The spleen tyrosine kinase inhibitor entospletinib resolves inflammation to promote repair following acute kidney injury
Bibliographic record
Abstract
Nonresolving inflammation and maladaptive renal repair contribute to the pathogenesis of acute kidney injury (AKI) transition to chronic kidney disease (CKD). Few therapies have been identified that can modulate these injurious pathways following AKI. Spleen tyrosine kinase (SYK) is an immune regulator expressed in the kidney and a potential therapeutic target for AKI. The effect of the selective SYK inhibitor entospletinib was studied in AKI-to-CKD transition. Entospletinib was administered to mice undergoing unilateral renal ischemia-reperfusion injury (IRI), with kidneys analyzed over 14 days. Single-cell RNA sequencing, digital spatial profiling, intravital microscopy, and flow cytometry were employed to study renal phenotypes. Entospletinib administered before and after IRI protected ischemic kidneys and significantly attenuated the transition to CKD. Entospletinib targeted leukocyte-expressed SYK and prevented neutrophil/monocyte recruitment to the kidney. Entospletinib reduced nonresolving tubulointerstitial inflammation after AKI by blocking activation of mannose receptor-1- and C-type lectin domain family 7 member A-expressing proinflammatory macrophages. The resolution of renal inflammation mediated by entospletinib was associated with a reciprocal increase in resident macrophages, reparative gene expression, preserved tubular integrity, and reduced renal fibrosis. The SYK inhibitor entospletinib resolves renal inflammation and promotes repair following AKI.
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 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) | 0.001 | 0.000 |
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.
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".