Status and Prospects of Liver Cirrhosis Treatment by Using Bone Marrow-Derived Cells and Mesenchymal Cells
Bibliographic record
Abstract
In 2003, we started autologous bone marrow cell infusion (ABMi) therapy for treating liver cirrhosis. ABMi therapy uses 400 mL of autologous bone marrow obtained under general anesthesia and infused mononuclear cells from the peripheral vein. The clinical study expanded and we treated liver cirrhosis induced by HCV and HBV infection and alcohol consumption. We found that the ABMi therapy was effective for cirrhosis patients and now we are treating patients with combined HIV and HCV infection and with metabolic syndrome-induced liver cirrhosis. Currently, to substantiate our findings that liver cirrhosis can be successfully treated by the ABMi therapy, we are conducting randomized multicenter clinical studies designated "Advanced medical technology B" for HCV-related liver cirrhosis in Japan. On the basis of our clinical study, we developed a proof-of-concept showing that infusion of bone marrow cells (BMCs) improved liver fibrosis and sequentially activated proliferation of hepatic progenitor cells and hepatocytes, further promoting restoration of liver functions. To treat patients with severe forms of liver cirrhosis, we continued translational research to develop less invasive therapies by using mesenchymal stem cells derived from bone marrow. We obtained a small quantity of BMCs under local anesthesia and expanded them into mesenchymal stem cells that will then be used for treating cirrhosis. In this review, we present our strategy to apply the results of our laboratory research to clinical studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".