Steroid-Based Supramolecular Systems and their Biomedical Applications: Biomolecular Recognition and Transportation
Bibliographic record
Abstract
In this chapter, the biomedical application of steroid-based compounds at “beyond the molecule”—supramolecular level—is reviewed. The renewable and economic natural steroid compounds could be employed as building blocks in the design and construction of steroid-based supramolecular systems. The specific physicochemical features (size, shape, topology, hydrophobicity, chemical modifiability, etc.) and biological properties (biocompatibility, biodegradability, bioaffinity, etc.) could be integrated into functional supramolecular systems by chemical synthesis, modification and intermolecular interactions (such as hydrogen bonding, π-π stacking, van der Waals forces, inclusion interactions, chiral interactions, electrostatic interactions, and so on). The steroid-based (supra)molecules could be employed for molecular recognition and/or be self-assembled into various functional supramolecular assemblies for biomedical applications. The specific physicochemical and biological properties, good biocompatibility, and biological activity endow the steroid-based supramolecular systems good feasibility to be employed in biomolecular recognition/sensing and biomolecular transportation (gene/drug delivery). The examples in this chapter are exemplificative of the transformation of natural steroid-based compounds into functional steroid-based supramolecular systems through molecular and supramolecular engineering technology, moreover, which may inspire the systematic study of natural product-based supramolecular (nano)materials toward future pharmaceutical and biomedical industry.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".