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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".