Distribution of antibiotic resistance genes in the environment
Bibliographic record
Abstract
The prevalence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in the microbiome is a major public health concern globally. Many habitats in the environment are under threat due to excessive use of antibiotics and evolutionary changes occurring in the resistome. ARB and ARGs from farms, cities and hospitals, wastewater treatment plants (WWTPs) or as water runoffs, may accumulate in water, soil, and air. We present a global picture of the resistome by examining ARG-related papers retrieved from PubMed and published in the last 30 years (1990-2020). Natural Language Processing (NLP) was used to retrieve 496,640 papers, out of which 9374 passed the filtering test and were further analyzed to determine the distribution and diversity of ARG subtypes. The papers revealed seven major antibiotic families together with their respective ARG subtypes in different habitats on six continents. Asia, especially China, had the highest number of ARGs related papers compared to other countries/regions/continents. ARGs belonging to multidrug, glycopeptide, and β-lactam families were the most common in reports from hospitals and sulfonamide and tetracycline families were common in reports from farms, WWTPs, water and soil. We also highlight the 'omics' tools used in resistome research, describe some factors that shape the development of resistome, and suggest future work needed to better understand the resistome. The goal was to show the global nature of ARB and ARGs in order to encourage collaborate research efforts aimed at reducing the negative impacts of antibiotic resistance on the One Health concept.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".