Bibliographic record
Abstract
There is an urgent need for novel classes of antibiotics as most species of bacteria have at least one antibiotic resistant gene, resulting in the bacteria being almost impossible to eradicate. In the past 40 years, only two new classes of antibiotics have been developed. It is essential that novel antibiotics be developed to combat the increasing threat from multi-drug resistance bacteria. The purpose of this experiment was to determine if potential novel classes of antibiotics could be identified by analyzing Pseudomonas aeruginosa gene expression in response to 13 antimicrobial substances, these being 9 chemical compounds, 3 plant tinctures, and silver nitrate. The antimicrobial substances and 14 antibiotics were tested against 96 mutated strains of Pseudomonas aeruginosa . Each strain has been mutated to produce light when one specific gene is expressed. This allowed me to determine which genes were affected by each substance. Cluster analyses were run on the results to determine statistically similar patterns of gene expression. The nine antimicrobial chemicals were sorted into two statistically distinct groups from the antibiotics tested. This indicates that they form two potential novel classes of antibiotics, with modes of action distinct from the tested antibiotics. The plant tinctures had methods of action similar to that of Kanamycin, an aminoglycoside, indicating their potential as novel antibiotics. This is a novel experiment as the antimicrobial substances have not previously been analyzed for their genomic effects and the mutant Pseudomonas aeruginosa has not previously been used as an indicator of gene expression. Il y a un besoin alarmant pour de nouvelles classes d’antibiotiques puisque la plupart des espèces de bactéries détiennent au moins un gène de résistance aux antibiotiques, ce qui rend les bactéries presque impossibles à éradiquer. Au cours des quarante dernières années, seulement deux nouvelles classes d’antibiotiques ont été développées. Il est essentiel que de nouveaux antibiotiques soient développés afin de lutter contre la menace croissante de bactéries détenant une résistance pléïotropique (ou ultidrogue). Le but de cette expérience était de déterminer si une nouvelle classe d’antibiotique pouvait être identifiée en analysant l’expression de gène du Pseudomonas aeruginosa en présence de 13 substances antimicrobiennes, soient 9 composés chimiques, 3 teintures de plantes et du nitrate d’argent. Les substances antimicrobiennes et 14 antibiotiques ont été testés contre 96 souches mutées de seudomonas aeruginosa . Chaque souche a été mutée afin de produire de la lumière quand un gène en particulier est exprimé. Ceci m'a permis de déterminer quels gènes sont affectés par chaque substance. Des partitionnements de données ont été effectués sur les résultats pour déterminer des modèles statistiquement similaires d’expression de gènes. Les neuf produits chimiques antimicrobiens ont été classés en deux groupes statistiquement distincts parmi les antibiotiques testés. Cela démontre qu'ils forment deux nouvelles classes d'antibiotiques, avec des modes d’action distincts par rapport aux antibiotiques testés. Les teintures végétales avaient des méthodes d'action similaire à celle de la anamycine, un aminoglycoside, démontrant leur potentiel en tant que nouveaux antibiotiques. Ceci est une nouvelle expérience ingénieuse puisque les substances antimicrobiennes n’avaient pas été analysées auparavant pour leurs effets génomiques et les mutants du Pseudomonas aeruginosa n’avaient pas été auparavant utilisés en tant qu’indicateurs d’expression de gène.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".