RESUMO
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.
RESUMO
Due to their ease of fabrication and mechanical flexibility, silver nanowire transparent electrodes have been touted as a promising replacement for metal oxides such as indium tin oxide (ITO). Here we study an additional advantage: their high transparency in the near-infrared region (NIR) which is highly desirable for some applications. For electrodes that are 96% transparent in the visible, ones made from ITO are only 35% transparent at a wavelength of 2500 nm, but those made from silver nanowires maintain a transparency as high as 94%. Experiments and modelling show that to minimize the transparency drop from the visible to the NIR, the nanowires should be sparse and larger in diameter. This is found to be attributed to both the larger average spacing between nanowires in such networks and the lower absoprtion losses of larger diameter nanowires in the NIR.
RESUMO
This article presents an overview of common cardiac arrhythmias. Interventions for severe and potentially severe arrhythmias are discussed. Tips for recording and viewing the electrocardiogram as employed in polysomnography are presented. The focus is on arrhythmias, and as such the information revolves around rhythm, rate, and blocks. The reader is referred to other sources for discussion of topics such as axis, ischemia, and infarct.
