Evidence map›Paper›PMID 38482161›Full record

ArticleProceedings. IEEE International Conference on Micro Electro Mechanical Systems2024

TOWARD CONTROLLED-RELEASE DRUG DELIVERY MICROCARRIERS ENABLED BY DIRECT LASER WRITING 3D PRINTING.

Sunandita Sarker, Kimia Forghani, Ziteng Wen, Ryan N Halli, Stephen Hoag, Sharon Flank, Ryan D Sochol

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Conference on Micro Electro Mechanical Systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 47% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Sunandita SarkerDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Kimia ForghaniDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Ziteng WenDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Ryan N HalliDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Stephen HoagDepartment of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, USA.
Sharon FlankInfraTrac, Inc., Silver Spring, MD, USA.
Ryan D SocholDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
University of Maryland, College Park · USUniversity of Maryland, Baltimore · US

Funding

Design and Engineering of Biodegradable 3D Nanoprinted Microcarriers for HIV Drug DeliveryR41AI167166 · NIAID · INFRATRAC, INC. · PI FLANK, SHARON, SOCHOL, RYAN DANIEL · 2022 to 2023
$604k
NIAID NIH HHS R41 AI167166
6 · The paper itself

Abstract

Controlled-release, and especially long-acting, drug delivery systems hold promise for improving treatments for numerous medical conditions. Previously, we reported an additive manufacturing or "three-dimensional (3D) printing" approach for fabricating liquid-core-shell-cap microcarriers comprising standard photoresists. Here we explore the potential to extend this strategy to achieve microcarriers comprising biodegradable materials as a new pathway to controlled-release drug delivery options. Specifically, we investigate the use of "Two-Photon Direct Laser Writing (DLW)" as a means to 3D print microcarriers composed of: (

Indexed as

3D PrintingAdditive ManufacturingDirect Laser WritingDrug DeliveryTwo-Photon Polymerization

Identifiers

PMID38482161
PMCPMC10936737
OpenAlexW4392026413

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.