The Slow Pandemic: Emergence of Antimicrobial Resistance in the Postadvent of SARS‐CoV‐2 Pandemic
Bibliographic record
Abstract
Background: The unprecedented outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic has dramatically changed the global approach to public health, emphasizing the importance of measures to control and prevent infections. In response to the COVID‐19 crisis, stringent hygiene practices and surface disinfection have become the norm, with an unprecedented surge in the use of disinfectants and antiseptics (DAs). Main Text: While these measures have been crucial in curbing the spread of the virus, an emerging concern has taken center stage: the potential impact of the prolonged and widespread use of antimicrobial compounds in these products on the development of antibiotic resistance. Antimicrobial resistance (AMR) has long been recognized as one of the most pressing global health threats. Quaternary ammonium compounds (QAC) such as benzalkonium chloride, benzethonium chloride, and cetylpyridinium chloride, which are extensively used in DAs formulations, have gained less attention in the context of AMR. Conclusion: A high abundance of QACs was detected in wastewater, and certain bacteria such as Pseudomonas aeruginosa, Acinetobacter baumannii , and Enterococcus species developed resistance to these compounds over time. We analyzed the available evidence from the scientific literature, examining the presence and concentrations of QACs in different water sources, and their resistance mechanisms. This review aimed to shed light on the multifaceted challenges that arise from the dual battle against the COVID‐19 pandemic and the ongoing global fight against AMR.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".