Evidence map›Paper›PMID 35058197›Full record

ArticleSLAS technology2022

AI-driven laboratory workflows enable operation in the age of social distancing.

Diego Marescotti, Chandrasekaran Narayanamoorthy, Filipe Bonjour, Ken Kuwae, Luc Graber, Florian Calvino-Martin, Samik Ghosh, Julia Hoeng

Open access · diamondAbstract read
In one paragraph

Article in SLAS technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
9.7field-weighted citation impact, top 3% of its field
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

6 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
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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

8 authors at 1 institution in 1 country.

Diego MarescottiPMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland. Electronic address: Diego.marescotti@pmi.com.
Chandrasekaran NarayanamoorthySBX Corporation, 5-10-25 Higashi Gotanda, Shinagawa, Tokyo 141-0022, Japan.
Filipe BonjourPMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland.
Ken KuwaeSBX Corporation, 5-10-25 Higashi Gotanda, Shinagawa, Tokyo 141-0022, Japan.
Luc GraberPMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland.
Florian Calvino-MartinPMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland.
Samik GhoshSBX Corporation, 5-10-25 Higashi Gotanda, Shinagawa, Tokyo 141-0022, Japan.
Julia HoengPMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, Neuchâtel CH-2000, Switzerland.
Philip Morris International (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 (Coronavirus disease 2019) global pandemic has upended the normal pace of society at multiple levels-from daily activities in personal and professional lives to the way the sciences operate. Many laboratories have reported shortage in vital supplies, change in standard operating protocols, suspension of operations because of social distancing and stay-at-home guidelines during the pandemic. This global crisis has opened opportunities to leverage internet of things, connectivity, and artificial intelligence (AI) to build a connected laboratory automation platform. However, laboratory operations involve complex, multicomponent systems. It is unrealistic to completely automate the entire diversity of laboratories and processes. Recently, AI technology, particularly, game simulation has made significant strides in modeling and learning complex, multicomponent systems. Here, we present a cloud-based laboratory management and automation platform which combines multilayer information on a simulation-driven inference engine to plan and optimize laboratory operations under various constraints of COVID-19 and risk scenarios. The platform was used to assess the execution of two cell-based assays with distinct parameters in a real-life high-content screening laboratory scenario. The results show that the platform can provide a systematic framework for assessing laboratory operation scenarios under different conditions, quantifying tradeoffs, and determining the performance impact of specific resources or constraints, thereby enabling decision-making in a cost-effective manner. We envisage the laboratory management and automation platform to be further expanded by connecting it with sensors, robotic equipment, and other components of scientific operations to provide an integrated, end-to-end platform for scientific laboratory automation.

Indexed as

COVID-19Physical DistancingArtificial IntelligenceHumansLaboratoriesWorkflowLaboratory planningMachine learning

Identifiers

PMID35058197
PMCPMC8679500
OpenAlexW4205751574

What OpenQuestion holds

Textmetadata
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.