In vivo Optical Imaging zum Nachweis der Leberrepopulation nach Konditionierung der Empfängerleber und Hepatozytentransplantation
Bibliographic record
Abstract
Near infrared fluorescence (NIRF) optical imaging is a technique particularly powerful when studying in vivo processes at the molecular level in preclinical animal models. The purpose of this study was to assess optical imaging and the feasibility of donor cell expansion tracking in vivo using a fluorescent probe. Livers of dipeptidylpeptidase IV (DPPIV)-deficient rats were preconditioned with irradiation. Four days later, a partial hepatectomy was performed and wild-type (DPPIV+) hepatocytes were transplanted into recipient livers via the spleen. Repopulation by transplanted DPPIV+ hepatocytes was detected in vivo with Cy5.5-conjugated DPPIV antibody using the Optix MX2 System (ART, Montreal, Canada). Results were compared with nontransplanted control animals and transplanted animals receiving nonspecific antibody. Optical imaging detected Cy5.5-specific fluorescence in the liver region of the transplanted animals, increasing in intensity with time, representing extensive host liver repopulation within 16 weeks following transplantation. A general pattern of donor cell multiplication emerged, with an initially accelerating growth curve and later plateau phase. In contrast, no specific fluorescence was detected in the control groups. Comparison with ex vivo immunofluorescence staining of liver sections confirmed the optical imaging results. Optical imaging constitutes a potent method of assessing the longitudinal kinetics of liver repopulation in the rat transplantation model. Our results provide a basis for the future development of clinical protocols for suitable fluorescent dyes and imaging technologies.
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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) | 0.001 | 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 | 0.001 |
| 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".