The role of artificial cells in the fight against COVID-19: deliver vaccine, hemoperfusion removes toxic cytokines, nanobiotherapeutics lower free radicals and pCO <sub>2</sub> and replenish blood supply
Bibliographic record
Abstract
This review concentrates on how artificial cells can contribute to helping patients with COVID-19. Artificial cells have led to mRNA vaccines with more improvements to come. Excessive cytokines in severe COVID-19 can damage organs leading to death. Artificial cell-based collodion macroporous activated charcoal adsorbent can effectively remove middle molecular weight range molecules in patients. A novel hemoperfusion device based on collodion membrane macroporous synthetic resin effectively removes cytokines and recovery in COVID-19 patients. This has been approved as an emergency treatment for COVID-19 in China, Europe, and Canada. A recent nanobiotherapeutic containing haemoglobin and up to six times the concentration of red blood cell enzymes: catalase, superoxide dismutase and carbonic anhydrase. In an animal study, this can effectively lower the damaging increase in free radicals and the removal of increased tissue pCO2. This can also help as blood substitute for the severe and critical problem of COVID-19 pandemic donor blood supply crisis.KEY MESSAGESCOVID-19 and its variants have resulted in major pandemics, severe sicknesses, and deaths around the world. COVID-19 and its variants has only started less than 3 years ago, and it is even more recently that we know more about its mechanisms, requirements, prevention, and treatment. This being the case, this is the first review on the present status and future perspectives of the use of the principle of artificial cells for COVID-19 related to vaccines, treatment, and critical donor blood supply shortage.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one teacher head, 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".