Application of RNAIII inhibiting peptide and Image Information Counting Algorithm in the Rehabilitation Treatment of Staphylococcus Aureus Infection in Old People’s Bone and Joint (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED The study aims to explore application of RNAIII inhibiting peptide (RIP) and image information counting algorithm in the rehabilitation treatment plan of staphylococcus aureus infection in old people’s bone and joint. The relationship between the ability of Staphylococcus aureus to form membrane and the concentration of RNAIII inhibiting peptide (RIP) was observed. Additionally, the influence of RIP on the adhesion of Staphylococcus aureus was counted and analyzed by image information counting algorithm. Moreover, the initial image of Staphylococcus aureus was processed with gray scale, the processed gray scale image was expanded with expansion operation, and the processed image was adjusted to binary image for counting. Furthermore, the real-time quantitative PCR (RT-q PCR) was used to detect the mRNA levels of five genes related to biofilm formation, including ica A, ica R, fnb A, atl A, and sar A. It was found that the adhesion ability of Staphylococcus aureus decreased under the effect of RIP, and the results showed that the expression level of ica A, ica R, fnb A, and atl A gene in the experimental group was significantly lower than that in the control group, and the expression level of ica R gene in the experimental group was higher than that in the control group, with statistical difference. There was no significant difference between the two groups in gene expression of sar A. Through the image information counting algorithm for Staphylococcus aureus technology, it was found that RIP could inhibit the adhesion of Staphylococcus aureus and the formation of early biofilm, but the specific regulatory mechanism needs further study. To sum up, according to the results of this study, RIP can be applied to old people's bone and joint diseases caused by Staphylococcus aureus infection, such as pyogenic arthritis and osteomyelitis. This experiment can provide new ideas for the treatment of old people's bone and joint caused by Staphylococcus aureus infection.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".