Role of spleen-derived CD11b+Gr-1+ cells in sepsis-induced acute kidney injury
Bibliographic record
Abstract
PURPOSE: CD11b+Gr-1+ cells play a key role in inflammation and the purpose of this study was to determine whether splenic CD11b+Gr-1+ cells are mobilized to the kidney and lead to acute kidney injury during sepsis. METHODS: The sepsis model was generated via cecum ligation and puncture (CLP). The mice were randomly distributed into control, sham operated, CLP and CLP+splenectomy (CLPS) groups (n=5-10/group). The percentage of CD11b+Gr-1+ cells in circulating, bone marrow and spleen were determined. Plasma concentrations of interleukin-6, interleukin-1β, creatinine (Cr) and neutrophil gelatinase-associated lipocalin were measured. CD11b+Gr-1+ cells were detected by immunofluorescence and qRT-PCR. Hematoxylin-eosin (HE) and periodic acid-Schiff (PAS) staining and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labelling (TUNEL) were performed. Expression of mammalian target of rapamycin (mTOR), hypoxia-inducible factor-1α (HIF-1α) and cleaved caspase-3 was measured. RESULTS: The percentage of CD11b+Gr-1+ cells in blood was significantly higher in the CLP group and lower in CLPS group. CD11b+Gr-1+ cells in the spleen were significantly lower in the CLP group. In the CLP group, the plasma concentrations of interleukin-6, interleukin-1β, Cr and neutrophil gelatinaseassociated lipocalin were higher. The expression of Gr-1 and CD11b were higher in CLP. The CD11b+Gr-1+ cells were detected in the kidneys of the CLP group. HE, PAS and TUNEL showed inflammatory cell infiltration and cell apoptosis in CLP. Western blot indicated dephosphorylation of mTOR, down-expression of HIF-1α and increased expression of cleaved caspase-3 in sepsis kidney. CONCLUSION: Splenic CD11b+Gr-1+ cells migrated to the kidney in sepsis, which led to acute kidney injury via the inhibition of mTOR/HIF-1α.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".