Lipocalin 2 Plays an Immunomodulatory Role and Has Detrimental Effects after Spinal Cord Injury
Bibliographic record
Abstract
Lipocalin 2 (Lcn2) plays an important role in defense against bacterial infection by interfering with bacterial iron acquisition. Although Lcn2 is expressed in a number of aseptic inflammatory conditions, its role in these conditions remains unclear. We examined the expression and role of Lcn2 after spinal cord injury (SCI) in adult mice by using a contusion injury model. Lcn2 expression at the protein level is rapidly increased 12-fold at 1 d after SCI and decreases gradually thereafter, being three times as high as control levels at 21 d after injury. Lcn2 expression is strongly induced after contusion injury in astrocytes, neurons, and neutrophils. The Lcn2 receptor (Lcn2R), which has been shown to influence cell survival, is also expressed after SCI in the same cell types. Lcn2-deficient (Lcn2⁻/⁻) mice showed significantly better locomotor recovery after spinal cord contusion injury than wild-type (Lcn2⁺/⁺) mice. Histological assessments indicate improved neuronal and tissue survival and greater sparing of myelin in Lcn2⁻/⁻ mice after contusion injury. Flow cytometry showed a decrease in neutrophil influx and a small increase in the monocyte population in Lcn2⁻/⁻ injured spinal cords. This change was accompanied by a reduction in the expression of several pro-inflammatory chemokines and cytokines as well as inducible nitric oxide synthase early after SCI in Lcn2⁻/⁻ mice compared with wild-type animals. Our results, therefore, suggest a role for Lcn2 in regulating inflammation in the injured spinal cord and that lack of Lcn2 reduces secondary damage and improves locomotor recovery after spinal cord contusion injury.
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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) | 0.001 | 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 | 0.001 | 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".