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10.1021/acsnano.0c06807 http://scihub22266oqcxt.onion/10.1021/acsnano.0c06807 33226787!8299938!33226787     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ACS+Nano 2021 ; 15 (1): 665-673 Nephropedia Template TP {{tp|p=33226787|t=2021. Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning |pdf=|usr=}}{{33226787}} gab.com Text Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning JO: ACS Nano http://www.kidney.de/pget.php?&tw=1&pmid=33226787 #FOAvip Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work. Twit Text FOAVip Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning ????ACS Nano http://www.kidney.de/pget.php?&tw=1&pmid=33226787 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33226787 #FOAvip English Wikipedia <ref>{{Cite pmid|33226787}}</ref> Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning #MMPMID33226787 Shokr A; Pacheco LGC; Thirumalaraju P; Kanakasabapathy MK; Gandhi J; Kartik D; Silva FSR; Erdogmus E; Kandula H; Luo S; Yu XG; Chung RT; Li JZ; Kuritzkes DR; Shafiee H ACS Nano 2021[Jan]; 15 (1): 665-673 PMID33226787show ga Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work. |*Deep Learning[MESH] |*Signal Processing, Computer-Assisted[MESH] |Antigens, Viral/isolation & purification[MESH] |COVID-19 Testing/*instrumentation/*methods[MESH] |COVID-19/*diagnosis[MESH] |CRISPR-Cas Systems[MESH] |Communicable Disease Control[MESH] |Disaster Planning[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/methods[MESH] |Metal Nanoparticles/chemistry[MESH] |Neural Networks, Computer[MESH] |Platinum[MESH] |Point-of-Care Testing[MESH] |Public Health[MESH] |Reproducibility of Results[MESH] |Smartphone[MESH]Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Advers(epmc).pdf DeepDyve Pubget Overpricing