Prominent elevation of extracellular matrix molecules in intracerebral hemorrhage
Bibliographic record
Abstract
Abstract Background In many neurological conditions, the deposition of extracellular matrix (ECM) molecules is a prominent obstacle for regenerative processes and an enhancer of neuroinflammation. Whether ECM molecules alter in composition after intracerebral hemorrhage (ICH), and which ECM members may inhibit repair in hemorrhagic stroke, remain unknown. Methods The collagenase-induced ICH mouse model and an autopsied human ICH specimen were investigated for expression of ECM members by immunofluorescence microscopy. Sections from a mouse model of multiple sclerosis were used as disease controls. Tissue culture was employed to examine the roles of ECM members on oligodendrocyte precursor cells (OPCs). Results Amongst the lectican chondroitin sulphate proteoglycan members, neurocan but not aggrecan, versican-V1 and versican-V2 was prominently expressed in perihematomal tissue and lesion core compared to the contralateral area in murine ICH. Fibrinogen, fibronectin and heparan sulphate proteoglycan were also elevated after murine ICH while thrombospondin was not. Confocal microscopy with Imaris 3D rendering co-localized neurocan, fibrinogen, fibronectin and HSPG molecules to Iba1+ microglia/macrophages or GFAP+ astrocytes. Marked differentiation from the multiple sclerosis model was observed, the latter with high versican-V1 and negligible neurocan. In culture, purified neurocan inhibited adhesion and process outgrowth of OPCs, which are early steps in myelination in vivo. The prominent expression of neurocan in murine ICH was corroborated in human ICH sections. Conclusion ICH caused distinct alterations in ECM molecules, and prominently neurocan. In tissue culture, neurocan impeded the properties of oligodendrocyte lineage cells. Alterations to the ECM in ICH may adversely affect reparative outcomes after stroke.
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 | 0.001 | 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) | 0.001 | 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".