Bibliographic record
Abstract
We are now moving into the antimicrobial resistance (AMR) era where more antibiotic resistant bacteria are now the majority, a problem brought on by both misuse and over use of antibiotics. Unfortunately, the antibiotic development pipeline dwindled away over the past decades as they are not very profitable compounds for companies to develop. Regardless researchers over the past decade have made strides to explore alternative options and out of this we see revisiting historical infection control agents such as toxic metals. From this we now see a field of research exploring the efficacy of metal ions and metal complexes as antimicrobials. Such antimicrobials are delivered in a variety of forms from metal salts, alloys, metal complexes, organometallic compounds, and metal based nanomaterials and gives us the broad term metalloantimicrobials. We now see many effective formulations applied for various applications using metals as antimicrobials that are effective against drug resistant strains. The purpose of the document here is to step aside and begin a conversation on the issues of use of such toxic metal compounds against microbes. This critical opinion mini-review in no way aims to be comprehensive. The goal here is to understand the benefits of metalloantimicrobials, but also to consider strongly the disadvantages of using metals, and what are the potential consequences of misuse and overuse. We need to be conscious of the issues, to see the entire system and affect through a OneHealth vision.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".