Extracellular Vesicles of Salivary Mesenchymal Stem Cells Mitigate Acute Irradiation Injury: Use of an ex-vivo organotypic human slice tissue culture as a disease model
Bibliographic record
Abstract
Abstract Ionizing radiation (IR) therapy for cancer patients can damage surrounding healthy tissues, particularly the salivary glands (SGs), leading to oral and systemic health issues reducing the quality of life of the patients. The mechanisms behind IR damage in SGs are not fully understood, and current therapies often fail to meet patient needs adequately. Therefore, identifying targeted pathways and alternative treatments is essential. To address this, we developed an ex vivo model of SG damage using human salivary glands obtained from patients. Healthy submandibular glands were harvested, cultured, and exposed to IR. RNA sequencing revealed elevated markers for DNA damage, inflammation, and ferroptosis, with four specific genes—FDXR, MDM2, H2AX, and p21—showing increases in expression that correlated with the IR dose. Using them, we developed a high-throughput genetic screening method to evaluate stem cell therapies aimed at mitigating IR injury. Conditioned media from mesenchymal stem/stromal cells (MSC-CM) were found to reduce the expression of all four markers, maintain tissue viability, promote cell proliferation, and decrease oxidative stress. Further analysis involved separating MSC-CM into two fractions: Extracellular Vesicles (EV)-rich and EV-depleted. The EV-depleted fractions retained elevated levels of DNA damage response markers, indicating that EVs play a crucial role in mediating tissue repair. In contrast, the EV-rich fractions reduced the markers of DNA damage response and were readily absorbed by the tissue slices. In conclusion, we have developed a genetic screening method to evaluate treatments for acute IR injury, emphasizing the significant role that EVs play in the repair process.
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 | 0.001 |
| 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 | 0.000 | 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".