Bibliographic record
Abstract
Inspired by Dr. PK Ghosh’s intriguing article on 3D printing, I decided to delve deeper into the technology and compose this editorial. Part 1 of the article appeared in the April-June issue of this journal,[1] and Part 2[2] is featured in the current issue. Once a concept confined to science fiction, organ printing has rapidly become a ground breaking reality, ushering in a new era in healthcare. Using 3D bioprinting technology to fabricate functional human organs layer by layer, the potential to save lives and revolutionize medical treatment is immense. The history of 3D bioprinting dates back to the early 1980s when American engineer Charles Hull developed the first 3D printer using an acrylic-based photopolymer. This new 3D printing technology, stereolithography, was later applied to creating 3D bio-prints using bioinks containing living cells. A primary objective of 3D bioprinting technology is to create in-vitro tissues or organs that can replace damaged tissues or organs in individuals. 3D bio-printed products include biomedical devices, tissues and organs, bioengineered prosthetics, and implants. This technology may also help in the development of new drugs and biosensors. The implications of organ printing are profound. They address critical challenges such as organ shortages, transplant rejections, and long waiting lists. Traditional organ transplantation, while life-saving, is fraught with limitations, including donor scarcity and the risk of rejection. Organ printing offers a viable solution, providing custom-made organs tailored to each patient’s unique physiology, thereby mitigating rejection risks and eliminating the need for donors. Moreover, organ printing has the potential to revolutionize drug discovery and testing. 3D bioprinting technology can create bioengineered tumor models in vitro, mimicking human tumor tissues. These models aid in anticancer drug screening and precision treatment regimens by replicating real tumor heterogeneity. 3D tumor organoids created by using bio-printed tumor cells collected from tumor patients would be useful for studying gene expression profiles, and such models can be used for therapy development and selecting effective molecules in cancer research. Another potential use of this technology may be in increasing the longevity of the human race by reprogramming aged cells and increasing the telomere length[3] and using such cells for bioprinting, followed by using the bioprinted structures for organ replacement. However, despite its transformative potential, organ printing still faces significant challenges. Replicating human organs’ intricate structures and functions remains a formidable obstacle. Additionally, the technology’s scalability and cost-effectiveness must be optimized to ensure widespread accessibility. Furthermore, ethical considerations surrounding organ printing cannot be overlooked. Questions regarding consent, equity in access, and the commodification of human tissues demand careful deliberation. As technology advances, ethical frameworks must evolve in tandem to safeguard the dignity and rights of individuals. There is considerable growth in the 3D printing market, and thousands of startup companies are connected with this technology. However, only a few are successful. The USA leads the pack, but Canada, France, Germany, Spain, the UK, Japan, and China have also made sizable contributions. Some of the startups in India are also developing 3D printing technology. In Bangalore(India), one of the technology companies has developed a bioprinter that can print human tissue.[4] Collaboration among scientists, clinicians, policymakers, and ethicists is essential to fully realizing the potential of organ printing. By fostering interdisciplinary dialogue and investing in research and development, we can overcome existing challenges and pave the way for a future where organ printing becomes a routine part of medical practice. As we stand on the brink of a healthcare revolution driven by the convergence of biotechnology and engineering, organ printing offers hope for needy patients. It promises a healthier, more equitable world. By embracing this transformative technology with caution, compassion, and foresight, we can unlock its vast potential to redefine the boundaries of modern medicine and enhance human well-being. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".