Evidence map›Paper›PMID 38930678›Full record

ArticleMicromachines2024

An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings.

Miren Urrutia Iturritza, Phuthumani Mlotshwa, Jesper Gantelius, Tobias Alfvén, Edmund Loh, Jens Karlsson, Chris Hadjineophytou, Krzysztof Langer, Konstantinos Mitsakakis, Aman Russom and 2 more

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Miren Urrutia IturritzaDepartment of Global Public Health, Karolinska Institutet, 17177 Stockholm, Sweden.
Phuthumani MlotshwaDepartment of Global Public Health, Karolinska Institutet, 17177 Stockholm, Sweden.ORCID 0000-0002-5967-5657
Jesper GanteliusDepartment of Global Public Health, Karolinska Institutet, 17177 Stockholm, Sweden.
Tobias AlfvénDepartment of Global Public Health, Karolinska Institutet, 17177 Stockholm, Sweden.ORCID 0000-0002-2328-3512
Edmund LohDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17165 Stockholm, Sweden.ORCID 0000-0001-7050-566X
Jens KarlssonDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17165 Stockholm, Sweden.ORCID 0000-0002-8005-9197
Chris HadjineophytouDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17165 Stockholm, Sweden.
Krzysztof LangerScience for Life Laboratory, Division of Nanobiotechnology, Department of Protein Science, KTH Royal Institute of Technology, 17165 Stockholm, Sweden.
Konstantinos MitsakakisHahn-Schickard, Georges-Koehler-Allee 103, 79110 Freiburg, Germany.ORCID 0000-0002-3287-9123
Aman RussomScience for Life Laboratory, Division of Nanobiotechnology, Department of Protein Science, KTH Royal Institute of Technology, 17165 Stockholm, Sweden.
Håkan N JönssonScience for Life Laboratory, Division of Nanobiotechnology, Department of Protein Science, KTH Royal Institute of Technology, 17165 Stockholm, Sweden.
Giulia GaudenziDepartment of Global Public Health, Karolinska Institutet, 17177 Stockholm, Sweden.ORCID 0000-0003-4923-6965

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost

Indexed as

infectious diseasesmicroarraymodular automationopen-sourcerecombinase polymerase amplificationsignal enhancement

Identifiers

PMID38930678
PMCPMC11205418

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.