Literature DB >> 29746790

MVO Automation Platform: Addressing Unmet Needs in Clinical Laboratories with Microcontrollers, 3D Printing, and Open-Source Hardware/Software.

Brian Iglehart1.   

Abstract

Laboratory automation improves test reproducibility, which is vital to patient care in clinical laboratories. Many small and specialty laboratories are excluded from the benefits of automation due to low sample number, cost, space, and/or lack of automation expertise. The Minimum Viable Option (MVO) automation platform was developed to address these hurdles and fulfill an unmet need. Consumer 3D printing enabled rapid iterative prototyping to allow for a variety of instrumentation and assay setups and procedures. Three MVO versions have been produced. MVOv1.1 successfully performed part of a clinical assay, and results were comparable to those of commercial automation. Raspberry Pi 3 Model B (RPI3) single-board computers with Sense Hardware Attached on Top (HAT) and Raspberry Pi Camera Module V2 hardware were remotely accessed and evaluated for their suitability to qualify the latest MVOv1.2 platform. Sense HAT temperature, barometric pressure, and relative humidity sensors were stable in climate-controlled environments and are useful in identifying appropriate laboratory spaces for automation placement. The RPI3 with camera plus digital dial indicator logged axis travel experiments. RPI3 with camera and Sense HAT as a light source showed promise when used for photometric dispensing tests. Individual well standard curves were necessary for well-to-well light and path length compensations.

Entities:  

Keywords:  3D printing; IoT; automation; microcontroller; open source

Mesh:

Year:  2018        PMID: 29746790     DOI: 10.1177/2472630318773693

Source DB:  PubMed          Journal:  SLAS Technol        ISSN: 2472-6303            Impact factor:   3.047


  2 in total

1.  OpenWorkstation: A modular open-source technology for automated in vitro workflows.

Authors:  Sebastian Eggert; Pawel Mieszczanek; Christoph Meinert; Dietmar W Hutmacher
Journal:  HardwareX       Date:  2020-10-20

2.  Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications.

Authors:  Alessandro Tonelli; Veronica Mangia; Alessandro Candiani; Francesco Pasquali; Tiziana Jessica Mangiaracina; Alessandro Grazioli; Michele Sozzi; Davide Gorni; Simona Bussolati; Annamaria Cucinotta; Giuseppina Basini; Stefano Selleri
Journal:  Sensors (Basel)       Date:  2021-05-20       Impact factor: 3.576

  2 in total

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