Regenerative Therapy for Liver Cirrhosis Based on Intrahepatic Arterial Infusion of Autologous Subcutaneous Adipose Tissue-Derived Regenerative (Stem) Cells: Protocol for a Confirmatory Multicenter Uncontrolled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Liver cirrhosis results from chronic hepatitis, and is characterized by advanced fibrosis due to long-term hepatic inflammation. Cirrhosis ultimately leads to manifestations of jaundice, ascites, and encephalopathy, and increases the risk of hepatocellular carcinoma. Once cirrhosis is established, resulting in hepatic failure, no effective treatment is available. Therefore, novel therapies to inhibit disease progression of cirrhosis are needed. OBJECTIVE: The objective of this investigator-initiated clinical trial is to assess the safety and efficacy of autologous adipose tissue-derived regenerative (stem) cell therapy delivered to the liver via the hepatic artery in patients with liver cirrhosis. METHODS: Through consultation with the Japan Pharmaceuticals and Medical Devices Agency, we designed a clinical trial to assess a therapy for liver cirrhosis based on autologous adipose tissue-derived regenerative (stem) cells, which are extracted using an adipose tissue dissociation device. The primary endpoints of the trial are the serum albumin concentration, prothrombin activity, harmful events, and device malfunction. RESULTS: Enrollment and registration were initiated in November 2017, and the follow-up period ended in November 2019. Data analysis and the clinical study report will be completed by the end of March 2020. CONCLUSIONS: Completion of this clinical trial, including data analysis, will provide data on the safety and efficacy of this novel liver repair therapy based on autologous adipose tissue-derived regenerative (stem) cells using an adipose tissue dissociation device. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000022601; https://tinyurl.com/w9uqw3q. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17904.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".