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Record W3038072119 · doi:10.4314/tjpr.v19i2.21

Synthesis and evaluation of antimicrobial properties of some azole derivatives

2020· article· en· W3038072119 on OpenAlexaff
Mohd Imran, Abdulhakim Bawadekji, Nawaf Alotaibi

Bibliographic record

VenueTropical Journal of Pharmaceutical Research · 2020
Typearticle
Languageen
FieldChemistry
TopicSynthesis and biological activity
Canadian institutionsScience North
FundersNorthern Border University
KeywordsChemistryAspergillus nigerAspergillus flavusPenicillium citrinumThiazoleMoietyAntimicrobialImidazoleOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Purpose: To synthesize new azole derivatives and determine their antimicrobial properties.
 Methods: The reaction of the intermediates (2a-2c) with 3a-3c in acetone/potassium carbonate solution yielded 4a-4i, which were characterized using Fourier-transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H-NMR), carbon-13 nuclear magnetic resonance (13C-NMR)) and mass spectrometry (MS). Compounds 4a-4i were assessed for their antibacterial and antifungal effects using the sequential dilution technique, relative to ofloxacin and ketoconazole.
 Results: The spectral data for 4a-4i were consistent with the assigned structures. The MIC of compound 4h (10 μg/ml) was similar to that of ketoconazole against Aspergillus flavus, Penicillium citrinum, and Aspergillus niger. The MIC value of compound 4b (10 μg/ml) for Penicillium citrinum was comparable to that of ketoconazole while the MIC value of compound 4d against Staphylococcus aureus and Escherichia coli (20 μg/ml) was equivalent to the corresponding MIC value for ofloxacin.
 Conclusion: The synthesized compounds bearing boronic acid moiety are good antimicrobial agents. Accordingly, further investigation into the thiazole-imidazole or thiazole-triazole derivatives bearing boronic acid moiety is suggested.
 Keywords: Synthesis, Imidazole, Thiazole, Triazole, Antimicrobials

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.405
GPT teacher head0.450
Teacher spread0.045 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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