Nanomaterials as tools in plant transformation: A protoplast-centric perspective
Bibliographic record
Abstract
Genetic engineering of plants can boost disease resistance, enhance crop traits, and ultimately improve agricultural productivity. Several approaches to plant bioengineering have been successful in recent decades. Nanomaterials (NMs) can be customized and fabricated with targeting capabilities, making them well-suited for bioengineering applications. These NMs include organic, inorganic, and composite materials with many different structures, including nanofibers, nanoparticles (NPs), and nanomembranes. Protoplasts are often used as target cells because they lack a cell wall and are more likely to endocytose NM. In this review, the efficacy of NMs in delivering genetic material to protoplasts is examined. The challenges associated with protoplast generation and optimization of protocols for transformation are explored and the possible advantages of NMs in this process are identified. The chemical properties of these NMs in relation to their potency is briefly discussed. Ultimately, this technology is evolving and our understanding of NMs and the requirement for migration through the cellular membrane is still missing several key pieces of information. The next decades will likely produce important new insights that will have important impacts in this field.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".