Screening of high-efficiency and low-toxicity antitumor active components in <i>Macleaya cordata</i> seeds based on the competitive effect of drugs on double targets by a new laminar flow chip
Bibliographic record
Abstract
It is urgent to obtain targeted drugs that selectively bind to pathological targets rather than physiological targets in the early stage of drug screening. G-Quadruplex has become one of the important targets in the development of anti-tumor drugs. However, drugs that target quadruplexes may also bind to dsDNA, which may lead to adverse reactions. In this study, a new three-phase laminar flow chip was constructed to enable the multi-components of a traditional Chinese medicine extract to dynamically and competitively bind with G-quadruplex DNA (on target) and double-stranded DNA (off target), so as to select high-efficiency and low-toxicity anti-tumor drugs. The results showed that there were five compounds in the extracts of Macleaya cordata seeds that exhibited obvious differences in binding to the two targets. Furthermore, the binding constants and modes of four identified alkaloids as they bound to two DNA targets were verified by fluorescence spectra and molecular docking methods. The toxicity to HepG2 and LO2 cells from the four alkaloids was also compared. The results showed that sanguinarine and chelerythrine could be used as candidate drugs with stronger binding to HT24 than DNA26. The chip can also be used for other types of double-target screening of other traditional Chinese medicine extracts or compound libraries.
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) | 0.000 | 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".