Oncostatin M Receptor Type II Knockout Mitigates Inflammation and Improves Survival from Sepsis in Mice
Bibliographic record
Abstract
Sepsis remains one of the leading causes of death worldwide. Oncostatin M (OSM), an interleukin (IL)-6 family cytokine, can be found at high levels in septic patients. However, little is known about its role in sepsis. This study aimed to determine if the genetic knockout of OSM receptor (OSMR) type II signaling would improve survival in a murine model of sepsis. Aged (>50 weeks) OSMR type II knockout (KO) mice and wild-type (WT) littermates received an intraperitoneal injection of fecal slurry (FS) or vehicle. The KO mice had better survival 48 h after the injection of FS than the WT mice (p = 0.005). Eighteen hours post-FS injection, the KO mice had reduced peritoneal, serum, and tissue cytokine levels (including IL-1β, IL-6, TNFα, KG/GRO, and IL-10) compared to the WT mice (p < 0.001 for all). Flow cytometry revealed decreased recruitment of CD11b+ F4/80+ Ly6chigh+ macrophages in the peritoneum of KO mice compared to WT mice (34 ± 6 vs. 4 ± 3%, PInt = 0.005). Isolated peritoneal macrophages from aged KO mice had better live E. coli killing capacity than those from WT mice (p < 0.001). Peritoneal lavage revealed greater bacterial counts in KO mice than in WT mice (KO: 305 ± 22 vs. 116 ± 6 CFU (×109)/mL; p < 0.001). In summary, deficiency in OSMR type II receptor signaling provided a survival benefit in the progression of sepsis. This coincided with reduced serum levels of pro-inflammatory (IL-1β, TNFα, and KC/GRO) and anti-inflammatory markers (IL-10), increased bacterial killing ability of macrophages, and reduced macrophage infiltration into to site of infection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".