<i>In vitro</i> antileukemia, antibacterial and antifungal activities of some 3d metal complexes: Chemical synthesis and structure – activity relationships
Bibliographic record
Abstract
The present paper describes the synthesis, characterization and in vitro biological evaluation screening of different classes (ammoniacates, dioximates, carboxylates, semi- and thiosemicarbazidates) of Co(II), Co(III), Cu(II), Ni(II), Mn(II), Zn(II) and Fe(III) complexes. Schiff bases were obtained from the reaction of some salicyl aldehydes with, respectively, furoylhydrazine, benzoylhydrazine, semicarbazide, thiosemicarbazide and S-methylthiosemicarbazide to give tridentate ligands containing ONO, ONS or ONN as donor atoms. The synthetic metal complexes are of various geometrical and electronic structures, thermodynamic and thermal stabilities, and magnetic and conductance properties. All complexes, except those of Cu, are octahedral. Some Cu, Co and Mn compounds have a dimeric or a polymeric structure. The composition and structure of complexes were analysed by elemental analysis, IR and (1)H NMR and (13)C NMR spectroscopies, and magnetochemical, thermoanalytical and molar conductance measurements. All ligands and metal complexes were tested as inhibitors of human leukemia (HL-60) cells growth, and the most potent, the Cu(II) complexes, have been also tested for their in vitro antibacterial and antifungal activities. Structure-activity relationships were carried out.
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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".