MétaCan
Menu
Back to cohort
Record W2904261177 · doi:10.3906/sag-1806-223

Expression changes of CD177 and MPO as novel biomarkers in lung tissue of CLP model rats

2018· article· en· W2904261177 on OpenAlexaff
Azadeh Rasooli, Elham Ghafari, Hamed Saedi, Saba Miri

Bibliographic record

VenueTURKISH JOURNAL OF MEDICAL SCIENCES · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMyeloperoxidaseSepsisMedicineLungBiomarkerPathologyImmunologyInflammationInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Background/aim: Sepsis is an unregulated systemic response to microbial invasion that can lead to multiple organ failure. This study aims at investigating the relationships among myeloperoxidase (MPO) and CD177 in major organ systems including whole blood, liver, and lung tissues in septic rats. Materials and methods: Sepsis was induced by cecal ligation and puncture (CLP) in female Wistar rats. Whole blood, liver, and lung samples were obtained from rats of 3 groups (n = 10 for each group, n total = 30: control as a wild-type group, laparotomy group (LAP), and CLP). Gene expression of MPO and CD177 in targeted tissues was determined by real-time PCR after CLP. MPO activity was also determined by ELISA method for the result validation of the real-time PCR. Results: Expression levels of MPO increased significantly in all targeted organs in the CLP group, while CD177 expression was upregulated only in lung tissue in response to sepsis (P < 0.05). The results obtained with ELISA analysis also show that MPO level was significantly increased in all the targeted tissues in the CLP group (P < 0.05). Conclusion: A high level of MPO as an inflammatory enzyme can be a potentially novel biomarker for sepsis in all organs. On the other hand, CD177 may be a marker in lung tissue.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.323
Teacher spread0.287 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTURKISH JOURNAL OF MEDICAL SCIENCESSame topicNeutrophil, Myeloperoxidase and Oxidative MechanismsFrench-language works237,207