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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308575

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 other 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 with other 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. The proposed method is a label-free, easy implemented, and reliable technique with high sensitivity for clinical use.

2.
Acta Otolaryngol ; 141(11): 989-993, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1479845

ABSTRACT

BACKGROUND: The effect of Covid-19 infection on nasal mucociliary clearance (MCC) is unknown. AIMS/OBJECTIVES: The aim of this study is to investigate the relationship between Covid-19 and nasal MCC in terms of smoking, Covid-19 symptoms and treatment. METHODS: Thirty-six patients who were hospitalized in the pandemic ward due to Covid-19 and 36 volunteers (Covid-19 negative test result) who presented to the otolaryngology outpatient clinic with non-nasal symptoms were included in this study. The Saccharin test was performed in both groups to evaluate nasal MCC. RESULTS: The patients and control groups were not significantly different in terms of age and gender. The nasal MCC time was significantly higher in the patient group compared to the control group (19.18 ± 10.84 min and 13.78 ± 8.18 min, p = .003). CONCLUSIONS AND SIGNIFICANCE: In this study, we found that Covid-19 prolonged nasal MCC time regardless of age. We suggest that corticosteroids should be included in the treatment of Covid-19, both with its symptom reduction and its positive effect on MCC duration.


Subject(s)
COVID-19/physiopathology , Mucociliary Clearance/physiology , Nasal Mucosa/physiopathology , Smoking/physiopathology , Adrenal Cortex Hormones/pharmacology , Adrenal Cortex Hormones/therapeutic use , Adult , Amides/therapeutic use , Antiviral Agents/therapeutic use , COVID-19/complications , COVID-19/drug therapy , Case-Control Studies , Female , Humans , Hydroxychloroquine/adverse effects , Length of Stay , Male , Middle Aged , Mucociliary Clearance/drug effects , Pyrazines/therapeutic use
3.
Sci Rep ; 11(1): 18444, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1415956

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.


Subject(s)
Methicillin Resistance , Staphylococcus aureus/classification , Staphylococcus aureus/growth & development , Deep Learning , Discriminant Analysis , Humans , Metal Nanoparticles/chemistry , Microbial Sensitivity Tests , Neural Networks, Computer , Signal-To-Noise Ratio , Silver/chemistry , Spectrum Analysis, Raman , Staphylococcus aureus/drug effects , Support Vector Machine
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