FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3214944438> ?p ?o ?g. }

• W3214944438 endingPage "e1008946" @default.
• W3214944438 startingPage "e1008946" @default.
• W3214944438 abstract "Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations-deformable and non-deformable sRBCs-utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring." @default.
• W3214944438 created "2021-12-06" @default.
• W3214944438 creator A5000219441 @default.
• W3214944438 creator A5019537430 @default.
• W3214944438 creator A5051732988 @default.
• W3214944438 creator A5059420437 @default.
• W3214944438 creator A5060298583 @default.
• W3214944438 creator A5071023609 @default.
• W3214944438 creator A5081678687 @default.
• W3214944438 date "2021-11-29" @default.
• W3214944438 modified "2023-10-14" @default.
• W3214944438 title "Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin" @default.
• W3214944438 cites W1584253018 @default.
• W3214944438 cites W1881216638 @default.
• W3214944438 cites W2053495832 @default.
• W3214944438 cites W2057631009 @default.
• W3214944438 cites W2100805904 @default.
• W3214944438 cites W2102605133 @default.
• W3214944438 cites W2108598243 @default.
• W3214944438 cites W2141426499 @default.
• W3214944438 cites W2146292423 @default.
• W3214944438 cites W2167575052 @default.
• W3214944438 cites W2169423263 @default.
• W3214944438 cites W2170505850 @default.
• W3214944438 cites W2194775991 @default.
• W3214944438 cites W2244253896 @default.
• W3214944438 cites W2281698256 @default.
• W3214944438 cites W2295107390 @default.
• W3214944438 cites W2298652832 @default.
• W3214944438 cites W2326476344 @default.
• W3214944438 cites W2531409750 @default.
• W3214944438 cites W2543380750 @default.
• W3214944438 cites W2555568021 @default.
• W3214944438 cites W2562103559 @default.
• W3214944438 cites W2562319768 @default.
• W3214944438 cites W2766119845 @default.
• W3214944438 cites W2793261083 @default.
• W3214944438 cites W2808384893 @default.
• W3214944438 cites W2902685885 @default.
• W3214944438 cites W2919886931 @default.
• W3214944438 cites W2954996726 @default.
• W3214944438 cites W2962858109 @default.
• W3214944438 cites W2963881378 @default.
• W3214944438 cites W2966502918 @default.
• W3214944438 cites W2981525295 @default.
• W3214944438 cites W3014641072 @default.
• W3214944438 cites W3022534807 @default.
• W3214944438 cites W3028092061 @default.
• W3214944438 cites W3030381113 @default.
• W3214944438 cites W3042825289 @default.
• W3214944438 cites W3132455321 @default.
• W3214944438 cites W4211186710 @default.
• W3214944438 doi "https://doi.org/10.1371/journal.pcbi.1008946" @default.
• W3214944438 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34843453" @default.
• W3214944438 hasPublicationYear "2021" @default.
• W3214944438 type Work @default.
• W3214944438 sameAs 3214944438 @default.
• W3214944438 citedByCount "10" @default.
• W3214944438 countsByYear W32149444382021 @default.
• W3214944438 countsByYear W32149444382022 @default.
• W3214944438 countsByYear W32149444382023 @default.
• W3214944438 crossrefType "journal-article" @default.
• W3214944438 hasAuthorship W3214944438A5000219441 @default.
• W3214944438 hasAuthorship W3214944438A5019537430 @default.
• W3214944438 hasAuthorship W3214944438A5051732988 @default.
• W3214944438 hasAuthorship W3214944438A5059420437 @default.
• W3214944438 hasAuthorship W3214944438A5060298583 @default.
• W3214944438 hasAuthorship W3214944438A5071023609 @default.
• W3214944438 hasAuthorship W3214944438A5081678687 @default.
• W3214944438 hasBestOaLocation W32149444381 @default.
• W3214944438 hasConcept C108583219 @default.
• W3214944438 hasConcept C136229726 @default.
• W3214944438 hasConcept C154945302 @default.
• W3214944438 hasConcept C171250308 @default.
• W3214944438 hasConcept C177212765 @default.
• W3214944438 hasConcept C192562407 @default.
• W3214944438 hasConcept C41008148 @default.
• W3214944438 hasConcept C70721500 @default.
• W3214944438 hasConcept C71924100 @default.
• W3214944438 hasConcept C77088390 @default.
• W3214944438 hasConcept C8673954 @default.
• W3214944438 hasConcept C86803240 @default.
• W3214944438 hasConcept C89600930 @default.
• W3214944438 hasConceptScore W3214944438C108583219 @default.
• W3214944438 hasConceptScore W3214944438C136229726 @default.
• W3214944438 hasConceptScore W3214944438C154945302 @default.
• W3214944438 hasConceptScore W3214944438C171250308 @default.
• W3214944438 hasConceptScore W3214944438C177212765 @default.
• W3214944438 hasConceptScore W3214944438C192562407 @default.
• W3214944438 hasConceptScore W3214944438C41008148 @default.
• W3214944438 hasConceptScore W3214944438C70721500 @default.
• W3214944438 hasConceptScore W3214944438C71924100 @default.
• W3214944438 hasConceptScore W3214944438C77088390 @default.
• W3214944438 hasConceptScore W3214944438C8673954 @default.
• W3214944438 hasConceptScore W3214944438C86803240 @default.
• W3214944438 hasConceptScore W3214944438C89600930 @default.
• W3214944438 hasFunder F4320306076 @default.
• W3214944438 hasFunder F4320332161 @default.

• next