An imbalanced ratio between PC(16:0/16:0) and LPC(16:0) revealed by lipidomics supports the role of the Lands cycle in ischemic brain injury
Bibliographic record
Abstract
Promoting brain recovery after stroke is challenging as a plethora of inhibitory molecules are produced in the brain preventing it from full healing. Moreover, the full scope of inhibitory molecules produced is not well understood. Here, using a high-sensitivity UPLC-MS-based shotgun lipidomics strategy, we semiquantitively measured the differential lipid contents in the mouse cerebral cortex recovering from a transient middle cerebral artery occlusion (MCAO). The lipidomic data were interrogated using the soft independent modeling of class analogy (SIMCA) method involving principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Statistics of the 578 confirmed lipids revealed 84 species were differentially changed during MCAO/reperfusion. The most dynamic changes in lipids occurred between 1 and 7 days post-MCAO, whereas concentrations had subsided to the Sham group level at 14 and 28 days post-MCAO. Quantitative analyses revealed a strong monotonic relationship between the reduction in phosphatidylcholine (PC)(16:0/16:0) and the increase in lysophosphatidylcholine (LPC)(16:0) levels (Spearman's Rs = -0.86) during the 1 to 7 days reperfusion period. Inhibition of cPLA2 prevented changes in the ratio between PC(16:0/16:0) and LPC(16:0), indicating altered Land's cycle of PC. A series of in vitro studies showed that LPC(16:0), but not PC(16:0/16:0), was detrimental to the integrity of neuronal growth cones and neuronal viability through evoking intracellular calcium influx. In contrast, PC(16:0/16:0) significantly suppressed microglial secretion of IL-1β and TNF-α, limiting neuroinflammation pathways. Together, these data support the role of the imbalanced ratio between PC(16:0/16:0) and LPC(16:0), maintained by Lands' cycle, in neuronal damage and microglia-mediated inflammatory response during ischemic recovery.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".