Single-Cell Nanoencapsulation: Chemical Synthesis of Artificial Cell-in-Shell Spores
Bibliographic record
Abstract
Nature has evolved adaptive strategies to protect living cells and enhance their resilience against hostile environments, exemplified by bacterial and fungal spores. Inspired by cryptobiosis in nature, chemists have designed and synthesized artificial "cell-in-shell" structures, endowed with the protective and functional capabilities of nanoshells. The cell-in-shells hold the potential to overcome the inherent limitations of biologically naı̈ve cells, enabling the acquisition of exogenous phenotypic traits through the chemical process known as single-cell nanoencapsulation (SCNE). This review highlights recent advancements in the development of artificial spores, with sections organized based on the categorization of material types utilized in SCNE, specifically organic, hybrid, and inorganic types. Particular emphasis is placed on the cytoprotective and multifunctional roles of nanoshells, demonstrating potential applications of SCNEd cells across diverse fields, including synthetic biology, biochemistry, materials science, and biomedical engineering. Furthermore, the perspectives outlined in this review propose future research directions in SCNE, with the goal of achieving fine-tuned precision in chemical modulation at both intracellular and pericellular levels, paving the way for the design and construction of customized artificial spores tailored to meet specific functional needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".