Microneedles-mediated transdermal drug delivery techniques in modern medicine
Bibliographic record
Abstract
Transdermal Drug Delivery Systems (TDDS) present a transformative alternative to traditional drug administration methods,addressing key challenges and revolutionizing the pharmaceutical industry. Despite the prevalence of traditional methods dueto their ease of administration and cost-effectiveness, they face limitations such as low bioavailability, gastrointestinal sideeffects, patient non-adherence, and additional risks associated with invasive procedures. TDDS offer a near-painlessadministration route that minimizes fluctuations in systemic drug exposure and enhances treatment adherence, especially inlow and middle-income countries. TDDS work by overcoming skin permeability barriers through modifications to drugproperties and the development of novel formulations and technologies, such as microneedles (MNs), which create micro-channels in the skin for painless drug delivery. MNs have applications in treating various conditions, including HIV,neurological disorders, diabetes, and cancer. Here in this review we discuss different types of MNs, such as dissolvable,core–shell, and stimuli-responsive formulations and explore TDDS efficacy. Recent advancements, particularly in microneedletechnology, promise to revolutionize drug delivery methods, allowing for a more patient-friendly and effective means ofdelivering necessary therapeutic agents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".