<i>Pseudomonas fluorescens</i> ' view of the periodic table
Bibliographic record
Abstract
Growth in a biofilm modulates microbial metal susceptibility, sometimes increasing the ability of microorganisms to withstand toxic metal species by several orders of magnitude. In this study, a high-throughput metal toxicity screen was initiated with the aim of correlating biological toxicity data in planktonic and biofilm cells to the physiochemical properties of metal ions. To this end, Pseudomonas fluorescens ATCC 13525 was grown in the Calgary Biofilm Device (CBD) and biofilms and planktonic cells of this microorganism were exposed to gradient arrays of different metal ions. These arrays included 44 different metals with representative compounds that spanned every group of the periodic table (except for the halogens and noble gases). The minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and minimum biofilm eradication concentration (MBEC) values were obtained after exposing the biofilms to metal ions for 4 h. Using these values, metal ion toxicity was correlated to the following ion-specific physicochemical parameters: standard reduction-oxidation potential, electronegativity, the solubility product of the corresponding metal-sulfide complex, the Pearson softness index, electron density and the covalent index. When the ions were grouped according to outer shell electron structure, we found that heavy metal ions gave the strongest correlations to these parameters and were more toxic on average than the other classes of the ions. Correlations were different for biofilms than for planktonic cells, indicating that chemical mechanisms of metal ion toxicity differ between the two modes of growth. We suggest that biofilms can specifically counter the toxic effects of certain physicochemical parameters, which may contribute to the increased ability of biofilms to withstand metal toxicity.
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) | 0.006 | 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".