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Record W4405212819 · doi:10.60087/jklst.v4.n1.003

Microneedles-mediated transdermal drug delivery techniques in modern medicine

2024· article· en· W4405212819 on OpenAlexaff
Saloni Verma, Karan Dhingra

Bibliographic record

VenueJournal of Knowledge Learning and Science Technology ISSN 2959-6386 (online) · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTransdermalMedicineDrug deliveryDrugIntensive care medicinePharmacologyNanotechnology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.422
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Knowledge Learning and Science Technology ISSN 2959-6386 (online)Same topicAdvancements in Transdermal Drug DeliveryFrench-language works237,207