Effect of the Faradarmani Consciousness Field on the susceptibility of antibiotic-resistant human pathogenic bacteria Pseudomonas aeruginosa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Faradarmani Consciousness Field is one of the several Taheri Consciousness Fields (TCFs) were introduced by Mohammad Ali Taheri. TCFs do not possess a quantity, so we cannot directly measure them. However, it is possible to evaluate their effects indirectly through experimental evidence in the laboratory. The resistance of bacteria to antibiotics is a global challenge because the number of bacterial strains resistant to antibiotics has expanded yearly and has spread worldwide. It seems that novel approaches are needed to solve this problem. In this study, the effect of Faradarmani CF on the susceptibility of antibiotic-resistant Pseudomonas aeruginosa as a human bacterial pathogen was evaluated. Antibiotic susceptibility in the presence and absence of Faradarmani CF was assessed via the antimicrobial disk diffusion method. Afterward, it was used from real-time RT-PCR for evaluation of the expression level of the MexA, MexB, and OprM genes of P. aeruginosa strain overexpressing the MexAB–OprM efflux pump. According to the results of the disk diffusion test, Faradarmani CF decreased resistance to antibiotics in P. aeruginosa significantly (p<0.05). The RNA expression level of MexB and OprM genes was decreased in the Faradarmani treatment group compared with the control group (p<0.05). The RNA expression level of MexA decreased, but it was not significant (p>0.05). We showed that the drug resistance of P. aeruginosa decreased under the influence of Faradarmani CF, and it can be examined in P. aeruginosa infections in vivo and in clinical studies. In addition, it is recommended that the effects of T-Consciousness Fields on other drug-resistant pathogens be investigated.
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.
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 | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it