The Effect of Antimicrobial Drugs Tylocolinum, Tetragold and Cidisept-o on Escherichia coli Ultrastructure
Bibliographic record
Abstract
As a result of wide antibiotics, sulfonamides and other antimicrobial agents usage for the therapy of the animals with the bacterial infections caused by various causative agents including Escherichia coli, many microorganisms gained resistance to the chemotherapeutic agents. New combined drugs are being worked out during recent years, the components of which have various influence mechanisms on the bacterial cell that helps to provide resistance forming control. The results of the researches of the new antimicrobial agents, containing antibiotics in their composition, and non-antibiotic agent influence on the ultrastructure of Escherichia coli are represented in this study. 5-hour Escherichia coli 866 culture was processed by the drugs of the minimum bactericidal (Tylocolinum-0.39 µg/ml, Tetragold-6.25 µg/ml, Cidisept-o-25 µg/ml) and 4-time concentrations during 3 hours. Samples and control culture (without drugs) were fixed by the 2.5% glutaricdialdehyde on the s-Collidine Buffer, dehydrated in the ethanol with rising concentration, filled in epoxies. Ultrathin slices were stained by 2% water solution of uranyl acetate and lead citrate for 10 minutes. Then they were examined with the use of the electron microscope JEM-100 CX II by JEOL. The research showed deep ultrastructural changes in Escherichia coli cells under the antimicrobial agent influence determined by synergistic effect of combined Tylocolinum and Tetragold drugs components, possessing various bacteria influencing mechanisms, and aldehyde that is a component of Cidisept-o. The electron microscopy usage allows to get unique information about the impact consequences of the traditional improved drugs and new drugs with antimicrobial activity on the bacterial infectious agents.
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) | 0.001 | 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".