Hybrid of Metapenaeus dobsoni lectin and platinum nanoparticles exert antimicrobial and immunostimulatory effects to reduce bacterial bioburden in infected Nile tilapia
Bibliographic record
Abstract
A novel antibacterial immunostimulant using Platinum nanoparticles (PtNPs) and lectin from Metapenaeus dobsoni (Md-Lec) was developed. The Md-Lec and PtNPs (Pt-lec) hybrid formed through non-covalent interaction exhibits antimicrobial activity against fish specific pathogens by affecting membrane integrity and producing excess reactive oxygen species. The therapeutic efficacy of Pt-lec was demonstrated through rescuing Aeromonas hydrophila infected Nile Tilapia. Pt-lec prevents the infection spreading and reduces the bacterial bioburden in less than 12 h, and as a result of this the fish were restored to normalcy. To assess immunostimulation, we studied the expression of three different immune related genes, namely LEC, Myd88 and COX-2 in the gills, liver, spleen and kidney of fish under various experimental conditions. Our results showed that Pt-lec treatment appeared to be better when compared to lectin alone in enhancing the expression of Myd88 and COX-2, but LEC was not as expected. These results suggest that Pt-lec has the ability to protect Nile Tilapia against bacterial infection by restricting bacterial bioburden through their direct effects on the bacterial membrane and indirectly through their effects on host immune-related gene expression. This hybrid could have potential "green" application in fish farming in rescuing infected animals when compared to widely and unregulated antibiotics.
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) | 0.000 | 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".