Progress of Nanomedicine-Integrated Treatments for Pulmonary Embolism: A Review, Challenges, and Future Possibilities
Bibliographic record
Abstract
Introduction: Pulmonary embolism (PE) is a form of venous thromboembolism that entails the migration of a thrombus to the pulmonary vasculature, causing a blockage, and may even lead to the death of the patient. Currently, low-risk PE is treated with oral anticoagulants, with higher risk cases requiring thrombolytic or surgical interventions. Due to the time sensitive nature of severe cases, the treatments that deal with PE can improve to reduce mortality the rate. Nanomedicine provides that scope of improvement in various avenues of treatment for PE. Although no known nanomedicines have been approved for human trials associated with PE, many companies have achieved success up till animal models and provide a promising scope of discovery within this field. Methods and Results: This study uses research articles, case reports, news articles, and statistical data to provide accurate and comprehensive information. Open-access sources, including Google Scholar and PubMed, were used to access peer-reviewed studies on nanomedicine applications in PE. The study also explored data focusing on preclinical trials with animal models and innovative therapeutic and diagnostic approaches. A review of current PE treatments and nanomedicine options was conducted, highlighting potential advancements and suggesting future directions for research to improve PE treatment strategies. Discussion: The nanomedicines options being actively explored focus on supplementing current PE treatments available, instead of completely replacing them. The field of nanomedicines for PE is making headway within the areas of targeted drug delivery, controlled release systems (CRS), nanocarriers, magnetic nanoparticles (NPs), biosensors as well as wearables. Conclusion: Our compiled literature presents an exhaustive summary of the strides made within nanomedical spaces to treat PE and such related diseases. Despite the lack of clinical data with human populations, the preclinical studies with animal models have unique ways to combat the challenges present with PE treatments today.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".