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10.1038/s41598-021-97882-4 http://scihub22266oqcxt.onion/10.1038/s41598-021-97882-4 34531449!8446005!34531449     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34531449&cmd=llinks): Failed to open stream: HTTP request failed! 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Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques |pdf=|usr=}}{{34531449}} gab.com Text Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34531449 #FOAvip 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. Twit Text FOAVip Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34531449 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34531449 #FOAvip English Wikipedia <ref>{{Cite pmid|34531449}}</ref> Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques #MMPMID34531449 Ciloglu FU; Caliskan A; Saridag AM; Kilic IH; Tokmakci M; Kahraman M; Aydin O Sci Rep 2021[Sep]; 11 (1): 18444 PMID34531449show ga 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. |*Methicillin Resistance[MESH] |Deep Learning[MESH] |Discriminant Analysis[MESH] |Humans[MESH] |Metal Nanoparticles/chemistry[MESH] |Microbial Sensitivity Tests[MESH] |Neural Networks, Computer[MESH] |Signal-To-Noise Ratio[MESH] |Silver/chemistry[MESH] |Spectrum Analysis, Raman[MESH] |Staphylococcus aureus/*classification/drug effects/*growth & development[MESH]Drug-resistant Staphylococcus aureus bacteria detection by combining (epmc).pdf DeepDyve Pubget Overpricing