Editorial: Innovative medical technology based on artificial cells, including its different configurations
Bibliographic record
Abstract
Artificial cell is a highly interdisciplinary area involving medicine, chemistry, bioengineering, biotechnology, biochemistry, and other areas.The highly interdisciplinary approach is such that this Research Topic includes the following participating Frontiers journals: Frontiers in Medical Technology, Frontiers in Bioengineering and Biotechnology, Frontiers in Medicine, Frontiers in Oncology, Frontiers in Molecular Biosciences, and Frontiers in Pediatrics.Red blood cells (rbc) are one of the most important cells since without them, our organs, tissues and cells cannot survive.The first artificial cell prepared is artificial rbc (1, 2).(Figure 1).By going outside the box, we and others are able to extend this to prepare artificial cells of unlimited configurations and contents (2-6).(Figure 1) resulting in large areas of applications (2-6) (Figure 1).Many of the ideas of artificial cells are being extensively applied and extended by us and by researchers worldwide, resulting in exciting progress and applications (Figure 1).Artificial cell is too large an area to be covered under this journal Research Topic.Artificial red blood cells or blood substitute alone already require a >1,000-page multiauthor books (7).Many other areas of artificial cells are in development or routine clinical use.For this Research Topic we shall concentrate on artificial red blood cells as the basis for novel and innovative medical technology and showing how outside the box approaches can lead to innovation application. Artificial red blood cell (blood substitutes)Red blood cells have 3 major functions: 1. Oxygen Carrier 2. Antioxidant functions and 3. transport of carbon dioxide.There was no initial public interest to develop artificial rbc
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".