Macrophages polarization and phenotype following hip fracture injury in elderly subjects 2290
Bibliographic record
Abstract
Abstract Description Healthy elderly individuals are particularly prone to catastrophic events at any moment of their lives. One stressful event for individuals aged 65 and older is a fall that results in a fracture of the hip (HF). HF causes a state of inflammation that may affect immune responses. To assess the impact of HF on macrophage phenotypes. Distribution, functions (chemotaxis, phagocytosis, superoxide production and cytokine production)and phenotype were evaluated in polarized macrophages by GM-CSF and M-CSF before surgery and 6 weeks and 6 months after the event. In healthy older (HO) subjects (n-12) the monocytes under GM-CSF normally polarized towards MI (CD86, CD80, IFN-gamma and IL-12) toward MII under M-CSF (CD163,Arginase, IL-10, Il-4). The use of autologous sera in HO shifted the macrophages towards M1 phenotype which remained during the 6 months follow up in the hip fracture (HF) group (n = 22). Interestingly, when the monocytes from HF patients were treated by heterologous (HO) autologous sera they polarized towards MII even after 6 months. The phagocytosis decreased, however the chemotaxis and ROS production increasing in macrophages during the 6 months followup in HF patients. These data indicate that HF polarizes macrophages towards mainly Mii phenotype and except their phagocytosis all their other functions are increasing creating a sort of permanent inflammatory setting after 6 months HF followup despite the MII phenotype. Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".