Dataset Information

Fluorimetric Detection of Single Pathogenic Bacterium in Milk and Sewage Water Using pH-Sensitive Fluorescent Carbon Dots and MALDI-TOF MS.

ABSTRACT: The current study is focused on the application of water-soluble, fluorescent, and pH-sensitive carbon dots (CDs) as a nanoprobe for sensitive detection of pathogenic bacteria in milk and sewage water. The CDs were facilely synthesized through the controlled carbonization of sucrose using sulfuric acid and characterized through XRD, FTIR, TEM, UV-Vis Spectroscopy, and fluorescent analysis. The as-synthesized CDs were highly water-soluble, stable, and pH-sensitive fluorescent nanomaterials. The pH-related fluorescence study showed that the ratio of fluorescence intensity (Log[IF410/IF350]) changed linearly in the pH range between 4.9 and 6.9 in the Britton-Robison buffer. By determining the pH variation of the growth medium caused by the released acidic metabolites, the CDs-based ratiometric nanoprobe and MALDI-TOF mass spectrometry (MS) were used for the detection and identification of Escherichia coli O157:H7, respectively. The practical applicability of the pH-sensitive fluorescent CDs-based ratiometric nanoprobe was evaluated to detect Escherichia coli O157:H7 in real samples, i.e., milk and sewage water using agar count plate method with a limit of detection (LOD) up to 1 colony-forming unit per mL (CFU/mL).

SUBMITTER: Yang Q

PROVIDER: S-EPMC7022441 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fluorimetric Detection of Single Pathogenic Bacterium in Milk and Sewage Water Using pH-Sensitive Fluorescent Carbon Dots and MALDI-TOF MS.

Yang Qiaoli Q Farooq Umer U Chen Wei W Ullah Muhammad Wajid MW Wang Shenqi S

Microorganisms 20191226 1

The current study is focused on the application of water-soluble, fluorescent, and pH-sensitive carbon dots (CDs) as a nanoprobe for sensitive detection of pathogenic bacteria in milk and sewage water. The CDs were facilely synthesized through the controlled carbonization of sucrose using sulfuric acid and characterized through XRD, FTIR, TEM, UV-Vis Spectroscopy, and fluorescent analysis. The as-synthesized CDs were highly water-soluble, stable, and pH-sensitive fluorescent nanomaterials. The p ...[more]

PMID: 31888104

Similar Datasets

Project description:Simple Summary Monitoring health status and disease outbreak among food animal herds is vitally important to global food safety. Affected animals will experience production losses, and in uncurable cases, operations will need to be modified or animals culled. Producers and veterinary personnel have consistent interest in new diagnostic tools to provide rapid, accurate, and simple testing strategies which do not undermine financial viability of the operation. This manuscript describes a new approach for diagnosis of mastitis in dairy animals. The new method involves first analyzing raw milk from animals by matrix-assisted laser desorption/ionization mass spectrometry and collecting mass spectra. Then, peaks from the mass spectra are imported into a machine learning model, and this software application discovers non-obvious patterns present in the data which coincide with the mastitis condition. Finally, a separate set of milk samples is analyzed (scoring set) to evaluate the diagnostic accuracy of the new model. Results suggest that certain machine learning models offer value to the producer for diagnosis of subclinical mastitis in dairy cows. More generally, the manuscript outlines use of the machine learning approach for diagnoses of animal disease, and we prophesize that this strategy may be applicable to a wide array of animal health concerns. Abstract Novel strategies for diagnostic screening of animal and herd health are crucial to contain disease outbreaks, maintain animal health, and maximize production efficiency. Mastitis is an inflammation of the mammary gland in dairy cows, often resulting from infection from a microorganism. Mastitis outbreaks result in loss of production, degradation of milk quality, and the need to isolate and treat affected animals. In this work, we evaluate MALDI-TOF mass spectrometry as a diagnostic for the culture-less screening of mastitis state from raw milk samples collected from regional dairies. Since sample preparation requires only minutes per sample using microvolumes of reagents and no cell culture, the technique is promising for rapid sample turnaround and low-cost diagnosis. Machine learning algorithms have been used to detect patterns embedded within MALDI-TOF spectra using a training set of 226 raw milk samples. A separate scoring set of 100 raw milk samples has been used to assess the specificity (spc) and sensitivity (sens) of the approach. Of machine learning models tested, the gradient-boosted tree model gave global optimal results, with the Youden index of J = 0.7, sens = 0.89, and spc = 0.81 achieved for the given set of conditions. Random forest models also performed well, achieving J > 0.63, with sens = 0.83 and spc = 0.81. Naïve Bayes, generalized linear, fast large-margin, and deep learning models failed to produce diagnostic results that were as favorable. We conclude that MALDI-TOF MS combined with machine learning is an alternative diagnostic tool for detection of high somatic cell count (SCC) and subclinical mastitis in dairy herds.

Project description:MALDI-TOF mass spectrometry (MS) on whole cells enables the detection of different cell types and cell activation. Here, we wondered whether this approach would be useful to investigate the host response in sepsis.Peripheral blood mononuclear cells (PBMCs) from patients with severe sepsis and healthy donors were analyzed with MALDI-TOF MS. PBMCs from healthy donors were also stimulated with lipopolysaccharide, peptidoglycan, CpG oligonucleotides, polyinosinic polycytidylic acid, and with heat-inactivated bacteria. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software and matching scores between each spectrum from patients and averaged spectra from the database were calculated.We show that the MALDI-TOF MS signature of PBMCs from septic patients was specific, compared with healthy controls. As the fingerprints observed in patients may be related to PBMC activation, PBMCs from healthy controls were stimulated with cytokines, soluble Pathogen-Associated Molecular Patterns (PAMPs) and heat-killed bacteria, and we created a database of reference spectra. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. No differences were found between patients with documented infection (n?=?6) and those without bacteriological documentation (n?=?6). The spectra of PBMCs from septic patients matched with those of interferon-?- and interleukin-10-stimulated PBMCs, confirming that sepsis is characterized by both inflammatory and immunoregulatory features. Interestingly, the spectra of PBMCs from septic patients without documented infection matched with the reference spectrum of PBMCs stimulated by CpG oligonucleotides, suggesting a bacterial etiology in these patients.Despite the limits of this preliminary study, these results indicate that the monitoring of functional status of PBMCs in peripheral blood by whole cell MALDI-TOF MS could provide unique opportunities to assess disease progression or resolution in clinical settings.

Dataset Information

Fluorimetric Detection of Single Pathogenic Bacterium in Milk and Sewage Water Using pH-Sensitive Fluorescent Carbon Dots and MALDI-TOF MS.

Publications

Fluorimetric Detection of Single Pathogenic Bacterium in Milk and Sewage Water Using pH-Sensitive Fluorescent Carbon Dots and MALDI-TOF MS.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets