Synthesis of magnetite (Fe <sub>3</sub> O <sub>4</sub> )–Avastin nanocomposite as a potential drug for AMD treatment
Bibliographic record
Abstract
Age‐related macular degeneration (AMD) is one of the prevailing causes of blindness in the aged over 50 years. There are several conventional treatments for AMD including laser therapy, surgery, and intravitreal injection of anti‐vascular endothelial growth factor directly into the eye. Intravitreal injection may result in various side effects such as increasing the eye pressure, eye infections, blurring vision; furthermore, it can negatively affect other body organs. In addition, large number of injections would be required to regenerate the lost vision. To overcome some of these obstacles, the work proposes a new drug delivery technique by using Avastin–Fe 3 O 4 nanocomposites synthesised through co‐precipitation method with approximate size of 20 nm. The saturation magnetisation ( M s ) of the synthesised iron oxide nanoparticles (NPs) was about 55.6648 emu/g which is completely suitable for movement of the (Avastin–Fe 3 O 4 ) NPs. Besides, the results of the flow cytometry tests showed that 90.5% of NPs were Avastin loaded. The proposed new method can be replaced with conventional treatments as the long‐term sustained release of Avastin instead of several injections as well as declining the systemic side effects due to the high concentration of Avastin in the posterior segment of the eye.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".