Evidence map›Paper›PMID 36199700›Full record

ArticleBio-protocol2022

A Semi-quantitative Scoring System for Green Histopathological Evaluation of Large Animal Models of Acute Lung Injury.

Iran A N Silva, Nika Gvazava, Deniz A Bölükbas, Martin Stenlo, Jiao Dong, Snejana Hyllen, Leif Pierre, Sandra Lindstedt, Darcy E Wagner

Open access · greenAbstract read
In one paragraph

Article in Bio-protocol, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.5field-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

13 citing papers in PubMed, 26 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 3 institutions in 2 countries.

Iran A N SilvaLung Bioengineering and Regeneration, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Nika GvazavaLung Bioengineering and Regeneration, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Deniz A BölükbasLung Bioengineering and Regeneration, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Martin StenloWallenberg Center for Molecular Medicine, Lund University, Lund, Sweden.
Jiao DongLung Bioengineering and Regeneration, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Snejana HyllenDepartment of Cardiothoracic Anesthesia and Intensive Care, Skåne University Hospital, Lund, Sweden.
Leif PierreDepartment of Cardiothoracic Surgery, Heart and Lung Transplantation, Skåne University Hospital, Lund, Sweden.
Sandra LindstedtWallenberg Center for Molecular Medicine, Lund University, Lund, Sweden.
Darcy E WagnerLung Bioengineering and Regeneration, Department of Experimental Medical Sciences, Lund University, Lund, Sweden.
Lund University · SENagoya University · JPSkåne University Hospital · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute respiratory distress syndrome (ARDS) is a life-threatening, high mortality pulmonary condition characterized by acute lung injury (ALI) resulting in diffuse alveolar damage. Despite progress regarding the understanding of ARDS pathophysiology, there are presently no effective pharmacotherapies. Due to the complexity and multiorgan involvement typically associated with ARDS, animal models remain the most commonly used research tool for investigating potential new therapies. Experimental models of ALI/ARDS use different methods of injury to acutely induce lung damage in both small and large animals. These models have historically played an important role in the development of new clinical interventions, such as fluid therapy and the use of supportive mechanical ventilation (MV). However, failures in recent clinical trials have highlighted the potential inadequacy of small animal models due to major anatomical and physiological differences, as well as technical challenges associated with the use of clinical co-interventions [e.g., MV and extracorporeal membrane oxygenation (ECMO)]. Thus, there is a need for larger animal models of ALI/ARDS, to allow the incorporation of clinically relevant measurements and co-interventions, hopefully leading to improved rates of clinical translation. However, one of the main challenges in using large animal models of preclinical research is that fewer species-specific experimental tools and metrics are available for evaluating the extent of lung injury, as compared to rodent models. One of the most relevant indicators of ALI in all animal models is evidence of histological tissue damage, and while histological scoring systems exist for small animal models, these cannot frequently be readily applied to large animal models. Histological injury in these models differs due to the type and severity of the injury being modeled. Additionally, the incorporation of other clinical support devices such as MV and ECMO in large animal models can lead to further lung damage and appearance of features absent in the small animal models. Therefore, semi-quantitative histological scoring systems designed to evaluate tissue-level injury in large animal models of ALI/ARDS are needed. Here we describe a semi-quantitative scoring system to evaluate histological injury using a previously established porcine model of ALI via intratracheal and intravascular lipopolysaccharide (LPS) administration. Additionally, and owing to the higher number of samples generated from large animal models, we worked to implement a more sustainable and greener histopathological workflow throughout the entire process.

Indexed as

Acute lung injuryAcute respiratory distress syndromeGreen histologyHistological score systemLarge animal model

Identifiers

PMID36199700
PMCPMC9486691
OpenAlexW4292510277

What OpenQuestion holds

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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.