Intracellular Targeted Nanocapsules Containing Nanobiotherapeutic Suppress Lung, Liver, Breast and Cervix Cancer Cell Lines by Prodrug Activation or Removal of Intracellular Tyrosine
Bibliographic record
Abstract
Background: Unlike melanoma, cancer cell lines like Hepa 1-6 liver cancer line, A549 lung cancer line, Hela cervical cancer line, and MCF7 breast cancer line do not have over expressed tyrosinase to convert quercetin into its active o-quinone. Furthermore, they do not need extracellular supply of tyrosine for growth, since they can produce this intracellularly. Method: In this study we used (1) nanocapsules containing polyhemoglobin-tyrosinase (PolyHb-Tyr-nano) to activate the prodrug quercetin. (2) We also located these nanocapsules intracellularly to remove the tyrosine produced intracellularly. We studied this in 4 cancer cell lines: Hepa 1-6 liver cancer line, A549 lung cancer Hela cervical cancer line, and MCF7 breast cancer line. Results: (1) Nanocapsules containing polyhemoglobin-tyrosinase (PolyHb-Tyr-nano) activate the prodrug resulting in increased intracellular o-quinone and suppression of the cancer cell lines. (2) Intracellular located nanocapsules containing polyhemoglobin-tyrosinase (PolyHb-Tyr-nano) were able to lower intracellular tyrosine and suppressed the growth of these cancer cell lines. Furthermore, for PolyHb-Tyr-nano, the dosage needed to suppress 50% of the liver cancer cells (LD50), is 0.7808 mg/ml. In the normal liver cells, the LD50 is 84181 mg/ml. Compared to the result of quercetin activation of LD50 (2.73mg/ml in liver cancer, and 74.18mg/ml in normal hepatocytes). Conclusion: Promising result of using nanocapsules containing polyhemoglobin-tyrosinase (PolyHb-Tyr-nano) to (1) activate the prodrug quercetin and (2) locating these nanocapsules intracellularly to remove the tyrosine produced intracellularly. Method (2) appears to be effective with virtually no adverse effects compared to activation of quercetin.
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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".