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Record W4396948267 · doi:10.36359/scivp.2024-25-1.08

SAFETY OF TETRACYCLINES FOR PUBLIC HEALTH AND THE ENVIRONMENT

2024· article· en· W4396948267 on OpenAlexaboutno aff
Yu. M. Kosenko, N. V. Ostapiv, L. E. Zaruma

Bibliographic record

VenueScientific and Technical Bulletin оf State Scientific Research Control Institute of Veterinary Medical Products and Fodder Additives аnd Institute of Animal Biology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthBusinessEnvironmental healthComputer scienceMedicineNursing

Abstract

fetched live from OpenAlex

Antimicrobial veterinary medicinal products of the tetracycline class have been most widely used for many years in the veterinary practice, both in Ukraine and in European countries, the USA, Canada, etc. Teteracyclynes have a wide-spectrum activity against gram-positive and gram-negative microorganisms, rickettsia, mycoplasma, chlamydia and protozoan parasites and are successfully used for the treatment of infectious diseases of the gastrointestinal tract, respiratory organs, skin and urogenital infections. Antimicrobial veterinary medicinal products of this class belong to AMEG category D, which is the safest in terms of impact on public health. Tetracyclines are low cost agents, are not high toxic when used in animals, which justifies their frequent choice for empirical treatment. During the monitoring of sales volumes for 2016-2021, antimicrobial veterinary medicinal products of the tetracycline class occupied the largest share among all antimicrobials entering the market in Ukraine (25.9-39.4)%. Most frequently, these veterinary medicinal products are intended for oral administration, but since their low bioavailability (5-15)%, they are excreted from the body almost unchanged. However, there is a danger of the development of tetracyclines resistance and a hazard to the environment and public health, which is explained by the frequent use of this class substances, the permission of large amounts into the soil with manure and water following drinkers washing after the treatment. Tetracyclines are poorly metabolized, and their long-term use in veterinary practice contributes to the bioaccumulation of significant amounts in soil and water. The main goal of this study were the analysis of veterinary medicinal products containing tetracyclines which were authorized in Ukraine by the category of active substances and route of administration. Another problem of this article was to substantiate the impact of tetracycline class antibiotics on the environment and the acquired antimicrobial resistance in the view of the necessity of their reasonable and safety use for the animals treatment, taking into account the requirements of the current legislation in Ukraine and EU member states. The results of the study proved that antimicrobial veterinary medicinal products containing chlortetracycline, mainly for oral administration, prevail in Ukraine. The bias of this monitoring is the lack information on the volumes of their use in farms and for individual animal species. Although the tetracycline class antibiotics do not belong to persistent bio accumulative toxic substances (PBT), their long-term and widespread use causes concern in view of the possible acquiring antimicrobial resistance and harmful effects on the environment, which will have an impact on public health. It is necessary to introduce regulatory measures to limitation and optimal use of these compounds in veterinary medicine. It is also necessary to deliver information among stakeholders about the possible negative impact on the environment and plan activities to prevent the development of acquired antimicrobial resistance to the tetracycline class antibiotics.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.093
GPT teacher head0.371
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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