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Article ; Online: Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures.

Al, Serhat / Uysal Ciloglu, Fatma / Akcay, Aytac / Koluman, Ahmet

2023 Volume 210, Page(s) 109421

Abstract: Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at ...

Abstract	Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0-96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R
MeSH term(s)	Animals ; Cattle ; Escherichia coli O157 ; Temperature ; Meat Products/microbiology ; Colony Count, Microbial ; Food Contamination/prevention & control ; Food Contamination/analysis ; Food Microbiology ; Shiga-Toxigenic Escherichia coli
Language	English
Publishing date	2023-12-30
Publishing country	England
Document type	Journal Article
ZDB-ID	753319-6
ISSN	1873-4138 ; 0309-1740
ISSN (online)	1873-4138
ISSN	0309-1740
DOI	10.1016/j.meatsci.2023.109421
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms.

Al-Shaebi, Zakarya / Uysal Ciloglu, Fatma / Nasser, Mohammed / Aydin, Omer

ACS omega

2022 Volume 7, Issue 33, Page(s) 29443–29451

Abstract: Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. ... ...

Abstract	Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.
Language	English
Publishing date	2022-08-12
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.2c03856
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Investigation of mammalian cells expressing SARS-CoV-2 proteins by surface-enhanced Raman scattering and multivariate analysis.

Akdeniz, Munevver / Uysal Ciloglu, Fatma / Tunc, Cansu Umran / Yilmaz, Ummugulsum / Kanarya, Dilek / Atalay, Pinar / Aydin, Omer

The Analyst

2022 Volume 147, Issue 6, Page(s) 1213–1221

Abstract: COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. ...

Abstract	COVID-19 has caused millions of cases and deaths all over the world since late 2019. Rapid detection of the virus is crucial for controlling its spread through a population. COVID-19 is currently detected by nucleic acid-based tests and serological tests. However, these methods have limitations such as the requirement of high-cost reagents, false negative results and being time consuming. Surface-enhanced Raman scattering (SERS), which is a powerful technique that enhances the Raman signals of molecules using plasmonic nanostructures, can overcome these disadvantages. In this study, we developed a virus-infected cell model and analyzed this model by SERS combined with Principal Component Analysis (PCA). HEK293 cells were transfected with plasmids encoding the nucleocapsid (N), membrane (M) and envelope (E) proteins of SARS-CoV-2
MeSH term(s)	Animals ; COVID-19/diagnosis ; Gold/chemistry ; HEK293 Cells ; Humans ; Metal Nanoparticles/chemistry ; Multivariate Analysis ; SARS-CoV-2/genetics ; Spectrum Analysis, Raman/methods
Chemical Substances	Gold (7440-57-5)
Language	English
Publishing date	2022-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	210747-8
ISSN	1364-5528 ; 0003-2654
ISSN (online)	1364-5528
ISSN	0003-2654
DOI	10.1039/d1an01989a
Database	MEDical Literature Analysis and Retrieval System OnLINE

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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: SERS-based sensor with a machine learning based effective feature extraction technique for fast detection of colistin-resistant Klebsiella pneumoniae.

Ciloglu, Fatma Uysal / Hora, Mehmet / Gundogdu, Aycan / Kahraman, Mehmet / Tokmakci, Mahmut / Aydin, Omer

Analytica chimica acta

2022 Volume 1221, Page(s) 340094

Abstract: Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and ... ...

