Dimethyl 2,2&prime;-[2,2&prime;-(ethane-1,1-diyl)bis(1<em>H</em>-indole-3,2-diyl)]-diacetate: a small molecule capable of nano-scale assembly, inhibiting venous thrombosis and inducing no bleeding side effect
Bibliographic record
Abstract
Background: Due to the discovery that deep venous thrombosis (DVT) inhibitor is of chemotherapeutic importance, the nano-property of dimethyl 2,2'-[2,2'-(ethane-1,1-diyl)bis(1 H -indole-3,2-diyl)]-diacetate (DEBIC), a recently reported antitumor agent, is worthy of characterization. Materials and methods: One-pot reaction was used to prepare DEBIC. Electrospray Ionization (+/-)-Fourier Transform-Ion Cyclotron Resonance-Mass Spectrometer (ESI(+/-)-FT-ICR-MS), quadrupole Collision Induced Dissociation (qCID) and nuclear overhauser effect spectroscopy spectra were used to present the assembly of DEBIC. Transmission electron microscopy, scanning electron microscopy, atomic force microscopy and Faraday–Tyndall effect were used to visualize the nano-property of DEBIC. Rat models were used to evaluate DVT inhibition and the bleeding reaction of DEBIC. Results: One-pot reaction can provide DEBIC in acceptable yield and high purity. In water, rat plasma and lyophilized powders of DEBIC existed as particles of small nano-size. In vivo DEBIC inhibited DVT in a dose-dependent manner. The minimal effective dose of DEBIC was 1.7 µmol/kg. Even the dose of 36 µmol/kg/day DEBIC did not induce bleeding side effect in DVT rats like in warfarin (0.82 µmol/kg/day). Conclusion: DEBIC is a small molecule capable of nano-scale assembly, inhibiting venous thrombosis and inducing no bleeding side effect. Keywords : nano-property, DVT inhibition, bleeding-reaction, tumor, thrombosis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".