Effect of the Faradarmani Consciousness Field on the susceptibility of antibiotic-resistant human pathogenic bacteria Pseudomonas aeruginosa
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.
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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 | 0.001 |
| 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 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".