Antiproliferative Effects of Local Anesthetics on Mesenchymal Stem Cells
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stem cells (MSC) are self-renewing clonal progenitor cells of nonhematopoietic tissues that exhibit a marked tropism to wounds and tumors. The authors' studies aimed at exploring how local anesthetics would affect MSC biology. METHODS: Proliferation, colony formation, in vitro wound healing, and bone differentiation assays of culture-expanded bone-marrow-derived murine MSC were performed in the presence of increasing concentrations of lidocaine, ropivacaine, and bupivacaine. Cytotoxicity was monitored by measuring lactate dehydrogenase activity and phosphatidylserine exposure/propidium iodide staining (early apoptotic cells/necrotic cells). Measurements of mitochondrial function in intact and permeabilized cells, transcriptional changes, and changes in nuclear factor κ-light-chain-enhancer of activated B cells signaling in MSC treated with ropivacaine were used to further characterize the biologic effects of local anesthetics on MSC. RESULTS: All local anesthetics reduced MSC proliferation at 100 μM, consistent with cell cycle delay or arrest at the G0/1-S phase transition. They increased lactate dehydrogenase release and the number of annexin V-positive MSC but not necrotic MSC. Colony formation was decreased, differentiation into osteoblasts impaired, and in vitro wound healing delayed. Mitochondrial respiration and adenosine 5'-triphosphate concentrations were reduced. Microarray analysis revealed significant expression changes in lysosomal genes and genes controlling sterol metabolism, indicating an impaired phospholipid metabolism in the lysosome. Multiple transcriptional programs related to cell differentiation, tumorigenesis, and metastasis were negatively affected by ropivacaine. CONCLUSIONS: The authors' studies demonstrate that local anesthetics significantly affect important aspects of MSC biology. These experiments provide novel rationales for the perioperative use of local anesthetics in patients with cancer but also highlight the potentially detrimental effects of local anesthetics on wound healing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".