Innovative Use of Nanomaterials in Treating Retinopathy of Prematurity
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Retinopathy of prematurity (ROP) is a severe condition primarily affecting premature infants with a gestational age (GA) of 30 weeks or less and a birth weight (BW) of 1500 g or less. The objective of this review is to examine the risk factors, pathogenesis, and current treatments for ROP, such as cryotherapy, laser photocoagulation, and anti-VEGF therapy, while exploring the limitations of these approaches. Additionally, this review evaluates emerging nanotherapeutic strategies to address these challenges, aiming to improve ROP management. METHODS: A comprehensive literature review was conducted to gather data on the pathogenesis, traditional treatment methods, and novel nanotherapeutic approaches for ROP. This included assessing the efficacy and safety profiles of cryotherapy, laser treatment, anti-VEGF therapy, and nanotherapies currently under investigation. RESULTS: Traditional treatments, while effective in reducing disease progression, exhibit limitations, including long-term complications, tissue damage, and systemic side effects. Nanotherapeutic approaches, on the other hand, have shown potential in offering targeted drug delivery with reduced systemic toxicity, improved ocular drug penetration, and sustained release, which could decrease the frequency of treatments and enhance therapeutic outcomes. CONCLUSIONS: Nanotherapies represent a promising advancement in ROP treatment, offering safer and more effective management strategies. These innovations could address the limitations of traditional therapies, reducing complications and improving outcomes for premature infants affected by ROP. Further research is needed to confirm their efficacy and safety in clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".