We created a web-based laboratory information system, e-Chasqui to connect public laboratories to health centers to improve communication and analysis. After one year, we performed a pre and post assessment of communication delays and found that e-Chasqui maintained the average delay but eliminated delays of over 60 days. Adding digital verification maintained the average delay, but should increase accuracy. We are currently performing a randomized evaluation of the impacts of e-Chasqui.
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A prediction model for electrical strength of gaseous medium based on molecular reactivity descriptors and machine learning method.
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Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
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Buck Institute for Research On Aging, Novato, CA, 94945, USA.
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