Microcápsulas de sílice preparadas a partir de sistemas tensioactivos para la liberación de sustancias activas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One reason for the lack of regeneration, and poor clinical outcomes, following central nervous system (CNS) injury is the formation of a glial scar that inhibits new axon growth. In addition to forming the glial scar, astrocytes have been shown to be important for spontaneous SCI recovery in rodents, suggesting some astrocyte populations are pro-regenerative, while others are inhibitory following injury. In this work, the effect of implanting hyaluronic acid (HA) hydrogels containing extracellular matrix (ECM) harvested from mouse embryonic stem cell (mESC)-derived astrocytes on histologic outcomes following SCI in rats was explored. In addition, the ability of HA hydrogels with and without ECM to support the transplantation of mESC-derived V2a interneurons was tested. The incorporation of ECM harvested from protoplasmic (grey matter) astrocytes, but not ECM harvested from fibrous (white matter) astrocytes, into hydrogels was found to reduce the size of the glial scar, increase axon penetration into the lesion, and reduce macrophage/microglia staining two weeks after implantation. HA hydrogels were also found to support transplantation of V2a interneurons and the presence of these cells caused an increase in neuronal processes both within the lesion and in the 500 μm surrounding the lesion. Overall, protoplasmic mESC-derived astrocyte ECM showed potential to treat CNS injury. In addition, ECM:HA hydrogels represent a novel scaffold with beneficial effects on histologic outcomes after SCI both with and without cells.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it