Porphyrin Molecules Decorated on Metal-Organic Frameworks for Multi-Functional Biomedical Applications
Bibliographic record
Abstract
-MIL-53 assisted by high-gravity in a greener media for the first time. Then, porphyrins were deployed to adorn the surface of MOF to increase the sensitivity of the prepared nanocomposite to the genetic materials and in-situ cellular protein structures. The hydrogen bond formation between genetic domains and the porphyrin' nitrogen as well as the surface hydroxyl groups is equally probable and could be considered a milestone in chemical physics and physical chemistry for biomedical applications. In this context, the role of incorporating different forms of porphyrins, their relationship with the final surface morphology, and their drug/gene loading efficiency were investigated to provide a predictable pattern in regard to the previous works. The conceptual phenomenon was optimized to increase the interactions between the biomolecules and the substrate by reaching the limit of detection to 10 pM for the Anti-cas9 protein, 20 pM for the single-stranded DNA (ssDNA), below 10 pM for the single guide RNA (sgRNA) and also around 10 nM for recombinant SARS-CoV-2 spike antigen. Also, the MTT assay showed acceptable relative cell viability of more than 85% in most cases, even by increasing the dose of the prepared nanostructures.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".