Abstract	Colistin-resistant Klebsiella pneumoniae (ColR-Kp) causes high mortality rates since colistin is used as the last-line antibiotic against multi-drug resistant Gram-negative bacteria. To reduce infections and mortality rates caused by ColR-Kp fast and reliable detection techniques are vital. In this study, we used a label-free surface-enhanced Raman scattering (SERS)-based sensor with machine learning algorithms to discriminate colistin-resistant and susceptible strains of K. pneumoniae. A total of 16 K. pneumoniae strains were incubated in tryptic soy broth (TSB) for 4 h. Collected SERS spectra of ColR-Kp and colistin susceptible K. pneumoniae (ColS-Kp) have shown some spectral differences that hard to discriminate by the naked eye. To extract discriminative features from the dataset, autoencoder and principal component analysis (PCA) that extract features in a non-linear and linear manner, respectively were performed. Extracted features were fed into the support vector machine (SVM) classifier to discriminate K. pneumoniae strains. Classifier performance was evaluated by using features extracted by each feature extraction techniques. Classification results of SVM classifier with extracted features by an autoencoder (autoencoder-SVM) has shown better performance than SVM classifier with extracted features by PCA (PCA-SVM). The accuracy, sensitivity, specificity, and area under curve (AUC) value of the autoencoder-SVM model were found as 94%, 94.2%, 93.8%, and 0.98, respectively. Furthermore, the autoencoder-SVM model has demonstrated statistically significantly better classifier performance than PCA-SVM in terms of accuracy and AUC values. These results illustrate that non-linear features can be more discriminative than linear ones to determine SERS spectral data of antibiotic-resistant and susceptible bacteria. Our methodological approach enables rapid and high accuracy detection of ColR-Kp and ColS-Kp, suggesting that this can be a promising tool to limit colistin resistance.
MeSH term(s)	Anti-Bacterial Agents/pharmacology ; Colistin/pharmacology ; Humans ; Klebsiella Infections/drug therapy ; Klebsiella Infections/microbiology ; Klebsiella pneumoniae ; Machine Learning ; Microbial Sensitivity Tests
Chemical Substances	Anti-Bacterial Agents ; Colistin (Z67X93HJG1)
Language	English
Publishing date	2022-06-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1483436-4
ISSN	1873-4324 ; 0003-2670
ISSN (online)	1873-4324
ISSN	0003-2670
DOI	10.1016/j.aca.2022.340094
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering.

Arslan, Afra Hacer / Ciloglu, Fatma Uysal / Yilmaz, Ummugulsum / Simsek, Emrah / Aydin, Omer

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

2021 Volume 267, Issue Pt 1, Page(s) 120475

Abstract: Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these ...

Abstract	Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.
MeSH term(s)	Animals ; Bacteria ; Cluster Analysis ; Cryptosporidiosis ; Cryptosporidium ; Cryptosporidium parvum ; Escherichia coli ; Humans ; Metal Nanoparticles ; Oocysts ; Principal Component Analysis ; Silver ; Spectrum Analysis, Raman ; Staphylococcus aureus
Chemical Substances	Silver (3M4G523W1G)
Language	English
Publishing date	2021-10-08
Publishing country	England
Document type	Journal Article
ZDB-ID	210413-1
ISSN	1873-3557 ; 0370-8322 ; 0584-8539 ; 1386-1425
ISSN (online)	1873-3557
ISSN	0370-8322 ; 0584-8539 ; 1386-1425
DOI	10.1016/j.saa.2021.120475
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identification of methicillin-resistant

Uysal Ciloglu, Fatma / Saridag, Ayse Mine / Kilic, Ibrahim Halil / Tokmakci, Mahmut / Kahraman, Mehmet / Aydin, Omer

The Analyst

2020 Volume 145, Issue 23, Page(s) 7559–7570

Abstract: To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for ... ...

Abstract	To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.
MeSH term(s)	Anti-Bacterial Agents/pharmacology ; Bacteria ; Bayes Theorem ; Humans ; Machine Learning ; Methicillin-Resistant Staphylococcus aureus ; Microbial Sensitivity Tests ; Reproducibility of Results ; Spectrum Analysis, Raman ; Staphylococcal Infections ; Staphylococcus aureus
Chemical Substances	Anti-Bacterial Agents
Language	English
Publishing date	2020-11-02
Publishing country	England
Document type	Journal Article
ZDB-ID	210747-8
ISSN	1364-5528 ; 0003-2654
ISSN (online)	1364-5528
ISSN	0003-2654
DOI	10.1039/d0an00476f
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 2423: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Ciloglu, Fatma Uysal / Caliskan, Abdullah / Saridag, Ayse Mine / Kilic, Ibrahim Halil / Tokmakci, Mahmut / Kahraman, Mehmet / Aydin, Omer

Scientific reports

2021 Volume 11, Issue 1, Page(s) 18444

Abstract: Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing ... ...

Abstract	Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.
MeSH term(s)	Deep Learning ; Discriminant Analysis ; Humans ; Metal Nanoparticles/chemistry ; Methicillin Resistance ; Microbial Sensitivity Tests ; Neural Networks, Computer ; Signal-To-Noise Ratio ; Silver/chemistry ; Spectrum Analysis, Raman ; Staphylococcus aureus/classification ; Staphylococcus aureus/drug effects ; Staphylococcus aureus/growth & development ; Support Vector Machine
Chemical Substances	Silver (3M4G523W1G)
Language	English
Publishing date	2021-09-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-97882-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques

Fatma Uysal Ciloglu / Abdullah Caliskan / Ayse Mine Saridag / Ibrahim Halil Kilic / Mahmut Tokmakci / Mehmet Kahraman / Omer Aydin

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 12

Abstract: Abstract Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in ... ...

Abstract	Abstract Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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