3D quantum theranosomes: a new direction for label-free theranostics
Bibliographic record
Abstract
Quantum-scale materials offer great potential in the field of cancer theranostics. At present, quantum materials are severely limited due to 0D & 1D materials lacking biocompatibility, resulting in coated materials with labelled tags for fluorescence excitation. In addition, the application of magnetic quantum materials has not been reported to date for cancer theranostics. In this current research study, we introduce the concept of applying nickel-based magnetic 3D quantum theranosomes for label-free broadband fluorescence enhancement and cancer therapy. To begin with, we present two (primary and secondary) distinct quantum theranosomes for cancer detection and differentiation (HeLa & MDAMB-231) from mammalian fibroblast cells. The primary theranosomes exhibit a metal enhanced fluorescence (MEF) property through localized surface plasmon resonance to act as cancer detectors, whereas the secondary theranosomes act as cancer differentiators through the fluorescence quenching of HeLa cancer cells. Apart from the above, the synthesized magnetic quantum theranosomes introduced therapeutic functionality wherein the theranosomes mimicked a tumor microenvironment by selectively accelerating the proliferation of mammalian fibroblasts cells while at the same time inducing cancer therapy. These quantum theranosomes were synthesized using femtosecond pulse laser ablation and self-assembled to form an interconnected 3D structure. The 3D architecture and the physicochemical properties of the laser synthesized quantum theranosomes closely resembled a tumor microenvironment. Furthermore, we anticipate that our current recorded findings can shed further light upon these unique magnetic quantum theranosomes as potential contenders towards opening an entirely new direction in the field of cancer theranostics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".