Bibliographic record
Abstract
Boron-based therapeutics represent a new class of molecules that could possess various biomedical applications with antiviral, anticancer, antibacterial and antifungal activities. Relatively few boron-containing compounds (BCC) have been approved for clinical or commercial applications. Very little is known about the potential applications of most boron containing compounds against a wide range of diseases. This project focuses on the antiviral and anticancer arms of a larger screening project to determine biological activity of novel BCCs. First, using a human coronavirus model, we demonstrated the ability of our compounds to impair viral replication in both alpha- and beta- coronavirus models. Second, cell-based assays in human pancreatic and breast cancer cell lines demonstrated that novel BCCs show selective growth inhibition. The molecular functions of our compounds were probed in various cell lines using western blotting and cell cycle distribution analyses. These have revealed that our most effective BCCs in vitro likely impact different mechanism(s) in cancer cell killing than the TM commonly studied drug, bortezomib (Velcade ). Our results reaffirm the potential of boron chemistry and use of novel BCC in biomedical applications.
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) | 0.003 | 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".