A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

Surface-structured bacterial cellulose with guided assembly-based biolithography (GAB).

A powerful replica molding methodology to transfer on-demand functional topographies to the surface of bacterial cellulose nanofiber textures is presented. With this method, termed guided assembly-based biolithography (GAB), a surface-structured polydimethylsiloxane (PDMS) mold is introduced at the gas-liquid interface of an Acetobacter xylinum culture. Upon bacterial fermentation, the generated bacterial cellulose nanofibers are assembled in a three-dimensional network reproducing the geometric shape imposed by the mold. Additionally, GAB yields directional alignment of individual nanofibers and memory of the transferred geometrical features upon dehydration and rehydration of the substrates. Scanning electron and atomic force microscopy are used to establish the good fidelity of this facile and affordable method. Interaction of surface-structured bacterial cellulose substrates with human fibroblasts and keratinocytes illustrates the efficient control of cellular activities which are fundamental in skin wound healing and tissue regeneration. The deployment of surface-structured bacterial cellulose substrates in model animals as skin wound dressing or body implant further proves the high durability and low inflammatory response to the material over a period of 21 days, demonstrating beneficial effects of surface structure on skin regeneration.