked to vancomycin and a specific aptamer and during aggregation induce emission process enhanced fluorescence signal linearly with S. aureus concentrations between 102-108 CFU/mL, and the detection limit was 80 CFU/mL after 45 min as the optimum incubation time. Non-target bacteria generated negative results, proving the high specificity of the presented sensor. This strategy showed recoveries ranging 86%-98% in milk as real sample and can be used for the development of universal detection platforms for efficient and specific S. aureus detection with great potential applications for monitoring pathogenic bacteria.

Computational prediction of antifungal peptides via Chou's PseAAC and SVM.

With the increase in immunocompromised patients in the recent years, fungal infections have emerged as new and serious threat in hospitals. This, and the insufficiency of current antifungal therapies alongside their toxic effects on patients, has led to the increased interest in seeking new antifungal peptides. In the present study, we have developed a prediction method for screening of antifungal peptides. For this, we have chosen Chou's pseudo amino acid composition (PseAAC) to translate peptide sequences into numeric values. Thus, the SVM classifier was performed for binomial classification of antifungal peptides. The performance of the classifier was evaluated via ten-fold cross-validation and an independent dataset. For further validation of the model developed, 22 P24-derived peptides were predicted using the classifier and in vitro assays were performed on the three peptides with the highest prediction score. The results showed that the PseAAC + SVM method is able to predict AFPs with ACC of 94.76%. In vitro results also validate the SEN and SPC of the classifier. The results suggest that the computational approach used in this study is highly efficient for prediction of antifungal peptides, which can save time and money in AFP screening and synthesis of novel peptides.

An Evaluation on Different Machine Learning Algorithms for Classification and Prediction of Antifungal Peptides.

BACKGROUND: Fungi are an emerging threat in medicine and agriculture and current therapeutics have proved to be insufficient and toxic. This has led to an increased interest in peptide-based therapeutics, especially antifungal peptides (AFPs), being safer and more effective drug candidates against fungal threats. However, screening for peptides with antifungal activity is costly and timeconsuming. However, by using computational techniques, we can overcome these restricting factors. The aim of the present study is to compare different machine learning algorithms in combination with Chou's pseudo amino acid composition in classifying and predicting AFPs to represent a precise model for AFP prediction. METHODS: Five different machine learning algorithms frequently used for classification of biological data were used and their performance was evaluated and compared based on their accuracy, sensitivity, specificity and Matthew's correlation coefficient. The two algorithms with the best performance were then further verified with an independent test dataset. RESULTS: SVM and Bagged-C4.5 classifiers had the highest performance results among the five algorithms. Further validations showed that the model generated using SVM can be employed for precise classification and prediction of antifungal peptides. All the performance values of this model were above 80%, making the classifier highly accurate and trustable. CONCLUSION: Using computational approaches, especially data mining techniques, we can develop a precise prediction model for antifungal peptides. The model developed in this study using SVM can be considered a powerful tool for the prediction of antifungal peptides, which can be the first step in synthesis and discovery of novel fungi targeting agents.