Cinnamaldehyde Characterization as an Antibacterial Agent toward <i>E. coli</i> Metabolic Profile Using 96-Blade Solid-Phase Microextraction Coupled to Liquid Chromatography–Mass Spectrometry
Bibliographic record
Abstract
Sampling and sample preparation plays an important role in untargeted analysis as it influences final composition of the analyzed extract and consequently reflection of the metabolome. In the current work, mechanism of bactericidal action of cinnamaldehyde (CA) against Escherichia coli (E. coli) during bacteria growth applying high-throughput solid-phase microextraction in direct immersion mode coupled to a high-performance liquid chromatography-mass spectrometry system was investigated. Numerous discriminant metabolites due to CA addition to the bacteria culture were mapped in the E. coli metabolic pathways. We propose new metabolic pathways confirming that CA acts as an oxidative stress agent against E. coli. The results of the current research have successfully demonstrated that CA changes the bacterial metabolism through interactions with different biochemical families such as proteins, nucleic acids, lipids, and carbohydrates, which needs further validation by proteomics and transcriptomics studies. The results presented here show the great potential of the novel approach in drug discovery and food safety.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".