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

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          Abstract

          Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.

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          Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools.

          Aydogan Ozcan (2014)
          In this article, I discuss some of the emerging applications and the future opportunities and challenges created by the use of mobile phones and their embedded components for the development of next-generation imaging, sensing, diagnostics and measurement tools. The massive volume of mobile phone users, which has now reached ~7 billion, drives the rapid improvements of the hardware, software and high-end imaging and sensing technologies embedded in our phones, transforming the mobile phone into a cost-effective and yet extremely powerful platform to run, e.g., biomedical tests, and perform scientific measurements that would normally require advanced laboratory instruments. This rapidly evolving and continuing trend will help us transform how medicine, engineering and sciences are practiced and taught globally.
          Article 0 comments Cited 90 times – based on 0 reviews     Review now
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            Economic Savings for Scientific Free and Open Source Technology: A Review

            Joshua Pearce (2020)
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              Redox Biology in Transition Periods of Dairy Cattle: Role in the Health of Periparturient and Neonatal Animals

              Ángel Abuelo, Joaquín Hernández, José L Benedito … (2019)
              Dairy cows undergo various transition periods throughout their productive life, which are associated with periods of increased metabolic and infectious disease susceptibility. Redox balance plays a key role in ensuring a satisfactory transition. Nevertheless, oxidative stress (OS), a consequence of redox imbalance, has been associated with an increased risk of disease in these animals. In the productive cycle of dairy cows, the periparturient and neonatal periods are times of increased OS and disease susceptibility. This article reviews the relationship of redox status and OS with diseases of cows and calves, and how supplementation with antioxidants can be used to prevent OS in these animals.
              Article 0 comments Cited 32 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Guillermo Villanueva: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 20 May 2021
                Publication date Collection: May 2021
                Volume: 21
                Issue: 10
                Electronic Location Identifier: 3552
                Affiliations
                [1 ]DNAPhone S.R.L., Viale Mentana 150, 43121 Parma, Italy; alessandro.tonelli@ 123456dnaphone.it (A.T.); Veronicamangia93@ 123456gmail.com (V.M.); alessandro.candiani@ 123456dnaphone.it (A.C.); francesco.pasquali@ 123456dnaphone.it (F.P.); jessica.mangiaracina@ 123456dnaphone.it (T.J.M.); alessandro.grazioli@ 123456dnaphone.it (A.G.); michele.sozzi@ 123456dnaphone.it (M.S.)
                [2 ]H&D S.R.L., Strada Langhirano 264/1a, 43124 Parma, Italy; d.gorni@ 123456hedsrl.it
                [3 ]Dipartimento di Scienze Medico-Veterinarie, Via del Taglio 10, 43126 Parma, Italy; simona.bussolati@ 123456unipr.it (S.B.); giuseppina.basini@ 123456unipr.it (G.B.)
                [4 ]Dipartimento di Ingegneria e Architettura, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy; annamaria.cucinotta@ 123456unipr.it
                Author notes
                [* ]Correspondence: stefano.selleri@ 123456unipr.it ; Tel.: +39-052-190-5763
                Author information
                https://orcid.org/0000-0002-0440-9407
                https://orcid.org/0000-0002-2429-9110
                https://orcid.org/0000-0003-1571-7023
                https://orcid.org/0000-0001-8026-0846
                Article
                Publisher ID: sensors-21-03552
                DOI: 10.3390/s21103552
                PMC ID: 8160707
                SO-VID: 72c186fd-a7c8-434f-9f31-294ae436d7a3
                Copyright © © 2021 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 31 March 2021
                Date accepted : 16 May 2021
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: raspberry pi,cmos,raw data,photometric analysis,imaging,oxidative stress,d-rom fast,pat,horse blood
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: raspberry pi, cmos, raw data, photometric analysis, imaging, oxidative stress, d-rom fast, pat, horse blood

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