Serum metabonomics reveal the effectiveness of human placental mesenchymal stem cell therapy for primary sclerosing cholangitis
Bibliographic record
Abstract
BACKGROUND: The metabolic patterns of human placental-derived mesenchymal stem cell (hP-MSC) treatment for primary sclerosing cholangitis (PSC) remain unclear, and therapeutic effects significantly vary due to individual differences. Therefore, it is crucial to investigate the serological response to hP-MSC transplantation through small molecular metabolites and identify easily detectable markers for efficacy evaluation. METHODS: C-dansylation and DmPA labeling LC-MS analysis to investigate changes in metabolic pathways after hP-MSC treatment. Key metabolites and regulatory enzymes were validated by qRT-PCR and Western blotting. Potential biomarkers of hP-MSC efficacy were identified through correlation analysis and machine learning. RESULTS: Collectively, the results of the liver histology, serum liver enzyme levels, and inflammatory factors supported the therapeutic efficacy of hP-MSC treatment. Based on significant differences, 41 differentially expressed metabolites were initially identified; these were enriched in bile acid, lipid, and hydroxyproline metabolism. After treatment, bile acid transport was accelerated, whereas bile acid production was reduced; unsaturated fatty acid synthesis was upregulated overall, with increased FADS2 and elongase expression and enhanced fatty acid β-oxidation; hepatic proline 4-hydroxylase expression was decreased, leading to reduced hydroxyproline production. Correlation analysis of liver enzymes and metabolites, combined with time trends, identified eight potential biomarkers: 2-aminomuconate semialdehyde, L-1-pyrroline-3-hydroxy-5-carboxylic acid, L-isoglutamine, and maleamic acid were more abundant in model mice but decreased after hP-MSC treatment. Conversely, 15-methylpalmitic, eicosenoic, nonadecanoic, and octadecanoic acids were less abundant in model mice but increased after hP-MSC treatment. CONCLUSIONS: This study revealed metabolic regulatory changes in PSC model mice after hP-MSC treatment and identified eight promising biomarkers, providing preclinical evidence to support therapeutic applications of hP-MSC.
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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 | 0.001 | 0.001 |
| 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".