Synergy of Microfluidics and Nanomaterials: A Revolutionary Approach for Cancer Management
Bibliographic record
Abstract
Cancer affects millions of individuals every year and is the second most common cause of death. Various therapeutic strategies are explored for the management of cancer including radiation therapy and chemotherapy with or without surgical procedures. However, the drawbacks like poor cancer cell targeting and higher toxicity for healthy cells need the advancement of the therapeutic strategy. The exploration of nanomedicine achieves targeted distribution, and the adoption of microfluidics technology for the preparation of the nanoparticulate system has enhanced the efficacy and uniformity of the nanocarriers. The overview of the existing designs of the microfluidics device assisted in the preparation of the nanoparticles, and various nanodelivery systems formulated using the microfluidic device including liposomes, lipidic nanocarriers, quantum dots, polymeric nanoparticles, and metallic nanocarriers are discussed in this review. Further, the challenges associated with the fabrication of the microfluidics device and the fabrication of microfluidics device-based nanoparticles are detailed here.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".