A convolutional neural network approach for detection of E. coli bacteria in water

Clinical screening of Nocardia in sputum smears based on neural networks

Objective

Nocardia is clinically rare but highly pathogenic in clinical practice. Due to the lack of Nocardia screening methods, Nocardia is often missed in diagnosis, leading to worsening the condition. Therefore, this paper proposes a Nocardia screening method based on neural networks, aiming at quick Nocardia detection in sputum specimens with low costs and thereby reducing the missed diagnosis rate.

Methods

Firstly, sputum specimens were collected from patients who were infected with Nocardia, and a part of the specimens were mixed with new sputum specimens from patients without Nocardia infection to enhance the data diversity. Secondly, the specimens were converted into smears with Gram staining. Images were captured under a microscope and subsequently annotated by experts, creating two datasets. Thirdly, each dataset was divided into three subsets: the training set, the validation set and the test set. The training and validation sets were used for training networks, while the test set was used for evaluating the effeteness of the trained networks. Finally, a neural network model was trained on this dataset, with an image of Gram-stained sputum smear as input, this model determines the presence and locations of Nocardia instances within the image.

Results

After training, the detection network was evaluated on two datasets, resulting in classification accuracies of 97.3% and 98.3%, respectively. This network can identify Nocardia instances in about 24 milliseconds per image on a personal computer. The detection metrics of mAP50 on both datasets were 0.780 and 0.841, respectively.

Conclusion

The Nocardia screening method can accurately and efficiently determine whether Nocardia exists in the images of Gram-stained sputum smears. Additionally, it can precisely locate the Nocardia instances, assisting doctors in confirming the presence of Nocardia.

AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water

Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water. A deep learning model was used to identify and quantify target bacteria based on their microscopic patterns generated by specific interactions with bacteriophages. The model was trained on augmented datasets to maximize data efficiency, using input images of selected bacterial species, and then fine-tuned on a mixed culture. Model inference was performed on real-world water samples containing environmental noises unseen during model training. Overall, our AI model trained solely on lab-cultured bacteria achieved rapid (< 5.5 h) prediction with 80-100% accuracy on the real-world water samples, demonstrating its ability to generalize to unseen data. Our study highlights the potential applications in microbial water quality monitoring during food and agricultural processes.

Dissolved Carbon Dioxide: The Lifespan of Staphylococcus aureus and Enterococcus faecalis in Bottled Carbonated Mineral Water

During the process of mineral water production, many possible contamination settings can influence the quality of bottled water. Microbial contamination can originate from different sources, for example, the ambient air, the bottles, the caps, and from the bottling machine itself. The aim of this study was to investigate the influence of three different carbon dioxide (CO2) concentrations (3.0 g/L, 5.5 g/L, and 7.0 g/L; 20 bottles each) in bottled mineral water on the bacterial growth of Staphylococcus aureus (S. aureus) and Enterococcus faecalis (Ent. faecalis). The examined mineral water was artificially contaminated before capping the bottles inside the factory. After a specific number of days, water samples were taken from freshly opened bottles and after filtration (100 mL), filters were placed on Columbia Agar with 5% Sheep blood to cultivate S. aureus and Slanetz and Bartley Agar to cultivate Ent. faecalis. The respective colony-forming units (CFU) were counted after incubation times ranging from 24 to 120 h. Colony-forming units of S. aureus were not detectable after the 16th and 27th day, whereas Ent. faecalis was not cultivable after the 5th and 13th day when stored inside the bottles. The investigation of the bottles that were stored open for a certain amount of time with CO2 bubbling out showed only single colonies for S. aureus after the 5th day and no CFUs for Ent. faecalis after the 17th day. A reduction in the two investigated bacterial strains during storage in carbonated mineral water bottles means that a proper standardized disinfection and cleaning procedure, according to valid hygiene standards of industrial bottling machines, cannot be replaced by carbonation.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

Cefsulodin and Vancomycin: A Supplement for Chromogenic Coliform Agar for Detection of Escherichia coli and Coliform Bacteria from Different Water Sources

Background microorganism growth on Chromogenic Coliform Agar (CCA) can be challenging. For this reason, a new alternative method with a Cefsulodin/Vancomycin (CV)-supplemented CCA should be developed in this study. CCA supplemented with CV was validated according to ÖNORM EN ISO 16140-4:2021 using water from natural sources in Styria, Austria. Results show that the alternative method using the supplemented CCA has similar values in relation to sensitivity (82.2%), specificity (98.6%) and higher selectivity (59%) compared to the reference method. Repeatability and reproducibility were acceptable for the alternative method and showed similar results with the reference method. The alternative method shows a very low false positive rate and a low false negative rate paired with good performance regarding the inclusion study. The exclusion study shows the advantage of our method by suppressing background microorganisms and facilitating the process of enumeration of Escherichia coli and other coliform bacteria on CCA plates. Aeromonas hydrophila and Pseudomonas aeruginosa growth was inhibited using the supplement. To conclude, the coliform CV selective supplement combined with CCA is an appropriate tool for coliform bacteria detection in water samples.