Evidence map›Paper›PMID 35035091›Full record

ArticleApplied intelligence (Dordrecht, Netherlands)2022

Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods.

M A Alsalem, O S Albahri, A A Zaidan, Jameel R Al-Obaidi, Alhamzah Alnoor, A H Alamoodi, A S Albahri, B B Zaidan, F M Jumaah

Open access · bronzeAbstract read
In one paragraph

Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.1field-weighted citation impact, top 6% 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

9 citing papers in PubMed, 29 citations in OpenAlex.

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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

9 authors at 4 institutions in 3 countries.

M A AlsalemDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia.
O S AlbahriDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia.ORCID 0000-0002-7844-3990
A A ZaidanDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia.
Jameel R Al-ObaidiDepartment of Biology, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia.
Alhamzah AlnoorSchool of Management, Universiti Sains Malaysia, 11800 Gelugor, Pulau Pinang Malaysia.
A H AlamoodiDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia.
A S AlbahriInformatics Institute for Postgraduate Studies (IIPS), Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
B B ZaidanDepartment of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia.
F M JumaahDepartment of Advanced Applications and Embedded Systems, Intel Corporation, Plot 6, 11900 Bayan Lepas Technoplex, Pulau Pinang Malaysia.
Sultan Idris Education University · MYIntel (Malaysia) · MYIraqi University · IQUniversiti Sains Malaysia · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and

Indexed as

AHPCOVID-19Mesenchymal stem cell therapyMulticriteria decision-makingVIKOR

Identifiers

PMID35035091
PMCPMC8741536
OpenAlexW4205119394

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.