Human retinal secretome: A cross-link between mesenchymal and retinal cells
Bibliographic record
Abstract
In recent years, mesenchymal stem cells (MSC) have been considered the most effective source for regenerative medicine, especially due to released soluble paracrine bioactive components and extracellular vesicles. These factors, collectively called the secretome, play crucial roles in immunomodulation and in improving survival and regeneration capabilities of injured tissue. Recently, there has been a growing interest in the secretome released by retinal cytotypes, especially retinal pigment epithelium and Müller glia cells. The latter trophic factors represent the key to preserving morphofunctional integrity of the retina, regulating biological pathways involved in survival, function and responding to injury. Furthermore, these factors can play a pivotal role in onset and progression of retinal diseases after damage of cell secretory function. In this review, we delineated the importance of cross-talk between MSCs and retinal cells, focusing on common/induced secreted factors, during experimental therapy for retinal diseases. The cross-link between the MSC and retinal cell secretomes suggests that the MSC secretome can modulate the retinal cell secretome and vice versa. For example, the MSC secretome can protect retinal cells from degeneration by reducing oxidative stress, autophagy and programmed cell death. Conversely, the retinal cell secretome can influence the MSC secretome by inducing changes in MSC gene expression and phenotype.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".