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Record W2912188733 · doi:10.3892/mmr.2019.9884

Identification of critical genes associated with spinal cord injury based on the gene expression profile of spinal cord tissues from trkB.T1 knockout mice

2019· article· en· W2912188733 on OpenAlexfundno aff
Wei Li, Fei He, Wen Zhang, Wenhua Chen, Bo Yu

Bibliographic record

VenueMolecular Medicine Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersScience and Technology Commission of Shanghai MunicipalityOttawa Hospital Research Institute
KeywordsTropomyosin receptor kinase BBiologySpinal cord injuryOncogeneKnockout mouseMolecular medicineGene knockoutGene expressionSpinal cordGeneReceptorCell cycleGeneticsNeurotrophic factorsNeuroscience

Abstract

fetched live from OpenAlex

The present study aimed to identify the genes and underlying mechanisms critical to the pathology of spinal cord injury (SCI). Gene expression profiles of spinal cord tissues of trkB.T1 knockout (KO) mice following SCI were accessible from the Gene Expression Omnibus database. Compared with trkB.T1 wild type (WT) mice, the differentially expressed genes (DEGs) in trkB.T1 KO mice following injury at different time points were screened out. The significant DEGs were subjected to function, co‑expression and protein‑protein interaction (PPI) network analyses. A total of 664 DEGs in the sham group and SCI groups at days 1, 3, and 7 following injury were identified. Construction of a Venn diagram revealed the overlap of several DEGs in trkB.T1 KO mice under different conditions. In total, four modules (Magenta, Purple, Brown and Blue) in a co‑expression network were found to be significant. Protein tyrosine phosphatase, receptor type C (PTPRC), coagulation factor II, thrombin (F2), and plasminogen (PLG) were the most significant nodes in the PPI network. 'Fc γ R‑mediated phagocytosis' and 'complement and coagulation cascades' were the significant pathways enriched by genes in the PPI and co‑expression networks. The results of the present study identified PTPRC, F2 and PLG as potential targets for SCI treatment, which may further improve the general understanding of SCI pathology.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.061
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.383
Teacher spread0.349 · 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 teacher head, 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

Citations5
Published2019
Admission routes1
Has abstractyes

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