A First Semimanual Device for Clinical Intramuscular Repetitive Cell Injections
Bibliographic record
Abstract
Intramuscular cell transplantation in humans requires so far meticulous repetitive cell injections. Performed percutaneously with syringes operated manually, the procedure is very time consuming and requires a lot of concentration to deliver the cells exactly in the required region. This becomes impractical and inaccurate for large volumes of muscle. In order to accelerate this task, to render it more precise, and to perform injections more reproducible in large volumes of muscle, we developed a specific semimanual device for intramuscular repetitive cell injections. Our prototype delivers very small quantities of cell suspension, homogeneously throughout several needles, from a container in the device. It was designed in order to deliver the cells as best as possible only in a given subcutaneous region (in our case, skeletal muscles accessible from the surface), avoiding wasting in skin and hypodermis. The device was tested in monkeys by performing intramuscular allotransplantations of β-galactosidase-labeled myoblasts. During transplantations, it was more ergonomic and considerably faster than manually operated syringes, facilitating the cell graft in whole limb muscles. Biopsies of the myoblast-injected muscles 1 month later showed abundant β-galactosidase-positive myofibers with homogeneous distribution through the biopsy sections. This is the first device specifically designed for the needs of intramuscular cell transplantation in a clinical context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".