The Julia database is for anyone developing sensors, biosensors and medical diagnostics. Julia organises, analyses, presents the lab/clinical data pushes the data to the Cloud.
Julia acts as the integration between collecting the data and making decisions based on the data. Julia is the extra member of the team busy administrating the data and prenseting it as actionable information.
The data in the Julia database is searchable, and so allows historical data to be quickly found and viewed; whilst automatically calculating the Figures of Merit so the programmes technology improvements can be quantified.
Zimmer and Peacock has a very systematic integrated workflow for developing and trouble shooting sensors and medical diagnostics. The workflow involves initially assessing the current state of the sensor with respect to parameters including precision and accuracy, and then systematically working through tactics and strategies to improve the signal or to identify and subsequently fix the source of the sensor/diagnostic issues.
The work is carefully tracked and logged through Julia.
Zimmer and Peacock have a core Julia product/service which allows us to gather and organise the data for the project, but as projects may have unique requirements we can tailor the database to process or display data in the most meaningful manner for the project in order to be understood by the project stakeholders.
Julia can be accessed through a secure portal so that work done and progress made can be tracked in real time by our collaborators and their stakeholders.
The focus of Julia is to accelerate the development and troubleshooting of sensors and diagnostics in the most efficient manner with respect to time and cost.
In the image opposite Zimmer and Peacock show that at the beginning of the programme a sensors when tested with varying concentrations of analyte of interest did not show a linear response with concentration.
Though a single sensors may have shown a response to analyte which was proportional to the concentration, the variability meant that any signal was lost within the sensor to sensor variability.
Julia allowed Zimmer and Peacock to plan work on how to improve the sensor, and then to work against the plan, and using figures of merit we were able to track progressive improvements, and to quickly judge what worked and what didn't work.
One of the strengths of Julia is that it is Cloud based and so it a centralizing tool for teams that at different sites or on different continents, as it allows everyone to contribute to the pool of knowledge and it's searchable functionality allows us to extract the value.