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Record W4296753451 · doi:10.3390/v14092056

CD163-Expressing Porcine Macrophages Support NADC30-like and NADC34-like PRRSV Infections

2022· article· en· W4296753451 on OpenAlexaff
Yulin Xu, Mengxue Ye, Shaohua Sun, Qi Cao, Jia Luo, Yuening Wang, Wanglong Zheng, François Meurens, Nanhua Chen, Jianzhong Zhu

Bibliographic record

VenueViruses · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of Saskatchewan
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of China
KeywordsCD163Porcine reproductive and respiratory syndrome virusBiologyVirologyTropismVirusCell cultureMacrophageGenetics

Abstract

fetched live from OpenAlex

Porcine reproductive and respiratory syndrome virus (PRRSV) has a strict cell tropism. In addition to the primary alveolar macrophages, PRRSV is strictly cytotropic to African green monkey kidney cells, such as MARC-145 cells; however, MARC-145 cells are not infected by most NADC30-like and NADC34-like PRRSV strains. The essential scavenger receptor CD163 has been proved to mediate productive infection of PRRSV in various non-permissive cell lines. In this study, we systematically tested the porcine CD163 stably expressing 3D4/21 cells for infections with various PRRSV strains. The results showed that the porcine CD163-expressing macrophages support the infections of PRRSV2 of lineages 1, 5, and 8, as evidenced by Western blotting, immunofluorescence assay, quantitative PCR, and virus titration assay. Considering the current prevalence of NADC30-like and NADC34-like PRRSV2 of lineage 1 in China, the CD163-expressing macrophages are very useful for PRRSV research and disease management.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.251
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2022
Admission routes1
Has abstractyes

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